Atar, Mojtaba and Shah-Hosseini, Reza and Ghaffari, Omid (2024). Retrieval of Soil Moisture Using Time Series of Radar and Optical Remote Sensing Data at 10 m Resolution. Environmental Sciences Proceedings, 29, 1, 75. 10.3390/ECRS2023-16861
Bao, Xin and Zhang, Rui and He, Xu and Shama, Age and Yin, Gaofei and Chen, Jie and Zhang, Hongsheng and Liu, Guoxiang (2024). An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-22. 10.1109/jstars.2024.3413673
Besnier, Jessica and Getirana, Augusto and Beaudoing, Hiroko and Lakshmi, Venkataraman (2024). Characterizing the 2019-2021 drought in La Plata River Basin with GLDAS and SMAP. Journal of Hydrology: Regional Studies, 52, 101679. 10.1016/j.ejrh.2024.101679
Brunelli, Benedetta and Mancini, Francesco (2024). Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain). International Journal of Applied Earth Observation and Geoinformation, 129, 103874. 10.1016/j.jag.2024.103874
Chai, Linna and Zhu, Zhongli and Liu, Shaomin and Xu, Ziwei and Jin, Rui and Li, Xin and Kang, Jian and Che, Tao and Zhang, Yang and Zhang, Jinsong and Cui, Hongjing and Gao, Tiansheng and Xu, Tongren and Zhao, Shaojie and Pan, Xiaoduo and Guo, Ge (2024). QLB-NET: A Dense Soil Moisture and Freeze–Thaw Monitoring Network in the Qinghai Lake Basin on the Qinghai–Tibetan Plateau. Bulletin of the American Meteorological Society, 105, 3, E584-E604. 10.1175/bams-d-23-0186.1
Custódio, Gilliard and Prati, Ronaldo Cristiano (2024). Comparing modern and traditional modeling methods for predicting soil moisture in IoT-based irrigation systems. Smart Agricultural Technology, 7, 100397. 10.1016/j.atech.2024.100397
Del-Coco, Marco and Leo, Marco and Carcagnì, Pierluigi (2024). Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?. Information, 15, 6, 306. 10.3390/info15060306
Ding, Tao and Zhao, Wei and Yang, Yanqing (2024). Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model. International Journal of Applied Earth Observation and Geoinformation, 132, 104003. 10.1016/j.jag.2024.104003
Fang, Bin and Lakshmi, Venkataraman and Zhang, Runze (2024). Validation of downscaled 1‐km SMOS and SMAP soil moisture data in 2010–2021. Vadose Zone Journal, 23, 2, e20305. 10.1002/vzj2.20305
Fu, Z. and Ciais, P. and Wigneron, J. P. and Gentine, P. and Feldman, A. F. and Makowski, D. and Viovy, N. and Kemanian, A. R. and Goll, D. S. and Stoy, P. C. and Prentice, I. C. and Yakir, D. and Liu, L. and Ma, H. and Li, X. and Huang, Y. and Yu, K. and Zhu, P. and Li, X. and Zhu, Z. and Lian, J. and Smith, W. K. (2024). Global critical soil moisture thresholds of plant water stress. Nat Commun, 15, 1, 4826. 10.1038/s41467-024-49244-7
Gibon, François and Mialon, Arnaud and Richaume, Philippe and Rodríguez-Fernández, Nemesio and Aberer, Daniel and Boresch, Alexander and Crapolicchio, Raffaele and Dorigo, Wouter and Gruber, Alexander and Himmelbauer, Irene and Preimesberger, Wolfgang and Sabia, Roberto and Stradiotti, Pietro and Tercjak, Monika and Kerr, Yann H. (2024). Estimating the uncertainties of satellite derived soil moisture at global scale. Science of Remote Sensing, 10, 100147. 10.1016/j.srs.2024.100147
González-Zamora, Ángel and Benito-Verdugo, Pilar and Martínez-Fernández, José (2024). On the Variability in the Temporal Stability Pattern of Soil Moisture Under Mediterranean Conditions. Spanish Journal of Soil Science, 14, 12839. 10.3389/sjss.2024.12839
Hao, Longfei and Chen, Jingjing and Wei, Zushuai and Miao, Linguang and Zhao, Tianjie and Peng, Jian (2024). Validation of Satellite Soil Moisture Products by Sparsification of Ground Observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 5970-5985. 10.1109/jstars.2024.3362833
Hemmati, Emadoddin and Sahebi, Mahmod Reza (2024). Surface soil moisture retrieval based on transfer learning using SAR data on a local scale. International Journal of Remote Sensing, 45, 7, 2374-2406. 10.1080/01431161.2024.2329529
Heyvaert, Zdenko and Scherrer, Samuel and Dorigo, Wouter and Bechtold, Michel and De Lannoy, Gabriëlle (2024). Joint assimilation of satellite-based surface soil moisture and vegetation conditions into the Noah-MP land surface model. Science of Remote Sensing, 9, 100129. 10.1016/j.srs.2024.100129
Hua, Langqin and Zhang, Fangmin and Sun, Rui and Yu, Xiaolan and Ma, He (2024). Synergy of carbon and water use efficiencies in the Huai River Basin. Ecological Indicators, 160, 111874. 10.1016/j.ecolind.2024.111874
Jing, Cheng and Li, Weiqiang and Wan, Wei and Lu, Feng and Niu, Xinliang and Chen, Xiuwan and Rius, Antonio and Cardellach, Estel and Ribó, Serni and Liu, Baojian and Guo, Zhizhou and Nan, Yang (2024). A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface. Geo-spatial Information Science, 27, 3, 638-652. 10.1080/10095020.2024.2330547
Kim, Kyung Y. and Haagenson, Ryan and Kansara, Prakrut and Rajaram, Harihar and Lakshmi, Venkataraman (2024). Augmenting daily MODIS LST with AIRS surface temperature retrievals to estimate ground temperature and permafrost extent in High Mountain Asia. Remote Sensing of Environment, 305, 114075. 10.1016/j.rse.2024.114075
Liu, Yangxiaoyue and Chen, Xiaona and Bai, Yongqing and Zeng, Jiangyuan (2024). Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways. Journal of Hydrology, 636, 131241. 10.1016/j.jhydrol.2024.131241
Li, Yijie and Zhu, Muyuan and Luo, Linyu and Wang, Shuang and Chen, Ce and Zhang, Zhitao and Yao, Yifei and Hu, Xiaotao (2024). GNSS-IR dual-frequency data fusion for soil moisture inversion based on Helmert variance component estimation. Journal of Hydrology, 631, 130752. 10.1016/j.jhydrol.2024.130752
Luo, Xiaotian and Yin, Cong and Sun, Yueqiang and Bai, Weihua and Li, Wei and Song, Hongqing (2024). A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil. Water, 16, 7, 979. 10.3390/w16070979
Mai, Ruiwen and Xin, Qinchuan and Qiu, Jianxiu and Wang, Qianfeng and Zhu, Peng (2024). High spatial resolution soil moisture improves crop yield estimation in the midwestern United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-12. 10.1109/jstars.2024.3417424
Meng, Xiangjin and Peng, Jian and Hu, Jia and Li, Ji and Leng, Guoyong and Ferhatoglu, Caner and Li, Xueying and García-García, Almudena and Yang, Yingbao (2024). Validation and expansion of the soil moisture index for assessing soil moisture dynamics from AMSR2 brightness temperature. Remote Sensing of Environment, 303, 114018. 10.1016/j.rse.2024.114018
Munoz-Martin, Joan Francesc and Rodriguez-Alvarez, Nereida and Bosch-Lluis, Xavier and Oudrhiri, Kamal (2024). Scattering Matrix Retrieval Using Full-Polarimetric GNSS-R. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15. 10.1109/tgrs.2024.3414261
Nandintsetseg, Banzragch and Chang, Jinfeng and Sen, Omer L. and Reyer, Christopher P. O. and Kong, Kaman and Yetemen, Omer and Ciais, Philippe and Davaadalai, Jamts (2024). Future drought risk and adaptation of pastoralism in Eurasian rangelands. npj Climate and Atmospheric Science, 7, 1, 82. 10.1038/s41612-024-00624-2
Navari, Mahdi and Kumar, Sujay and Wang, Shugong and Geiger, James and Mocko, David M. and Arsenault, Kristi R. and Kemp, Eric M. (2024). Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model‐Physics Coupling Framework. Journal of Advances in Modeling Earth Systems, 16, 4, e2022MS003236. 10.1029/2022ms003236
Shan, Xu and Steele-Dunne, Susan and Hahn, Sebastian and Wagner, Wolfgang and Bonan, Bertrand and Albergel, Clement and Calvet, Jean-Christophe and Ku, Ou (2024). Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe. Remote Sensing of Environment, 308, 114167. 10.1016/j.rse.2024.114167
Shi, Changjiang and Zhang, Zhijie and Xiong, Shengqing and Zhang, Wanchang (2024). Enhancing Global Surface Soil Moisture Estimation From ESA CCI and SMAP Product With a Conditional Variational Autoencoder. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 9337-9359. 10.1109/jstars.2024.3393828
Sinha, Jhilam and Sharma, Ashish and Marshall, Lucy and Kim, Seokhyeon (2024). Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach. Water Resources Research, 60, 7, e2022WR034019. 10.1029/2022wr034019
Song, Jiaxi and Zhou, Sha and Yu, Bofu and Li, Yan and Liu, Yanxu and Yao, Ying and Wang, Shuai and Fu, Bojie (2024). Serious underestimation of reduced carbon uptake due to vegetation compound droughts. npj Climate and Atmospheric Science, 7, 1, 23. 10.1038/s41612-024-00571-y
Wang, Chi and Yang, Na and Zhao, Tianjie and Xue, Huazhu and Peng, Zhiqing and Zheng, Jingyao and Pan, Jinmei and Yao, Panpan and Gao, Xiaowen and Yan, Hongbo and Song, Peilin and Liou, Yuei-An and Shi, Jiancheng (2024). All-Season Liquid Soil Moisture Retrieval From SMAP. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 8258-8270. 10.1109/jstars.2024.3382315
Yang, Haoxuan and Wang, Qunming and Liu, Wenqi (2024). A stepwise method for downscaling SMAP soil moisture dataset in the CONUS during 2015–2019. International Journal of Applied Earth Observation and Geoinformation, 130, 103912. 10.1016/j.jag.2024.103912
Yang, Junran and Yang, Qinli and Hu, Feichi and Shao, Junming and Wang, Guoqing (2024). A climate-adaptive transfer learning framework for improving soil moisture estimation in the Qinghai-Tibet Plateau. Journal of Hydrology, 630, 130717. 10.1016/j.jhydrol.2024.130717
Yang, Wentao and Guo, Fei and Zhang, Xiaohong and Zhang, Zhiyu and Zhu, Yifan (2024). High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements. International Journal of Applied Earth Observation and Geoinformation, 128, 103777. 10.1016/j.jag.2024.103777
Zacharias, Steffen and Loescher, Henry W. and Bogena, Heye and Kiese, Ralf and Schrön, Martin and Attinger, Sabine and Blume, Theresa and Borchardt, Dietrich and Borg, Erik and Bumberger, Jan and Chwala, Christian and Dietrich, Peter and Fersch, Benjamin and Frenzel, Mark and Gaillardet, Jérôme and Groh, Jannis and Hajnsek, Irena and Itzerott, Sibylle and Kunkel, Ralf and Kunstmann, Harald and Kunz, Matthias and Liebner, Susanne and Mirtl, Michael and Montzka, Carsten and Musolff, Andreas and Pütz, Thomas and Rebmann, Corinna and Rinke, Karsten and Rode, Michael and Sachs, Torsten and Samaniego, Luis and Schmid, Hans Peter and Vogel, Hans‐Jörg and Weber, Ute and Wollschläger, Ute and Vereecken, Harry (2024). Fifteen Years of Integrated Terrestrial Environmental Observatories (TERENO) in Germany: Functions, Services, and Lessons Learned. Earth's Future, 12, 6, e2024EF004510. 10.1029/2024ef004510
Zhang, Danwen and Lu, Linjun and Li, Xuan and Zhang, Jiahua and Zhang, Sha and Yang, Shanshan (2024). Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism. Remote Sensing, 16, 8, 1394. 10.3390/rs16081394
Zhu, Liujun and Dai, Junjie and Liu, Yi and Yuan, Shanshui and Qin, Tianling and Walker, Jeffrey P. (2024). A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples. Remote Sensing of Environment, 301, 113944. 10.1016/j.rse.2023.113944
Zhu, Yanchao and Yang, Peng and Huang, Heqing and Xia, Jun and Chen, Yaning and Li, Zhi and Shi, Xiaorui (2024). How is about the flash drought events and their impacts on vegetation in Central Asia. Climate Dynamics, 1-21. 10.1007/s00382-024-07266-3
Zhu, Yifan and Guo, Fei and Zhang, Xiaohong and Yang, Wentao (2024). Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations. Geo-spatial Information Science, 1-15. 10.1080/10095020.2024.2374368
Abebrese, David Kwesi and Biney, James Kobina Mensah and Kara, Recep Serdar and Báťková, Kamila and Houška, Jakub and Matula, Svatopluk and Badreldin, Nasem and Truneh, Lemma Adane and Shawula, Tewodros Assefa (2023). Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices. Soil Use and Management, 40, 1. 10.1111/sum.12981
Amantai, Nigenare and Meng, Yuanyuan and Wang, Jingzhe and Ge, Xiangyu and Tang, Zhiyao (2023). Climate overtakes vegetation greening in regulating spatiotemporal patterns of soil moisture in arid Central Asia in recent 35 years. GIScience & Remote Sensing, 61, 1, 2286744. 10.1080/15481603.2023.2286744
Araki, Ryoko, Mu, Ye, McMillan, Hilary (2023). Evaluation of GLDAS soil moisture seasonality in arid climates. Hydrological Sciences Journal, 1-18. 10.1080/02626667.2023.2206032
A, Y. and Jiang, X. and Wang, Y. and Wang, L. and Zhang, Z. and Duan, L. and Fang, Q. (2023). Study on spatio-temporal simulation and prediction of regional deep soil moisture using machine learning. J Contam Hydrol, 258, 104235. 10.1016/j.jconhyd.2023.104235
Batchu, Vishal, Nearing, Grey, Gulshan, Varun (2023). A Deep Learning Data Fusion Model using Sentinel-1/2, SoilGrids, SMAP-USDA, and GLDAS for Soil Moisture Retrieval. Journal of Hydrometeorology. 10.1175/jhm-d-22-0118.1
Berthelin, Romane, Olarinoye, Tunde, Rinderer, Michael, Mudarra, Matías, Demand, Dominic, Scheller, Mirjam, Hartmann, Andreas (2023). Estimating karst groundwater recharge from soil moisture observations – a new method tested at the Swabian Alb, southwest Germany. Hydrology and Earth System Sciences, 27, 385-400. 10.5194/hess-27-385-2023
Brunelli, Benedetta and De Giglio, Michaela and Magnani, Elisa and Dubbini, Marco (2023). Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study. Environment, Development and Sustainability, 1-23. 10.1007/s10668-023-03635-w
Burnol, André, Armandine Les Landes, Antoine, Raucoules, Daniel, Foumelis, Michael, Allanic, Cécile, Paquet, Fabien, Maury, Julie, Aochi, Hideo, Guillon, Théophile, Delatre, Mickael, Dominique, Pascal, Bitri, Adnand, Lopez, Simon, Pébaÿ, Philippe P., Bazargan-Sabet, Behrooz (2023). Impacts of Water and Stress Transfers from Ground Surface on the Shallow Earthquake of 11 November 2019 at Le Teil (France). Remote Sensing, 15, 2270. 10.3390/rs15092270
Chen, H. and Chen, P. and Wang, R. and Qiu, L. and Tang, F. and Xiong, M. (2023). Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S. Sensors (Basel), 23, 19, 8019. 10.3390/s23198019
Corchia, Timothée and Bonan, Bertrand and Rodríguez-Fernández, Nemesio and Colas, Gabriel and Calvet, Jean-Christophe (2023). Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France. Remote Sensing, 15, 17, 4258. 10.3390/rs15174258
Dai, Junjie and Zhu, Liujun and Walker, Jeffrey (2023). Machine Learning Methods for 1 km Soil Moisture Retrieval from Sentinel-1: An Evaluation with Limited Training Samples. 2023 IEEE International Radar Conference (RADAR), 1-5. 10.1109/RADAR54928.2023.10371043
Deng, Xiaodong and Zhu, Luyao and Wang, Hongquan and Zhang, XianYun and Tong, Cheng and Li, Sinan and Wang, Ke (2023). Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1883-1899. 10.1109/jstars.2023.3235111
Ding, Qin and Liang, Yueji and Liang, Xingyong and Ren, Chao and Yan, Hongbo and Liu, Yintao and Zhang, Yan and Lu, Xianjian and Lai, Jianmin and Hu, Xinmiao (2023). Soil Moisture Retrieval Using GNSS-IR Based on Empirical Modal Decomposition and Cross-Correlation Satellite Selection. Remote Sensing, 15, 13, 3218. 10.3390/rs15133218
Donahue, K. and Kimball, J. S. and Du, J. and Bunt, F. and Colliander, A. and Moghaddam, M. and Johnson, J. and Kim, Y. and Rawlins, M. A. (2023). Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations. Front Big Data, 6, 1243559. 10.3389/fdata.2023.1243559
Du, Jinyang, Kimball, John S., Chan, Steven K., Chaubell, Mario Julian, Bindlish, Rajat, Dunbar, R. Scott, Colliander, Andreas (2023). Assessment of Surface Fractional Water Impacts on SMAP Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 4871-4881. 10.1109/jstars.2023.3278686
Graldi, Giulia and Zardi, Dino and Vitti, Alfonso (2023). Retrieving Soil Moisture at the Field Scale from Sentinel-1 Data over a Semi-Arid Mediterranean Agricultural Area. Remote Sensing, 15, 12, 2997. 10.3390/rs15122997
Han, Qianqian and Zeng, Yijian and Zhang, Lijie and Cira, Calimanut-Ionut and Prikaziuk, Egor and Duan, Ting and Wang, Chao and Szabó, Brigitta and Manfreda, Salvatore and Zhuang, Ruodan and Su, Bob (2023). Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale. Geoscientific Model Development, 16, 20, 5825-5845. 10.5194/gmd-16-5825-2023
Han, Q., Zeng, Y., Zhang, L., Wang, C., Prikaziuk, E., Niu, Z., Su, B. (2023). Global long term daily 1 km surface soil moisture dataset with physics informed machine learning. Sci Data, 10, 101. 10.1038/s41597-023-02011-7
Hegazi, Ehab H., Samak, Abdellateif A., Yang, Lingbo, Huang, Ran, Huang, Jingfeng (2023). Prediction of Soil Moisture Content from Sentinel-2 Images Using Convolutional Neural Network (CNN). Agronomy, 13, 656. 10.3390/agronomy13030656
Heyvaert, Zdenko, Scherrer, Samuel, Bechtold, Michel, Gruber, Alexander, Dorigo, Wouter, Kumar, Sujay, De Lannoy, Gabriëlle (2023). Impact of design factors for ESA CCI satellite soil moisture data assimilation over Europe. Journal of Hydrometeorology. 10.1175/jhm-d-22-0141.1
Huang, Shuzhe, Zhang, Xiang, Wang, Chao, Chen, Nengcheng (2023). Two-step fusion method for generating 1 km seamless multi-layer soil moisture with high accuracy in the Qinghai-Tibet plateau. ISPRS Journal of Photogrammetry and Remote Sensing, 197, 346-363. 10.1016/j.isprsjprs.2023.02.009
Huang, Xinyi and Feng, Shouming and Zhao, Shuaishuai and Fan, Jinlong and Qin, Zhihao and Zhao, Shuhe (2023). Assessment of Different Satellite Image-Derived Drought Indices over the Contiguous United States: A Comparison in Different Climates, Vegetation Cover Types, and Soil Layers. Water, 15, 20, 3634. 10.3390/w15203634
Hulsman, P. and Keune, J. and Koppa, A. and Schellekens, J. and Miralles, D. G. (2023). Incorporating Plant Access to Groundwater in Existing Global, Satellite‐Based Evaporation Estimates. Water Resources Research, 59, 8, e2022WR033731. 10.1029/2022wr033731
Hu, Lu, Zhao, Tianjie, Ju, Weimin, Peng, Zhiqing, Shi, Jiancheng, Rodríguez-Fernández, Nemesio J., Wigneron, Jean-Pierre, Cosh, Michael H., Yang, Kun, Lu, Hui, Yao, Panpan (2023). A twenty-year dataset of soil moisture and vegetation optical depth from AMSR-E/2 measurements using the multi-channel collaborative algorithm. Remote Sensing of Environment, 292, 113595. 10.1016/j.rse.2023.113595
Hu, Yifan and Wang, Guojie and Wei, Xikun and Zhou, Feihong and Kattel, Giri and Amankwah, Solomon Obiri Yeboah and Hagan, Daniel Fiifi Tawia and Duan, Zheng (2023). Reconstructing long-term global satellite-based soil moisture data using deep learning method. Frontiers in Earth Science, 11, 1130853. 10.3389/feart.2023.1130853
Jiang, K. and Pan, Z. and Pan, F. and Teuling, A. J. and Han, G. and An, P. and Chen, X. and Wang, J. and Song, Y. and Cheng, L. and Zhang, Z. and Huang, N. and Ma, S. and Gao, R. and Zhang, Z. and Men, J. and Lv, X. and Dong, Z. (2023). Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background. iScience, 26, 6, 106837. 10.1016/j.isci.2023.106837
Karamvasis, Kleanthis and Karathanassi, Vassilia (2023). Soil moisture estimation from Sentinel-1 interferometric observations over arid regions. Computers & Geosciences, 178, 105410. 10.1016/j.cageo.2023.105410
Lakshmi, Venkataraman, Le, Manh-Hung, Goffin, Benjamin D., Besnier, Jessica, Pham, Hung T., Do, Hong-Xuan, Fang, Bin, Mohammed, Ibrahim, Bolten, John D. (2023). Regional analysis of the 2015–16 Lower Mekong River basin drought using NASA satellite observations. Journal of Hydrology: Regional Studies, 46, 101362. 10.1016/j.ejrh.2023.101362
Li, Ji and Leng, Guoyong and Peng, Jian (2023). The Merit of Estimating High-Resolution Soil Moisture Using Combined Optical, Thermal, and Microwave Data. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. 10.1109/lgrs.2023.3291761
Liu, Jiangtao, Hughes, David, Rahmani, Farshid, Lawson, Kathryn, Shen, Chaopeng (2023). Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats. Geoscientific Model Development, 16, 1553-1567. 10.5194/gmd-16-1553-2023
Liu, Yi and Zhu, Ye and Ren, Liliang and Singh, Vijay P. and Yuan, Shanshui (2023). Flash drought fades away under the effect of accumulated water deficits: the persistence and transition to conventional drought. Environmental Research Letters, 18, 11, 114035. 10.1088/1748-9326/acfccb
Liu, Yonghao and Li, Taohui and Zhang, Wenxiang and Lv, Aifeng (2023). Regionalization of Root Zone Moisture Estimations from Downscaled Surface Moisture and Environmental Data with the Soil Moisture Analytical Relationship Model. Water, 15, 23, 4133. 10.3390/w15234133
Li, Zhenghao and Yuan, Qiangqiang and Zhang, Liangpei (2023). Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15. 10.1109/tgrs.2023.3280591
Luo, Qidi and Liang, Yueji and Guo, Yue and Liang, Xingyong and Ren, Chao and Yue, Weiting and Zhu, Binglin and Jiang, Xueyu (2023). Enhancing Spatial Resolution of GNSS-R Soil Moisture Retrieval through XGBoost Algorithm-Based Downscaling Approach: A Case Study in the Southern United States. Remote Sensing, 15, 18, 4576. 10.3390/rs15184576
Madelon, Remi, Rodríguez-Fernández, Nemesio J., Bazzi, Hassan, Baghdadi, Nicolas, Albergel, Clement, Dorigo, Wouter, Zribi, Mehrez (2023). Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data. Hydrology and Earth System Sciences, 27, 1221-1242. 10.5194/hess-27-1221-2023
Ma, Hongliang, Li, Xiaojun, Zeng, Jiangyuan, Zhang, Xiang, Dong, Jianzhi, Chen, Nengcheng, Fan, Lei, Sadeghi, Morteza, Frappart, Frédéric, Liu, Xiangzhuo, Wang, Mengjia, Wang, Huan, Fu, Zheng, Xing, Zanpin, Ciais, Philippe, Wigneron, Jean-Pierre (2023). An assessment of L-band surface soil moisture products from SMOS and SMAP in the tropical areas. Remote Sensing of Environment, 284, 113344. 10.1016/j.rse.2022.113344
Massart, Samuel and Vreugdenhil, Mariette and Bauer-Marschallinger, Bernhard and Navacchi, Claudio and Raml, Bernhard and Dostálová, Alena and Wagner, Wolfgang (2023). Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe. . 10.1080/22797254.2023.2300985
Mazzariello, A. and Albano, R. and Lacava, T. and Manfreda, S. and Sole, A. (2023). Intercomparison of recent microwave satellite soil moisture products on European ecoregions. Journal of Hydrology, 626, 130311. 10.1016/j.jhydrol.2023.130311
Min, Xiaoxiao, Li, Danlu, Shangguan, YuLin, Tian, Shuo, Shi, Zhou (2023). Characterizing the accuracy of satellite-based products to detect soil moisture at the global scale. Geoderma, 432, 116388. 10.1016/j.geoderma.2023.116388
Mi, Pei, Zheng, Chaolei, Jia, Li, Bai, Yu (2023). Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method. Remote Sensing, 15, 2116. 10.3390/rs15082116
Mohammed, Khaled, Leconte, Robert, Trudel, Mélanie (2023). Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation. Water, 15, 321. 10.3390/w15020321
Mohseni, Farzane and Ahrari, Amirhossein and Haunert, Jan-Henrik and Montzka, Carsten (2023). The synergies of SMAP enhanced and MODIS products in a random forest regression for estimating 1 km soil moisture over Africa using Google Earth Engine. Big Earth Data, 1-25. 10.1080/20964471.2023.2257905
Nabi, M. M. and Senyurek, Volkan and Lei, Fangni and Kurum, Mehmet and Gurbuz, Ali Cafer (2023). Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5629-5644. 10.1109/jstars.2023.3287591
Nadeem, Adeel Ahmad, Zha, Yuanyuan, Shi, Liangsheng, Ali, Shoaib, Wang, Xi, Zafar, Zeeshan, Afzal, Zeeshan, Tariq, Muhammad Atiq Ur Rehman (2023). Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China. Remote Sensing, 15, 812. 10.3390/rs15030812
Naz, Bibi S., Sharples, Wendy, Ma, Yueling, Goergen, Klaus, Kollet, Stefan (2023). Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe. Geoscientific Model Development, 16, 1617-1639. 10.5194/gmd-16-1617-2023
Ning, Jing and Yao, Yunjun and Tang, Qingxin and Li, Yufu and Fisher, Joshua B. and Zhang, Xiaotong and Jia, Kun and Xu, Jia and Shang, Ke and Yang, Junming and Yu, Ruiyang and Liu, Lu and Zhang, Xueyi and Xie, Zijing and Fan, Jiahui (2023). Soil moisture at 30 m from multiple satellite datasets fused by random forest. Journal of Hydrology, 625, 130010. 10.1016/j.jhydrol.2023.130010
Pasik, Adam and Gruber, Alexander and Preimesberger, Wolfgang and De Santis, Domenico and Dorigo, Wouter (2023). Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations. Geoscientific Model Development, 16, 17, 4957-4976. 10.5194/gmd-16-4957-2023
Pavur, Gigi, Lakshmi, Venkataraman (2023). Observing the recent floods and drought in the Lake Victoria Basin using Earth observations and hydrological anomalies. Journal of Hydrology: Regional Studies, 46, 101347. 10.1016/j.ejrh.2023.101347
Quast, Raphael and Wagner, Wolfgang and Bauer-Marschallinger, Bernhard and Vreugdenhil, Mariette (2023). Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model—A case-study over the Po-Valley. Remote Sensing of Environment, 295, 113651. 10.1016/j.rse.2023.113651
Ramsauer, Thomas, Marzahn, Philip (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. International Journal of Remote Sensing, 44, 542-566. 10.1080/01431161.2022.2162351
Rojas-Munoz, Oscar and Calvet, Jean-Christophe and Bonan, Bertrand and Baghdadi, Nicolas and Meurey, Catherine and Napoly, Adrien and Wigneron, Jean-Pierre and Zribi, Mehrez (2023). Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA. Remote Sensing, 15, 17, 4329. 10.3390/rs15174329
Scherrer, Samuel and De Lannoy, Gabriëlle and Heyvaert, Zdenko and Bechtold, Michel and Albergel, Clement and El-Madany, Tarek S. and Dorigo, Wouter (2023). Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe. Hydrology and Earth System Sciences, 27, 22, 4087-4114. 10.5194/hess-27-4087-2023
Shana, S. S., Sreenath, K. R., Sumithra, T. G., Krishnaveny, S. M. S., Joshi, K. K., Nameer, P. O., Gopalakrishnan, A. (2023). A Global-Scale Ecological Niche Modeling of the Emerging Pathogen Serratia marcescens to Aid in its Spatial Ecology. Curr Microbiol, 80, 59. 10.1007/s00284-022-03159-y
Shangguan, Yulin and Min, Xiaoxiao and Shi, Zhou (2023). Gap Filling of the ESA CCI Soil Moisture Data Using a Spatiotemporal Attention-Based Residual Deep Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 5344-5354. 10.1109/jstars.2023.3284841
Shangguan, Yulin and Min, Xiaoxiao and Wang, Nan and Tong, Cheng and Shi, Zhou (2023). A long-term, high-accuracy and seamless 1km soil moisture dataset over the Qinghai-Tibet Plateau during 2001–2020 based on a two-step downscaling method. GIScience & Remote Sensing, 61, 1, 2290337. 10.1080/15481603.2023.2290337
Shangguan, Yulin, Min, Xiaoxiao, Shi, Zhou (2023). Inter-comparison and integration of different soil moisture downscaling methods over the Qinghai-Tibet Plateau. Journal of Hydrology, 617, 129014. 10.1016/j.jhydrol.2022.129014
Shin, Yongchul, Mohanty, Binayak P., Kim, Jonggun, Lee, Taehwa (2023). Multi-model based soil moisture simulation approach under contrasting weather conditions. Journal of Hydrology, 617, 129112. 10.1016/j.jhydrol.2023.129112
Singh, Abhilash, Gaurav, Kumar, Sonkar, Gaurav Kailash, Lee, Cheng-Chi (2023). Strategies to Measure Soil Moisture Using Traditional Methods, Automated Sensors, Remote Sensing, and Machine Learning Techniques: Review, Bibliometric Analysis, Applications, Research Findings, and Future Directions. IEEE Access, 11, 13605-13635. 10.1109/access.2023.3243635
Skulovich, O., Gentine, P. (2023). A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. Sci Data, 10, 154. 10.1038/s41597-023-02053-x
Stephens, Graeme, Polcher, Jan, Zeng, Xubin, van Oevelen, Peter, Poveda, Germán, Bosilovich, Michael, Ahn, Myoung-Hwan, Balsamo, Gianpaolo, Duan, Qingyun, Hegerl, Gabriele, Jakob, Christian, Lamptey, Benjamin, Leung, Ruby, Piles, Maria, Su, Zhongbo, Dirmeyer, Paul, Findell, Kirsten L., Verhoef, Anne, Ek, Michael, L’Ecuyer, Tristan, Roca, Rémy, Nazemi, Ali, Dominguez, Francina, Klocke, Daniel, Bony, Sandrine (2023). The First 30 Years of GEWEX. Bulletin of the American Meteorological Society, 104, E126-E157. 10.1175/bams-d-22-0061.1
Sun, Hao and Xu, Qian and Wang, Yunjia and Zhao, Zhiyu and Zhang, Xiaohan and Liu, Hao and Gao, Jinhua (2023). Agricultural drought dynamics in China during 1982–2020: a depiction with satellite remotely sensed soil moisture. GIScience & Remote Sensing, 60, 1, 2257469. 10.1080/15481603.2023.2257469
Tesfamichael, Solomon G. and Shiferaw, Yegnanew A. and Woldai, Tsehaie (2023). Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data. European Journal of Remote Sensing, 56, 1, 2246638. 10.1080/22797254.2023.2246638
Tian, Jie and Zhang, Baoqing and Wang, Xuejin and He, Chansheng (2023). In situ observations of soil hydraulic properties and soil moisture in a high, cold mountainous area of the northeastern Qinghai-Tibet Plateau. Science China Earth Sciences, 66, 8, 1757-1775. 10.1007/s11430-022-1120-5
Tobin, Kenneth and Sanchez, Aaron and Esparza, Daniela and Garcia, Miguel and Ganta, Deepak and Bennett, Marvin (2023). Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains. Remote Sensing, 15, 21, 5120. 10.3390/rs15215120
Wang, Yakun and Hu, Xiaolong and Wang, Lijun and Li, Jinmin and Lin, Lin and Huang, Kai and Shi, Liangsheng (2023). Data worth analysis within a model-free data assimilation framework for soil moisture flow. Hydrology and Earth System Sciences, 27, 14, 2661-2680. 10.5194/hess-27-2661-2023
Wu, Kai, Ryu, Dongryeol, Wagner, Wolfgang, Hu, Zhongmin (2023). A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques. Remote Sensing of Environment, 285, 113387. 10.1016/j.rse.2022.113387
Xing, Zanpin and Li, Xiaojun and Fan, Lei and Colliander, Andreas and Frappart, Frédéric and de Rosnay, Patricia and Fernandez-Moran, Roberto and Liu, Xiangzhuo and Wang, Huan and Zhao, Lin and Wigneron, Jean-Pierre (2023). Assessment of 9 km SMAP soil moisture: Evidence of narrowing the gap between satellite retrievals and model-based reanalysis. Remote Sensing of Environment, 296, 113721. 10.1016/j.rse.2023.113721
Xi, Xuan, Zhuang, Qianlai, Kim, Seungbum, Gentine, Pierre (2023). Evaluating the Effects of Precipitation and Evapotranspiration on Soil Moisture Variability Within CMIP5 Using SMAP and ERA5 Data. Water Resources Research, 59, e2022WR034225. 10.1029/2022wr034225
Yang, Haoxuan, Wang, Qunming (2023). Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021. Journal of Hydrology, 621, 129579. 10.1016/j.jhydrol.2023.129579
Yang, Na and Xiang, Feng and Zhang, Hengjie (2023). The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products. , 15, 16, 3930. 10.3390/rs15163930
Yang, Wentao, Guo, Fei, Zhang, Xiaohong, Zhu, Yifan (2023). An Improved Method for Water Body Removal in Spaceborne GNSS-R Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-8. 10.1109/tgrs.2023.3264629
Yao, P., Lu, H., Zhao, T., Wu, S., Peng, Z., Cosh, M. H., Jia, L., Yang, K., Zhang, P., Shi, J. (2023). A global daily soil moisture dataset derived from Chinese FengYun Microwave Radiation Imager (MWRI) (2010-2019). Sci Data, 10, 133. 10.1038/s41597-023-02007-3
Yi, Chuanxiang, Li, Xiaojun, Zeng, Jiangyuan, Fan, Lei, Xie, Zhiqing, Gao, Lun, Xing, Zanpin, Ma, Hongliang, Boudah, Antoine, Zhou, Hongwei, Zhou, Wenjun, Sheng, Ye, Dong, Tianxiang, Wigneron, Jean-Pierre (2023). Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions. Journal of Hydrology, 619, 129325. 10.1016/j.jhydrol.2023.129325
Yin, Cong, Huang, Feixiong, Xia, Junming, Bai, Weihua, Sun, Yueqiang, Yang, Guanglin, Zhai, Xiaochun, Xu, Na, Hu, Xiuqing, Zhang, Peng, Wang, Jinsong, Du, Qifei, Wang, Xianyi, Cai, Yuerong (2023). Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification. Remote Sensing, 15, 1097. 10.3390/rs15041097
Yu, Tao, Guli·Jiapaer, Bao, Anming, Zhang, Junfeng, Tu, Haiyang, Chen, Bojian, De Maeyer, Philippe, Van de Voorde, Tim (2023). Evaluating surface soil moisture characteristics and the performance of remote sensing and analytical products in Central Asia. Journal of Hydrology, 617, 128921. 10.1016/j.jhydrol.2022.128921
Zhang, Peng and Yu, Hongbo and Gao, Yibo and Zhang, Qiaofeng (2023). Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics. Sustainability, 15, 11, 9112. 10.3390/su15119112
Zhang, Runze and Kim, Seokhyeon and Kim, Hyunglok and Fang, Bin and Sharma, Ashish and Lakshmi, Venkataraman (2023). Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting. Water Resources Research, 59, 12, e2023WR034457. 10.1029/2023wr034457
Zhang, Runze, Chan, Steven, Bindlish, Rajat, Lakshmi, Venkataraman (2023). A Performance Analysis of Soil Dielectric Models over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture. Remote Sensing, 15, 1658. 10.3390/rs15061658
Zhang, Shuangcheng and Guo, Qinyu and Liu, Qi and Ma, Zhongmin and Liu, Ning and Hu, Shengwei and Bao, Lin and Zhou, Xin and Zhao, Hebin and Wang, Lifu and Wan, Tianhe (2023). Improvement of CYGNSS soil moisture retrieval model considering water and surface temperature. Advances in Space Research, 72, 8, 3048-3064. 10.1016/j.asr.2023.06.026
Zhang, X. and Ren, C. and Liang, Y. and Liang, J. and Yin, A. and Wei, Z. (2023). Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers. Sensors (Basel), 23, 18, 7944. 10.3390/s23187944
Zhang, Yufang, Liang, Shunlin, Ma, Han, He, Tao, Wang, Qian, Li, Bing, Xu, Jianglei, Zhang, Guodong, Liu, Xiaobang, Xiong, Changhao (2023). Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning. Earth System Science Data, 15, 2055-2079. 10.5194/essd-15-2055-2023
Zhang, Zhaoxi and Chen, Yan and Zhang, Guodong and Zhang, Xueli (2023). Simulation of Hydrological Processes in the Jing River Basin Based on the WEP Model. Water, 15, 16, 2989. 10.3390/w15162989
Zhang, Zhenyu and Laux, Patrick and Baade, Jussi and Arnault, Joël and Wei, Jianhui and Wang, Xuejin and Liu, Yukun and Schmullius, Christiane and Kunstmann, Harald (2023). Impact of alternative soil data sources on the uncertainties in simulated land-atmosphere interactions. Agricultural and Forest Meteorology, 339, 109565. 10.1016/j.agrformet.2023.109565
Zheng, C., Jia, L., Zhao, T. (2023). A 21-year dataset (2000-2020) of gap-free global daily surface soil moisture at 1-km grid resolution. Sci Data, 10, 139. 10.1038/s41597-023-01991-w
Zhou, Yang, Zhang, Yan, Wang, Ruliang, Chen, Haishan, Zhao, Qifan, Liu, Binshuo, Shao, Qing, Cao, Lu, Sun, Shanlei (2023). Deep learning for daily spatiotemporally continuity of satellite surface soil Moisture over eastern China in summer. Journal of Hydrology, 619, 129308. 10.1016/j.jhydrol.2023.129308
Zhu, Liujun, Yuan, Shanshui, Liu, Yi, Chen, Cheng, Walker, Jeffrey P. (2023). Time series soil moisture retrieval from SAR data: Multi-temporal constraints and a global validation. Remote Sensing of Environment, 287, 113466. 10.1016/j.rse.2023.113466
Zribi, Mehrez and Nativel, Simon and Ayari, Emna and Gascoin, Simon and Albergel, Clement and Baghdadi, Nicolas and Madelon, Rémi and Rodriguez-Fernandez, Nemesio (2023). An Hybrid Approach for Soil Moisture Estimation with Sentinel Data. IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, 3186-3189. 10.1109/IGARSS52108.2023.10281450
A A Nadeem and Y Y Zha and L S Shi and G L Ran and S Ali and Z Jahangir and M M Afzal and M Awais (2022). Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China. Remote Sensing, 14, 982. ARTN 982
10.3390/rs14040982
A Karami and H R Moradi and A Mousivand and A I J M van Dijk and L Renzullo (2022). Using ensemble learning to take advantage of high-resolution radar backscatter in conjunction with surface features to disaggregate SMAP soil moisture product. International Journal of Remote Sensing, 43, 894-914. 10.1080/01431161.2021.2022239
A. Rahman, V. Maggioni, X. Zhang, P. Houser, T. Sauer and D. M. Mocko (2022). The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model. Remote Sensing,, 14. 10.3390/rs14030437
B. Fang, V. Lakshmi, M. Cosh, P.-W. Liu, R. Bindlish and T. J. Jackson (2022). A global 1-km downscaled SMAP soil moisture product based on thermal inertia theory. Vadose Zone Journal,, 21. https://doi.org/10.1002/vzj2.20182
Claudia Gessner and Erich M Fischer and Urs Beyerle and Reto Knutti (2022). Multi-year drought storylines for Europe and North America from an iteratively perturbed global climate model. Weather and Climate Extremes, 38, 100512. 10.1016/j.wace.2022.100512
D Kumawat and M Olyaei and L Gao and A Ebtehaj (2022). Passive Microwave Retrieval of Soil Moisture Below Snowpack at L-Band Using SMAP Observations. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16. Artn 4415216
10.1109/Tgrs.2022.3216324
E S Krueger and M R Levi and K O Achieng and J D Bolten and J D Carlson and N C Coops and Z A Holden and B I Magi and A J Rigden and T E Ochsner (2022). Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions. International Journal of Wildland Fire. 10.1071/Wf22056
F. Lei, V. Senyurek, M. Kurum, A. C. Gurbuz, D. Boyd, R. Moorhead, W. T. Crow and O. Eroglu (2022). Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations. Remote Sensing of Environment,, 276. 10.1016/j.rse.2022.113041
F. Meng, M. Luo, C. Sa, M. Wang and Y. Bao (2022). Quantitative assessment of the effects of climate, vegetation, soil and groundwater on soil moisture spatiotemporal variability in the Mongolian Plateau. Sci Total Environ,, 809, 152198. 10.1016/j.scitotenv.2021.152198
F Mohseni and S M Mirmazloumi and M Mokhtarzade and S Jamali and S Homayouni (2022). Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products. Remote Sensing, 14, 4624. ARTN 4624
10.3390/rs14184624
G Kaplan and T Rashid and M Gasparovic and A Pietrelli and V Ferrara (2022). Monitoring war-generated environmental security using remote sensing: A review. Land Degradation & Development, 33, 1513-1526. 10.1002/ldr.4249
G Portal and M Vall-llossera and M Piles and T Jagdhuber and A Camps and M Pablos and C Lopez-Martinez and N N Das and D Entekhabi (2022). Impact of Incidence Angle Diversity on SMOS and Sentinel-1 Soil Moisture Retrievals at Coarse and Fine Scales. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-18. Artn 4412218
10.1109/Tgrs.2022.3187467
H F Zhao and J Li and Q Q Yuan and L P Lin and L W Yue and H Z Xu (2022). Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau. Journal of Hydrology, 607, 127570. ARTN 127570
10.1016/j.jhydrol.2022.127570
H. H. Nguyen, S. Cho and M. Choi (2022). Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology,, 312. 10.1016/j.agrformet.2021.108719
Hoang Hai Nguyen and Seongkeun Cho and Minha Choi (2022). Spatial soil moisture estimation in agro-pastoral transitional zone based on synergistic use of SAR and optical-thermal satellite images. Agricultural and Forest Meteorology, 312, 108719. https://doi.org/10.1016/j.agrformet.2021.108719
H T Jiang and S X Chen and X H Li and J A Wu and J Zhang and L F Wu (2022). A Novel Method for Long Time Series Passive Microwave Soil Moisture Downscaling over Central Tibet Plateau. Remote Sensing, 14, 2902. ARTN 2902
10.3390/rs14122902
H T Wang and Q Q Yuan and H F Zhao and H Z Xu (2022). In-situ and triple-collocation based assessments of CYGNSS-R soil moisture compared with satellite and merged estimates quasi-globally. Journal of Hydrology, 615, 128716. ARTN 128716
10.1016/j.jhydrol.2022.128716
I Hajdu and I Yule and M White (2022). The Patitapu Soil Moisture Network (PTSMN) dataset and its deployment in New Zealand's hill country. Agricultural Water Management, 274, 107915. ARTN 107915
10.1016/j.agwat.2022.107915
I P Senanayake and I Y Yeo and G R Hancock and G R Willgoose (2022). A decadal record of soil moisture space-time variability over a south-east Australian catchment. Hydrological Processes, 36, e14770. ARTN e14770
10.1002/hyp.14770
J A Ahmad and B A Forman and S V Kumar (2022). Soil moisture estimation in South Asia via assimilation of SMAP retrievals. Hydrology and Earth System Sciences, 26, 2221-2243. 10.5194/hess-26-2221-2022
J Chung and Y Lee and J Kim and C Jung and S Kim (2022). Soil Moisture Content Estimation Based on Sentinel-1 SAR Imagery Using an Artificial Neural Network and Hydrological Components. Remote Sensing, 14, 465. ARTN 465
10.3390/rs14030465
J. Dong, F. Lei and W. T. Crow (2022). Land transpiration-evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat Commun,, 13, 336. 10.1038/s41467-021-27938-6
J. Lee, S. Park, J. Im, C. Yoo and E. Seo (2022). Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning. Journal of Hydrology,, 609. 10.1016/j.jhydrol.2022.127749
J T Liu and F Rahmani and K Lawson and C P Shen (2022). A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data. Geophysical Research Letters, 49, e2021GL096847. ARTN e2021GL096847
10.1029/2021GL096847
K Morrison and W Wagner (2022). Soil Moisture and Soil Depth Retrieval Using the Coupled Phase-Amplitude Behavior of C-Band Radar Backscatter in the Presence of Sub-Surface Scattering. Canadian Journal of Remote Sensing, 48, 779-792. 10.1080/07038992.2022.2120858
L. Gao, A. Ebtehaj, J. Cohen and J.-P. Wigneron (2022). Variability and Changes of Unfrozen Soils Below Snowpack. Geophysical Research Letters,, 49. https://doi.org/10.1029/2021GL095354
L. Gao, Q. Gao, H. Zhang, X. Li, M. J. Chaubell, A. Ebtehaj, L. Shen and J.-P. Wigneron (2022). A deep neural network based SMAP soil moisture product. Remote Sensing of Environment,, 277. 10.1016/j.rse.2022.113059
L. Zhu, R. Si, X. Shen and J. P. Walker (2022). An advanced change detection method for time-series soil moisture retrieval from Sentinel-1. Remote Sensing of Environment,, 279. 10.1016/j.rse.2022.113137
M F Celik and M S Isik and O Yuzugullu and N Fajraoui and E Erten (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sensing, 14, 5584. ARTN 5584
10.3390/rs14215584
M Karamouz and R S Alipour and M Roohinia and M Fereshtehpour (2022). A Remote Sensing Driven Soil Moisture Estimator: Uncertain Downscaling With Geostatistically Based Use of Ancillary Data. Water Resources Research, 58, e2022WR031946. ARTN e2022WR031946
10.1029/2022WR031946
M Y Xu and N Yao and H X Yang and J Xu and A N Hu and L G G de Goncalves and G Liu (2022). Downscaling SMAP soil moisture using a wide & deep learning method over the Continental United States. Journal of Hydrology, 609, 127784. ARTN 127784
10.1016/j.jhydrol.2022.127784
N MacBean and C Bacour and N Raoult and V Bastrikov and E N Koffi and S Kuppel and F Maignan and C Ottle and M Peaucelle and D Santaren and P Peylin (2022). Quantifying and Reducing Uncertainty in Global Carbon Cycle Predictions: Lessons and Perspectives From 15 Years of Data Assimilation Studies With the ORCHIDEE Terrestrial Biosphere Model. Global Biogeochemical Cycles, 36, e2021GB007177. ARTN e2021GB007177
10.1029/2021GB007177
O D Kozhushko and M V Boiko and M Yu Kovbasa and P M Martyniuk and O M Stepanchenko and N V Uvarov (2022). Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm. Mathematical Modeling and Computing, 9, 203-216. 10.23939/mmc2022.02.203
P. Konkathi and L. Karthikeyan (2022). Error and uncertainty characterization of soil moisture and VOD retrievals obtained from L-band SMAP radiometer. Remote Sensing of Environment,, 280. 10.1016/j.rse.2022.113146
P Leng and Z L Li and Q Y Liao and Y J Geng and Q Y Yan and X Zhang and G F Shang (2022). Enhanced Surface Soil Moisture Retrieval at High Spatial Resolution From the Integration of Satellite Observations and Soil Pedotransfer Functions. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. Artn 4513711
10.1109/Tgrs.2022.3222493
P. Luo, Y. Song, X. Huang, H. Ma, J. Liu, Y. Yao and L. Meng (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing,, 185, 111-128. 10.1016/j.isprsjprs.2022.01.009
P. Song, Y. Zhang, J. Guo, J. Shi, T. Zhao and B. Tong (2022). A 1?km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003�2019. Earth System Science Data,, 14, 2613-2637. 10.5194/essd-14-2613-2022
Q Xie and L Jia and M Menenti and G Hu (2022). Global soil moisture data fusion by Triple Collocation Analysis from 2011 to 2018. Sci Data, 9, 687. 10.1038/s41597-022-01772-x
Q Zhang and Q Q Yuan and T Y Jin and M P Song and F J Sun (2022). SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022. Earth System Science Data, 14, 4473-4488. 10.5194/essd-14-4473-2022
R Araki and F Branger and I Wiekenkamp and H McMillan (2022). A signature-based approach to quantify soil moisture dynamics under contrasting land-uses. Hydrological Processes, 36, e14553. ARTN e14553
10.1002/hyp.14553
R Khandan and J P Wigneron and S Bonafoni and A P Biazar and M Gholamnia (2022). Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review. Remote Sensing, 14, 770. ARTN 770
10.3390/rs14030770
R Meyer and W M Zhang and S J Kragh and M Andreasen and K H Jensen and R Fensholt and S Stisen and M C Looms (2022). Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale. Hydrology and Earth System Sciences, 26, 3337-3357. 10.5194/hess-26-3337-2022
R. Souissi, M. Zribi, C. Corbari, M. Mancini, S. Muddu, S. K. Tomer, D. B. Upadhyaya and A. Al Bitar (2022). Integrating process-related information into an artificial neural network for root-zone soil moisture prediction. Hydrology and Earth System Sciences,, 26, 3263-3297. 10.5194/hess-26-3263-2022
S. A. Wakigari and R. Leconte (2022). Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States. Remote Sensing,, 14. 10.3390/rs14030776
S Fathololoumi and M K Firozjaei and A Biswas (2022). Improving spatial resolution of satellite soil water index (SWI) maps under clear-sky conditions using a machine learning approach. Journal of Hydrology, 615, 128709. ARTN 128709
10.1016/j.jhydrol.2022.128709
S Feng and X Huang and S Zhao and Z Qin and J Fan and S Zhao (2022). Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US. Sensors (Basel), 22, 9977. 10.3390/s22249977
S. Huang, X. Zhang, N. Chen, H. Ma, J. Zeng, P. Fu, W.-H. Nam and D. Niyogi (2022). Generating high-accuracy and cloud-free surface soil moisture at 1 km resolution by point-surface data fusion over the Southwestern U.S. Agricultural and Forest Meteorology,, 321. 10.1016/j.agrformet.2022.108985
S. Nativel, E. Ayari, N. Rodriguez-Fernandez, N. Baghdadi, R. Madelon, C. Albergel and M. Zribi (2022). Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation. Remote Sensing,, 14. 10.3390/rs14102434
S O and R Orth and U Weber and S K Park (2022). High-resolution European daily soil moisture derived with machine learning (2003-2020). Sci Data, 9, 701. 10.1038/s41597-022-01785-6
S P Li and Y Sawada (2022). Soil moisture-vegetation interaction from near-global in-situ soil moisture measurements. Environmental Research Letters, 17, 114028. ARTN 114028
10.1088/1748-9326/ac9c1f
S V Travova and V M Stepanenko and A I Medvedev and M A Tolstykh and V Y Bogomolov (2022). Quality of Soil Simulation by the INM RAS-MSU Soil Scheme as a Part of the SL-AV Weather Prediction Model. Russian Meteorology and Hydrology, 47, 159-173. 10.3103/S1068373922030013
S Y Bunting and M J Ascott and D C Gooddy and J P Bloomfield (2022). Towards improved global estimates and model representations of water storage in the unsaturated zone. Hydrogeology Journal, 30, 1933-1936. 10.1007/s10040-022-02520-6
S Z Huang and X Zhang and N C Chen and H L Ma and P Fu and J Z Dong and X H Gu and W H Nam and L Xu and G Rab and D Niyogi (2022). A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio-Temporal Continuity. Water Resources Research, 58, e2021WR030827. ARTN e2021WR030827
10.1029/2021WR030827
T Yang and W Wan and J D Wang and B J Liu and Z G Sun (2022). A Physics-Based Algorithm to Couple CYGNSS Surface Reflectivity and SMAP Brightness Temperature Estimates for Accurate Soil Moisture Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15. Artn 4409715
10.1109/Tgrs.2022.3156959
V A Walker and A Colliander and J S Kimball (2022). Satellite Retrievals of Probabilistic Freeze-Thaw Conditions From SMAP and AMSR Brightness Temperatures. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11. Artn 4411211
10.1109/Tgrs.2022.3174807
Wan, W. and Liu, B. and Guo, Z. and Lu, F. and Niu, X. and Li, H. and Ji, R. and Cheng, J. and Li, W. and Chen, X. and Yang, J. and Bai, Z. (2022). Initial Evaluation of the First Chinese GNSS-R Mission BuFeng-1 A/B for Soil Moisture Estimation. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. 10.1109/LGRS.2021.3097003
X D Zheng and S Z Huang and J Peng and G Y Leng and Q Huang and W Fang and Y Guo (2022). Flash Droughts Identification Based on an Improved Framework and Their Contrasting Impacts on Vegetation Over the Loess Plateau, China. Water Resources Research, 58, e2021WR031464. ARTN e2021WR031464
10.1029/2021WR031464
X J Meng and Y B Yang and J Y Zeng and J Peng and J Hu (2022). Improvement of AMSR2 Soil Moisture Retrieval Using a Soil-Vegetation Temperature Decomposition Algorithm. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. Artn 2507805
10.1109/Lgrs.2022.3218518
X J Wu (2022). Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field. Remote Sensing, 14, 6020. ARTN 6020
10.3390/rs14236020
X W Fan and X S Zhao and X Pan and Y W Liu and Y B Liu (2022). Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data. Journal of Hydrology, 612, 128151. ARTN 128151
10.1016/j.jhydrol.2022.128151
X. Xi, P. Gentine, Q. Zhuang and S. Kim (2022). Evaluating the Variability of Surface Soil Moisture Simulated Within CMIP5 Using SMAP Data. Journal of Geophysical Research: Atmospheres,, 127. https://doi.org/10.1029/2021JD035363
X X Min and Y L Shangguan and D L Li and Z Shi (2022). Improving the fusion of global soil moisture datasets from SMAP, SMOS, ASCAT, and MERRA2 by considering the non-zero error covariance. International Journal of Applied Earth Observation and Geoinformation, 113, 103016. ARTN 103016
10.1016/j.jag.2022.103016
Y Bai and T J Zhao and L Jia and M H Cosh and J C Shi and Z Q Peng and X J Li and J P Wigneron (2022). A multi-temporal and multi-angular approach for systematically retrieving soil moisture and vegetation optical depth from SMOS data. Remote Sensing of Environment, 280, 113190. ARTN 113190
10.1016/j.rse.2022.113190
Y D Ji and Y Li and N Yao and A Biswas and X G Chen and L C Li and A Pulatov and F G Liu (2022). Multivariate global agricultural drought frequency analysis using kernel density estimation. Ecological Engineering, 177, 106550. ARTN 106550
10.1016/j.ecoleng.2022.106550
Y F Zhu and F Guo and X H Zhang (2022). Effect of surface temperature on soil moisture retrieval using CYGNSS. International Journal of Applied Earth Observation and Geoinformation, 112, 102929. ARTN 102929
10.1016/j.jag.2022.102929
Y H Deng and X Y Li and F Z Shi and L N Chai and S J Zhao and M K Ding and Q W Liao (2022). Nonlinear effects of thermokarst lakes on peripheral vegetation greenness across the Qinghai-Tibet Plateau using stable isotopes and satellite detection. Remote Sensing of Environment, 280, 113215. ARTN 113215
10.1016/j.rse.2022.113215
Y J Liang and J M Lai and C Ren and X J Lu and Y Zhang and Q Ding and X M Hu (2022). GNSS-IR multisatellite combination for soil moisture retrieval based on wavelet analysis considering detection and repair of abnormal phases. Measurement, 203, 111881. ARTN 111881
10.1016/j.measurement.2022.111881
Y. Kwon, S. V. Kumar, M. Navari, D. M. Mocko, E. M. Kemp, J. W. Wegiel, J. V. Geiger and R. Bindlish (2022). Irrigation characterization improved by the direct use of SMAP soil moisture anomalies within a data assimilation system. Environmental Research Letters,, 17. 10.1088/1748-9326/ac7f49
Y L Cai and P R Fan and S Lang and M Y Li and Y Muhammad and A X Liu (2022). Downscaling of SMAP Soil Moisture Data by Using a Deep Belief Network. Remote Sensing, 14, 5681. ARTN 5681
10.3390/rs14225681
Y Xia and J D Watts and M B Machmuller and J Sanderman (2022). Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States. PeerJ, 10, e14275. 10.7717/peerj.14275
Y. Zhang, S. Liang, Z. Zhu, H. Ma and T. He (2022). Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing,, 185, 32-47. 10.1016/j.isprsjprs.2022.01.005
Z H Xue and Y J Zhang and L Zhang and H Li (2022). Ensemble Learning Embedded With Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental US. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17. Artn 4508817
10.1109/Tgrs.2022.3166777
Z J Yuan and N NourEldeen and K B Mao and Z H Qin and T R Xu (2022). Spatiotemporal Change Analysis of Soil Moisture Based on Downscaling Technology in Africa. Water, 14, 74. ARTN 74
10.3390/w14010074
Anna Balenzano and Francesco Mattia and Giuseppe Satalino and Francesco P. Lovergine and Davide Palmisano and Jian Peng and Philip Marzahn and Urs Wegmüller and Oliver Cartus and Katarzyna Dabrowska-Zielinska and Jan P. Musial and Malcolm W.J. Davidson and Valentijn R.N. Pauwels and Michael H. Cosh and Heather McNairn and Joel T. Johnson and Jeffrey P. Walker and Simon H. Yueh and Dara Entekhabi and Yann H. Kerr and Thomas J. Jackson (2021). Sentinel-1 soil moisture at 1 km resolution: a validation study. Remote Sensing of Environment, 263, 112554. 10.1016/j.rse.2021.112554
Bin Fang and Prakrut Kansara and Chelsea Dandridge and Venkat Lakshmi (2021). Drought monitoring using high spatial resolution soil moisture data over Australia in 2015–2019. Journal of Hydrology, 594, 125960. 10.1016/j.jhydrol.2021.125960
Chen, Y. and Feng, X. and Fu, B. (2021). An improved global remote-sensing-based surface soil moisture (RSSSM)
dataset covering 2003--2018. Earth System Science Data, 13, 1, 1--31. 10.5194/essd-13-1-2021
De Roos, Shannon and De Lannoy, Gabrielle and Raes, Dirk (2021). A Regional Version of the Aquacrop Model Evaluated with Satellite Retrievals and Backscatter Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6572-6574. 10.1109/IGARSS47720.2021.9554287
Erlingis, Jessica M. and Rodell, Matthew and Peters-Lidard, Christa D. and Li, Bailing and Kumar, Sujay V. and Famiglietti, James S. and Granger, Stephanie L. and Hurley, John V. and Liu, Pang-Wei and Mocko, David M. (2021). A High-Resolution Land Data Assimilation System Optimized for the Western United States. JAWRA Journal of the American Water Resources Association. 10.1111/1752-1688.12910
Fang, Bin and Lakshmi, Venkat and Cosh, Michael H. and Hain, Christopher (2021). Very High Spatial Resolution Downscaled SMAP Radiometer Soil Moisture in the CONUS Using VIIRS/MODIS Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4946-4965. 10.1109/JSTARS.2021.3076026
Fernandez-Moran, Roberto and Piles, María and Camps-Valls, Gustau and Wigneron, Jean-Pierre and Li, Xiaojun and Wang, Mengjia and Fan, Lei and Al-Yaari, Amen and Gómez-Chova, Luis (2021). Towards a Better Understanding of Effective Temperature Modelling in the SMOS-IC Retrieval Algorithm. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6221-6224. 10.1109/IGARSS47720.2021.9553065
Greifeneder, Felix and Notarnicola, Claudia and Wagner, Wolfgang (2021). A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine. Remote Sensing, 13, 11. 10.3390/rs13112099
Grillakis, Manolis G. and Koutroulis, Aristeidis G. and Alexakis, Dimitrios D. and Polykretis, Christos and Daliakopoulos, Ioannis N. (2021). Regionalizing Root-Zone Soil Moisture Estimates From ESA CCI Soil Water Index Using Machine Learning and Information on Soil, Vegetation, and Climate. Water Resources Research, 57, 5, e2020WR029249. 10.1029/2020WR029249
Guevara, M. and Taufer, M. and Vargas, R. (2021). Gap-free global annual soil moisture: 15\,km grids for 1991--2018. Earth System Science Data, 13, 4, 1711--1735. 10.5194/essd-13-1711-2021
Guglielmo, Magda and Tang, Fiona and Pasut, Chiara and Maggi, Federico (2021). SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014. Scientific Data, 8. 10.1038/s41597-021-01032-4
Gupta, Dileep Kumar and Srivastava, Prashant K. and Singh, Ankita and Petropoulos, George P. and Stathopoulos, Nikolaos and Prasad, Rajendra (2021). SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network. Sustainability, 13, 11. 10.3390/su13116019
Hegazi, Ehab H. and Yang, Lingbo and Huang, Jingfeng (2021). A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images. Remote Sensing, 13, 24. 10.3390/rs13244964
He, Liming and Chen, Jing M. and Mostovoy, Georgy and Gonsamo, Alemu (2021). Soil Moisture Active Passive Improves Global Soil Moisture Simulation in a Land Surface Scheme and Reveals Strong Irrigation Signals Over Farmlands. Geophysical Research Letters, 48, 8, e2021GL092658. 10.1029/2021GL092658
Hongliang Ma and Jiangyuan Zeng and Xiang Zhang and Peng Fu and Donghai Zheng and Jean-Pierre Wigneron and Nengcheng Chen and Dev Niyogi (2021). Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations. Remote Sensing of Environment, 264, 112605. https://doi.org/10.1016/j.rse.2021.112605
J. Martínez-Fernández and A. González-Zamora and L. Almendra-Martín (2021). Soil moisture memory and soil properties: An analysis with the stored precipitation fraction. Journal of Hydrology, 593, 125622. 10.1016/j.jhydrol.2020.125622
Kai Wu and Dongryeol Ryu and Lei Nie and Hong Shu (2021). Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis. Remote Sensing of Environment, 256, 112324. 10.1016/j.rse.2021.112324
Kim, Seokhyeon and Sharma, Ashish and Liu, Yi and Young, Sean (2021). Rethinking Satellite Data Merging: From Averaging to SNR Optimization. IEEE Transactions on Geoscience and Remote Sensing. 10.36227/techrxiv.14214035
Laura Almendra-Martín and José Martínez-Fernández and María Piles and Ángel González-Zamora (2021). Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe. Remote Sensing of Environment, 258, 112377. 10.1016/j.rse.2021.112377
Lei, Fangni and Senyurek, Volkan and Kurum, Mehmet and Gurbuz, Ali and Boyd, Dylan and Moorhead, Robert (2021). Quasi-Global GNSS-R Soil Moisture Retrievals at High Spatio-Temporal Resolution from Cygnss and Smap Data. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6303-6306. 10.1109/IGARSS47720.2021.9554005
Li, Mingxing and Wu, Peili and Sexton, David MH and Ma, Zhuguo (2021). Potential shifts in climate zones under a future global warming scenario using soil moisture classification. Climate Dynamics, 56, 7, 2071--2092. 10.1007/s00382-020-05576-w
Ling, X. and Huang, Y. and Guo, W. and Wang, Y. and Chen, C. and Qiu, B. and Ge, J. and Qin, K. and Xue, Y. and Peng, J. (2021). Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China. Hydrology and Earth System Sciences, 25, 7, 4209--4229. 10.5194/hess-25-4209-2021
L. Karthikeyan and Ashok K. Mishra (2021). Multi-layer high-resolution soil moisture estimation using machine learning over the United States. Remote Sensing of Environment, 266, 112706. https://doi.org/10.1016/j.rse.2021.112706
Mahmoodi, Alireza and Rodríguez-Fernández, Nemesio J. and Richaume, Philippe and Kerr, Yann H. (2021). Global Estimation of Surface Soil Moisture Using Neural Networks Trained by In-Situ Measurements and Passive L-Band Telemetry. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6996-6999. 10.1109/IGARSS47720.2021.9555011
Manali Pal and Rajib Maity (2021). Assimilation of remote sensing based surface soil moisture to develop a spatially varying vertical soil moisture profile database for entire Indian mainland. Journal of Hydrology, 601, 126807. https://doi.org/10.1016/j.jhydrol.2021.126807
Mina Moradizadeh and Prashant K. Srivastava (2021). A new model for an improved AMSR2 satellite soil moisture retrieval over agricultural areas. Computers and Electronics in Agriculture, 186, 106205. 10.1016/j.compag.2021.106205
Min Luo and Chula Sa and Fanhao Meng and Yongchao Duan and Tie Liu and Yuhai Bao (2021). Assessing remotely sensed and reanalysis products in characterizing surface soil moisture in the Mongolian Plateau. International Journal of Digital Earth, 14, 10, 1255-1272. 10.1080/17538947.2020.1820590
Mira, Nuno Cirne and Catalão, João and Nico, Giovanni (2021). Observing Soil Moisture Change Using C-Band Interferometry using Machine Learning Regression. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6343-6346. 10.1109/IGARSS47720.2021.9554692
Mounir Abassi,El M’kaddem Kheddioui (2021). Estimation of soil moisture using SAR and Optical imagery in Area with Semi-arid and rainy seasons. European Journal of Molecular & Clinical Medicine, 8, 3, 2708-2716
Ojha, Nitu and Merlin, Olivier and Suere, Christophe and Escorihuela, Maria José (2021). Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data. Frontiers in Environmental Science, 9, 40. 10.3389/fenvs.2021.555216
Ontel, Irina and Irimescu, Anisoara and Boldeanu, George and Mihailescu, Denis and Angearu, Claudiu-Valeriu and Nertan, Argentina and Craciunescu, Vasile and Negreanu, Stefan (2021). Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania. Sensors, 21, 24. 10.3390/s21248371
Ramsauer, Thomas and Weiß, Thomas and Löw, Alexander and Marzahn, Philip (2021). RADOLAN_API: An Hourly Soil Moisture Data Set Based on Weather Radar, Soil Properties and Reanalysis Temperature Data. Remote Sensing, 13, 9. 10.3390/rs13091712
Raoult, Nina and Ottl{\'e}, Catherine and Peylin, Philippe and Bastrikov, Vladislav and Maugis, Pascal (2021). Evaluating and Optimizing Surface Soil Moisture Drydowns in the ORCHIDEE Land Surface Model at In Situ Locations. Journal of Hydrometeorology, 22, 4, 1025--1043. 10.1175/JHM-D-20-0115.1
Runze Zhang and Seokhyeon Kim and Ashish Sharma and Venkat Lakshmi (2021). Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability. Remote Sensing of Environment, 252, 112126. https://doi.org/10.1016/j.rse.2020.112126
Shi, Yajie and Liang, Yueji and Ren, Chao and Lai, Jianmin and Ding, Qin and Hu, Xinmiao (2021). Investigating the Effects of Meteorological Data Rainfall and Temperature on GNSS-R Soil Moisture Inversion. 2021 IEEE Specialist Meeting on Reflectometry using GNSS and other Signals of Opportunity (GNSS+R), 97-100. 10.1109/GNSSR53802.2021.9617574
Shi, Yajie and Ren, Chao and Yan, Zhiheng and Lai, Jianmin (2021). High Spatial-Temporal Resolution Estimation of Ground-Based Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) Soil Moisture Using the Genetic Algorithm Back Propagation (GA-BP) Neural Network. ISPRS International Journal of Geo-Information, 10, 9. 10.3390/ijgi10090623
Steele-Dunne, Susan C. and Hahn, Sebastian and Wagner, Wolfgang and Vreugdenhil, Mariette (2021). Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products. Remote Sensing, 13, 8. 10.3390/rs13081463
Sungmin, O and Orth, Rene (2021). Global soil moisture data derived through machine learning trained with in-situ measurements. Scientific Data, 8, 1, 1--14. 10.1038/s41597-021-00964-1
Sun, Hao and Cui, Yajing (2021). Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method. Remote Sensing, 13, 1. 10.3390/rs13010133
Tsagkatakis, Grigorios and Moghaddam, Mahta and Tsakalides, Panagiotis (2021). Deep multi-modal satellite and in-situ observation fusion for Soil Moisture retrieval. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6339-6342. 10.1109/IGARSS47720.2021.9553848
van der Schalie, Robin and van der Vliet, Mendy and Rodríguez-Fernández, Nemesio and Dorigo, Wouter A. and Scanlon, Tracy and Preimesberger, Wolfgang and Madelon, Rémi and de Jeu, Richard A. M. (2021). L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records. Remote Sensing, 13, 13. 10.3390/rs13132480
W C Adinugroho and R Imanuddin and H Krisnawati and A Syaugi and P B Santosa and M A Qirom and L B Prasetyo (2021). Exploring the potential of soil moisture maps using Sentinel Imagery as a Proxy for groundwater levels in peat. {IOP} Conference Series: Earth and Environmental Science, 874, 1, 012011. 10.1088/1755-1315/874/1/012011
Yajie Shi and Chao Ren and Zhiheng Yan and Jianmin Lai (2021). Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network. International Journal of Remote Sensing, 42, 23, 9085-9108. 10.1080/01431161.2021.1988186
Yangxiaoyue Liu and Yuke Zhou and Ning Lu and Ronglin Tang and Naijing Liu and Yong Li and Ji Yang and Wenlong Jing and Chenghu Zhou (2021). Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe. Journal of Hydrology, 594, 125949. 10.1016/j.jhydrol.2020.125949
Yang, Zhihui and Zhao, Jun and Liu, Jialiang and Wen, Yuanyuan and Wang, Yanqiang (2021). Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau. Sustainability, 13, 22. 10.3390/su132212635
Yao, Panpan and Lu, Hui and Shi, Jiancheng and Zhao, Tianjie and Yang, Kun and Cosh, Michael H and Gianotti, Daniel J Short and Entekhabi, Dara (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002--2019). Scientific data, 8, 1, 1--16. 10.1038/s41597-021-00925-8
Yawei Wang and Pei Leng and Jian Peng and Philip Marzahn and Ralf Ludwig (2021). Global assessments of two blended microwave soil moisture products CCI and SMOPS with in-situ measurements and reanalysis data. International Journal of Applied Earth Observation and Geoinformation, 94, 102234. https://doi.org/10.1016/j.jag.2020.102234
Zhang, Lijie and Zeng, Yijian and Zhuang, Ruodan and Szabó, Brigitta and Manfreda, Salvatore and Han, Qianqian and Su, Zhongbo (2021). In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sensing, 13, 23. 10.3390/rs13234893
Zhang, Ling and Zhang, Zixuan and Xue, Zhaohui and Li, Hao (2021). Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S. Water, 13, 15. 10.3390/w13152003
Zhang, Q. and Yuan, Q. and Li, J. and Wang, Y. and Sun, F. and Zhang, L. (2021). Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013--2019. Earth System Science Data, 13, 3, 1385--1401. 10.5194/essd-13-1385-2021
A. Gruber and G. De Lannoy and C. Albergel and A. Al-Yaari and L. Brocca and J.-C. Calvet and A. Colliander and M. Cosh and W. Crow and W. Dorigo and C. Draper and M. Hirschi and Y. Kerr and A. Konings and W. Lahoz and K. McColl and C. Montzka and J. Muñoz-Sabater and J. Peng and R. Reichle and P. Richaume and C. Rüdiger and T. Scanlon and R. {van der Schalie} and J.-P. Wigneron and W. Wagner (2020). Validation practices for satellite soil moisture retrievals: What are (the) errors?. Remote Sensing of Environment, 244, 111806. https://doi.org/10.1016/j.rse.2020.111806
Akhilesh S. Nair and Rohit Mangla and Thiruvengadam P and J. Indu (2020). Remote sensing data assimilation. Hydrological Sciences Journal, 1--33. 10.1080/02626667.2020.1761021
Albergel, C. and Zheng, Y. and Bonan, B. and Dutra, E. and Rodriguez-Fernandez, N. and Munier, S. and Draper, C. and de Rosnay, P. and Munoz-Sabater, J. and Balsamo, G. and Fairbairn, D. and Meurey, C. and Calvet, J.-C. (2020). Data assimilation for continuous global assessment of severe
conditions over terrestrial surfaces. Hydrology and Earth System Sciences, 24, 9, 4291--4316. 10.5194/hess-24-4291-2020
Beck, H. E. and Pan, M. and Miralles, D. G. and Reichle, R. H. and Dorigo, W. A. and Hahn, S. and Sheffield, J. and Karthikeyan, L. and Balsamo, G. and Parinussa, R. M. and van Dijk, A. I. J. M. and Du, J. and Kimball, J. S. and Vergopolan, N. and Wood, E. F. (2020). Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrology and Earth System Sciences Discussions, 2020, 1--35. 10.5194/hess-2020-184
Bin Fang and Venkataraman Lakshmi and Rajat Bindlish and Thomas J. Jackson and Pang-Wei Liu (2020). Evaluation and validation of a high spatial resolution satellite soil moisture product over the Continental United States. Journal of Hydrology, 588, 125043. https://doi.org/10.1016/j.jhydrol.2020.125043
Chen, Y. and Feng, X. and Fu, B. (2020). A new dataset of satellite observation-based global surface soil moisture covering 2003--2018. Earth System Science Data Discussions, 2020, 1--46. 10.5194/essd-2020-59
Clara Chew and Eric Small (2020). Estimating inundation extent using CYGNSS data: A conceptual modeling study. Remote Sensing of Environment, 246, 111869. https://doi.org/10.1016/j.rse.2020.111869
Deng, Yuanhong and Wang, Shijie and Bai, Xiaoyong and Luo, Guangjie and Wu, Luhua and Chen, Fei and Wang, Jinfeng and Li, Chaojun and Yang, Yujie and Hu, Zeyin and others (2020). Vegetation greening intensified soil drying in some semi-arid and arid areas of the world. Agricultural and Forest Meteorology, 292, 108103. https://doi.org/10.1016/j.agrformet.2020.108103
Foucras, Myriam and Zribi, Mehrez and Albergel, Clement and Baghdadi, Nicolas and Calvet, Jean-Christophe and Pellarin, Thierry (2020). Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy. Water, 12, 3, 866. 10.3390/w12030866
Hagan, Daniel Fiifi Tawia and Wang, Guojie and Kim, Seokhyeon and Parinussa, Robert M. and Liu, Yi and Ullah, Waheed and Bhatti, Asher Samuel and Ma, Xiaowen and Jiang, Tong and Su, Buda (2020). Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging. Remote Sensing, 12, 13, 2164. 10.3390/rs12132164
Han, Yizhi and Bai, Xiaojing and Shao, Wei and Wang, Jie (2020). Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields. Water, 12, 6. 10.3390/w12061726
Herbert, Christoph and Pablos, Miriam and Vall-llossera, Merce and Camps, Adriano and Martinez-Fernandez, Jose (2020). Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths. Remote Sensing, 12, 16, 2614. 10.3390/rs12162614
Jianzhi Dong and Wade T. Crow and Kenneth J. Tobin and Michael H. Cosh and David D. Bosch and Patrick J. Starks and Mark Seyfried and Chandra Holifield Collins (2020). Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation. Remote Sensing of Environment, 242, 111756. https://doi.org/10.1016/j.rse.2020.111756
Kovacevic, Jovan and Cvijetinovic, Zeljko and Stancic, Nikola and Brodic, Nenad and Mihajlovic, Dragan (2020). New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture. Remote Sensing, 12, 7, 1119. 10.3390/rs12071119
Lei Xu and Nengcheng Chen and Xiang Zhang and Hamid Moradkhani and Chong Zhang and Chuli Hu (2020). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254, 112248. https://doi.org/10.1016/j.rse.2020.112248
L. Gao and M. Sadeghi and A. F. Feldman and A. Ebtehaj (2020). A Spatially Constrained Multichannel Algorithm for Inversion of a First-Order Microwave Emission Model at L-Band. IEEE Transactions on Geoscience and Remote Sensing, 1--13. 10.1109/TGRS.2020.2987490
Li, Mingxing and Wu, Peili and Ma, Zhuguo (2020). A comprehensive evaluation of soil moisture and soil temperature from third-generation atmospheric and land reanalysis data sets. International Journal of Climatology. 10.1002/joc.6549
Lin, Liao-Fan and Pu, Zhaoxia (2020). Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land--Atmosphere Data Assimilation with GSI-EnKF. Monthly Weather Review, 148, 7, 2863--2888. 10.1175/MWR-D-19-0370.1
Lun Gao and Morteza Sadeghi and Ardeshir Ebtehaj (2020). Microwave retrievals of soil moisture and vegetation optical depth with improved resolution using a combined constrained inversion algorithm: Application for SMAP satellite. Remote Sensing of Environment, 239, 111662. https://doi.org/10.1016/j.rse.2020.111662
Ma, Chunfeng and Li, Xin and McCabe, Matthew F. (2020). Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sensing, 12, 14, 2303. 10.3390/rs12142303
Mimeau, L. and Tramblay, Y. and Brocca, L. and Massari, C. and Camici, S. and Finaud-Guyot, P. (2020). Modeling the response of soil moisture to climate variability in the Mediterranean region. Hydrology and Earth System Sciences Discussions, 2020, 1--29. 10.5194/hess-2020-302
M. Link and M. Drusch and K. Scipal (2020). Soil Moisture Information Content in SMOS, SMAP, AMSR2, and ASCAT Level-1 Data Over Selected In Situ Sites. IEEE Geoscience and Remote Sensing Letters, 17, 7, 1213--1217. 10.1109/LGRS.2019.2940633
Moreno-Martinez, Alvaro
and Piles, Maria
and Munoz-Mari, Jordi
and Campos-Taberner, Manuel
and Adsuara, Jose E.
and Mateo, Anna
and Perez-Suay, Adrian
and Javier Garcia-Haro, Francisco
and Camps-Valls, Gustau (2020). Machine Learning Methods for Spatial and Temporal Parameter Estimation. Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing, 5--35, Cham.. 10.1007/978-3-030-38617-7_2
Morteza Sadeghi and Lun Gao and Ardeshir Ebtehaj and Jean-Pierre Wigneron and Wade T. Crow and John T. Reager and Arthur W. Warrick (2020). Retrieving global surface soil moisture from GRACE satellite gravity data. Journal of Hydrology, 584, 124717. https://doi.org/10.1016/j.jhydrol.2020.124717
Naz, Bibi S. and Kollet, Stefan and Franssen, Harrie-Jan Hendricks and Montzka, Carsten and Kurtz, Wolfgang (2020). A 3km spatially and temporally consistent European daily soil moisture reanalysis from 2000 to 2015. Scientific Data, 7, 1, 111. 10.1038/s41597-020-0450-6
Nilda Sanchez and Laura Almendra and Javier Plaza and Ángel González-Zamora and José Martínez-Fernández (2020). Spatial averages of in situ measurements versus remote sensing observations: a soil moisture analysis. Journal of Spatial Science, 67, 439-454. 10.1080/14498596.2020.1833769
Peijun Li and Yuanyuan Zha and Chak-Hau Michael Tso and Liangsheng Shi and Danyang Yu and Yonggen Zhang and Wenzhi Zeng (2020). Data assimilation of uncalibrated soil moisture measurements from frequency-domain reflectometry. Geoderma, 374, 114432. https://doi.org/10.1016/j.geoderma.2020.114432
Portal, Gerard and Jagdhuber, Thomas and Vall-llossera, Merce and Camps, Adriano and Pablos, Miriam and Entekhabi, Dara and Piles, Maria (2020). Assessment of Multi-Scale SMOS and SMAP Soil Moisture Products across the Iberian Peninsula. Remote Sensing, 12, 3, 570. 10.3390/rs12030570
Sara Sadri and Ming Pan and Yoshihide Wada and Noemi Vergopolan and Justin Sheffield and James S. Famiglietti and Yann Kerr and Eric Wood (2020). A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP. Remote Sensing of Environment, 246, 111864. https://doi.org/10.1016/j.rse.2020.111864
Sebastian Helgert and Samiro Khodayar (2020). Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information. Remote Sensing of Environment, 246, 111812. https://doi.org/10.1016/j.rse.2020.111812
Sebastien Verrier (2020). Multifractal and multiscale entropy scaling of in-situ soil moisture time series: Study of SMOSMANIA network data, southwestern France. Journal of Hydrology, 585, 124821. https://doi.org/10.1016/j.jhydrol.2020.124821
Senyurek, Volkan and Lei, Fangni and Boyd, Dylan and Kurum, Mehmet and Gurbuz, Ali Cafer and Moorhead, Robert (2020). Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS. Remote Sensing, 12, 7, 1168. 10.3390/rs12071168
Solander, K. C. and Newman, B. D. and Carioca de Araujo, A. and Barnard, H. R. and Berry, Z. C. and Bonal, D. and Bretfeld, M. and Burban, B. and Antonio Candido, L. and Celleri, R. and Chambers, J. Q. and Christoffersen, B. O. and Detto, M. and Dorigo, W. A. and Ewers, B. E. and Jose Filgueiras Ferreira, S. and Knohl, A. and Leung, L. R. and McDowell, N. G. and Miller, G. R. and Terezinha Ferreira Monteiro, M. and Moore, G. W. and Negron-Juarez, R. and Saleska, S. R. and Stiegler, C. and Tomasella, J. and Xu, C. (2020). The pantropical response of soil moisture to El Nino. Hydrology and Earth System Sciences, 24, 5, 2303--2322. 10.5194/hess-24-2303-2020
Souissi, Roïya and Al Bitar, Ahmad and Zribi, Mehrez (2020). Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe. Water, 12, 11. 10.3390/w12113109
Suman, Swati and Srivastava, Prashant K. and Petropoulos, George P. and Pandey, Dharmendra K. and O{\textquoteright}Neill, Peggy E. (2020). Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective. Remote Sensing, 12, 12, 1977. 10.3390/rs12121977
Sun, Hao and Zhou, Baichi and Zhang, Chuanjun and Liu, Hongxing and Yang, Bo (2020). DSCALE\_mod16: A Model for Disaggregating Microwave Satellite Soil Moisture with Land Surface Evapotranspiration Products and Gridded Meteorological Data. Remote Sensing, 12, 6, 980. 10.3390/rs12060980
Wang, Lei and Fang, Shibo and Pei, Zhifang and Zhu, Yongchao and Khoi, Dao Nguyen and Han, Wei (2020). Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture. Remote Sensing, 12, 6, 1038. 10.3390/rs12061038
Wang, Yakun and Shi, Liangsheng and Lin, Lin and Holzman, Mauro and Carmona, Facundo and Zhang, Qiuru (2020). A robust data-worth analysis framework for soil moisture flow by hybridizing sequential data assimilation and machine learning. Vadose Zone Journal, 19, 1, e20026. 10.1002/vzj2.20026
Xaver, Angelika and Zappa, Luca and Rab, Gerhard and Pfeil, Isabella and Vreugdenhil, Mariette and Hemment, Drew and Dorigo, Wouter Arnoud (2020). Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications. Geoscientific Instrumentation, Methods and Data Systems, 9, 1, 117--139. 10.5194/gi-9-117-2020
Yangxiaoyue Liu and Wenlong Jing and Qi Wang and Xiaolin Xia (2020). Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms. Advances in Water Resources, 141, 103601. https://doi.org/10.1016/j.advwatres.2020.103601
Yuanhong Deng and Shijie Wang and Xiaoyong Bai and Guangjie Luo and Luhua Wu and Fei Chen and Jinfeng Wang and Qin Li and Chaojun Li and Yujie Yang and Zeyin Hu and Shiqi Tian (2020). Spatiotemporal dynamics of soil moisture in the karst areas of China based on reanalysis and observations data. Journal of Hydrology, 585, 124744. https://doi.org/10.1016/j.jhydrol.2020.124744
Abbaszadeh, P., Moradkhani, H., & Zhan, X. (2019). Downscaling SMAP radiometer soil moisture over the CONUS using an ensemble learning method. Water Resources Research, 55, 324-344. https://doi.org/10.1029/2018WR023354
Adamolekun, O. (2019). Field validation of proximal sensors on a typical Prairie field. https://hdl.handle.net/1993/33950
Afshar, M., Yilmaz, M., & Crow, W. (2019). Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products. Water Resources Research, 55, 7804-7825. https://doi.org/10.1029/2019WR025111
Albergel, C., Zheng, Y., Bonan, B., Dutra, E., Rodríguez-Fernández, N., Munier, S., Draper, C., de Rosnay, P., Muñoz-Sabater, J., Balsamo, G., Fairbairn, D., Meurey, C., & Calvet, J.-C. (2019). Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces. Hydrol. Earth Syst. Sci. Discuss. In review. https://doi.org/10.5194/hess-2019-534
Almagbile, A., Zeitoun, M., Hazaymeh, K., Sammour, H. A., & Sababha, N. (2019). Statistical analysis of estimated and observed soil moisture in sub-humid climate in north-western Jordan. Environmental monitoring and assessment, 191, 96. https://doi.org/10.1007/s10661-019-7230-9
Al-Yaari, A., Ducharne, A., Cheruy, F., Crow, W.T., & Wigneron, J.P. (2019). Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States. Sci Rep, 9, 1657. https://doi.org/10.1038/s41598-018-38309-5
Al-Yaari, A., Wigneron, J.P., Dorigo, W., Colliander, A., Pellarin, T., Hahn, S., Mialon, A., Richaume, P., Fernandez-Moran, R., Fan, L., Kerr, Y.H., & De Lannoy, G. (2019). Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements. Remote Sensing of Environment, 224, 289-303. https://doi.org/10.1016/j.rse.2019.02.008
Araghi, A., Adamowski, J., Martinez, C.J., & Olesen, J.E. (2019). Projections of future soil temperature in northeast Iran. Geoderma, 349, 11-24. https://doi.org/10.1016/j.geoderma.2019.04.034
Ardeshir Ebtehaj and Rafael L. Bras (2019). A physically constrained inversion for high-resolution passive microwave retrieval of soil moisture and vegetation water content in L-band. Remote Sensing of Environment, 233, 111346. https://doi.org/10.1016/j.rse.2019.111346
Arora, B., Dwivedi, D., Faybishenko, B., Jana, R. B., & Wainwright, H. M. (2019). Understanding and predicting vadose zone processes. Reviews in Mineralogy and Geochemistry, 85, 303-328. https://doi.org/10.2138/rmg.2019.85.10
Asmuß, T., Bechtold, M., & Tiemeyer, B. (2019). On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils. Remote Sensing 2019, 11, 1659. https://doi.org/10.3390/rs11141659
Babaeian, E., Sadeghi, M., Jones, S.B., Montzka, C., Vereecken, H., & Tuller, M. (2019). Ground. Proximal and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics. https://doi.org/10.1029/2018rg000618
Baczyk, M. K., Gromek, A., Kulpa, K., Gurdak, R., & Grzybowski, P. (2019). Neural Network-Based Soil Moisture Estimation Using Satellite SAR Data. 2019 Signal Processing Symposium (SPSympo). https://doi.org/10.1109/SPS.2019.8881987
Baik, J., Zohaib, M., Kim, U., Aadil, M., & Choi, M. (2019). Agricultural drought assessment based on multiple soil moisture products. Journal of arid environments, 167, 43-55. https://doi.org/10.1016/j.jaridenv.2019.04.007
Bai, L., Long, D., & Yan, L. (2019). Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55, 1105-1128. https://doi.org/10.1029/2018WR024162
Bai, L., Lv, X., & Li, X. (2019). Evaluation of Two SMAP Soil Moisture Retrievals Using Modeled-and Ground-Based Measurements. Remote Sensing 2019, 11, 2891. https://doi.org/10.3390/rs11242891
Baldwin, D., Manfreda, S., Lin, H., & Smithwick, E. A. (2019). Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model. Remote Sensing 2019, 11, 2013. https://doi.org/10.3390/rs11172013
Barbosa, L. R., Lira, N. B. d., Coelho, V. H. R., Silans, A. M. B. P. d., Gadêlha, A. N., & Almeida, C. d. N. (2019). Stability of Soil Moisture Patterns Retrieved at Different Temporal Resolutions in a Tropical Watershed. Revista Brasileira de Ciência do Solo, 43. https://doi.org/10.1590/18069657rbcs20180236
Berthelin, R., Rinderer, M., Andreo, B., Baker, A., Kilian, D., Leonhardt, G., Lotz, A., Lichtenwoehrer, K., Mudarra, M., Padilla, I. Y., Pantoja Agreda, F., Rosolem, R., Vale, A., & Hartmann, A. (2019). A soil moisture monitoring network to characterize karstic recharge and evapotranspiration at five representative sites across the globe. Geosci. Instrum. Method. Data Syst., 9, 11–23. https://doi.org/10.5194/gi-9-11-2020
Blyverket, J. (2019). Land Surface Data Assimilation of Satellite Derived Surface Soil Moisture: Towards an Integrated Representation of the Arctic Hydrological Cycle. https://bora.uib.no/handle/1956/20940
Blyverket, J., Hamer, P., Bertino, L., Albergel, C., Fairbairn, D., & Lahoz, W. (2019). An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US. Remote Sensing, 11. https://doi.org/10.3390/rs11050478
Blyverket, J., Hamer, P. D., Bertino, L., Albergel, C., Fairbairn, D., & Lahoz, W. A. (2019). Improving soil moisture estimates over the contiguous US using satellite retrievals and ensemble based data assimilation techniques. Preprints. https://doi.org/10.20944/preprints201901.0224.v1
Caldwell, T. G., Bongiovanni, T., Cosh, M. H., Jackson, T. J., Colliander, A., Abolt, C. J., et al. (2019). The Texas Soil Observation Network: A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation. Vadose Zone Journal, 18. https://doi.org/10.2136/vzj2019.04.0034
Carrera, M. L., Bilodeau, B., Bélair, S., Abrahamowicz, M., Russell, A., & Wang, X. (2019). Assimilation of passive L-band microwave brightness temperatures in the Canadian Land Data Assimilation System: Impacts on short-range warm season Numerical Weather Prediction. Journal of Hydrometeorology, 20, 1053-1079. https://doi.org/10.1175/JHM-D-18-0133.1
Chen, Y., Sun, L., Wang, W., & Pei, Z. (2019). Application of Sentinel 2 data for drought monitoring in Texas, America. 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). https://doi.org/10.1109/Agro-Geoinformatics.2019.8820491
Chuchón Prado, R. (2019). Láminas de riego en el cultivo de papa (Solanum tuberosum L.) variedad “unica” mediante riego por goteo en La Molina. Universidad Nacional Agraria La Molina. http://repositorio.lamolina.edu.pe/handle/UNALM/4245
Crow, W. T. (2019). Utility of soil moisture data products for natural disaster applications. Elsevier Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment. https://doi.org/10.1016/B978-0-12-814899-0.00003-1
Dasgupta, K., Das, K., & Padmanaban, M. (2019). Soil Moisture Evaluation Using Machine Learning Techniques on Synthetic Aperture Radar (SAR) And Land Surface Model. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8900220
Das, K., Singh, J., & Hazra, J. (2019). Comparison of Smap, Gldas and Simulated Soil Moisture Datasets Over A Malaysian Region. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10.1109/IGARSS.2019.8900589
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A., Yueh, S., et al. (2019). The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product. Remote Sensing of Environment, 233, 111380. https://doi.org/10.1016/j.rse.2019.111380
Deng, K.A.K., Lamine, S., Pavlides, A., Petropoulos, G.P., Bao, Y., Srivastava, P.K., & Guan, Y. (2019). Large scale operational soil moisture mapping from passive MW radiometry: SMOS product evaluation in Europe & USA. International Journal of Applied Earth Observation and Geoinformation, 80, 206-217. https://doi.org/10.1016/j.jag.2019.04.015
Deng, K., Lamine, S., Pavlides, A., Petropoulos, G., Srivastava, P., Bao, Y., Hristopulos, D., & Anagnostopoulos, V. (2019). Operational Soil Moisture from ASCAT in Support of Water Resources Management. Remote Sensing, 11. https://doi.org/10.3390/rs11050579
Deng, Y., Wang, S., Bai, X., Wu, L., Cao, Y., Li, H., (2019). Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations. Hydrological Processes, 34, 836– 851. https://doi.org/10.1002/hyp.13636
Di, Chongli and Wang, Tiejun and Istanbulluoglu, Erkan and Jayawardena, A. and Li, Si-Liang and Chen, Xi (2019). Deterministic chaotic dynamics in soil moisture across Nebraska. Journal of Hydrology, 578. 10.1016/j.jhydrol.2019.124048
Draper, Clara and Reichle, Rolf H. (2019). Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses. Monthly Weather Review, 147, 6, 2163-2188. 10.1175/MWR-D-18-0393.1
Eroglu, Orhan and Kurum, Mehmet and Boyd, Dylan and Gurbuz, Ali Cafer (2019). High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks. Remote Sensing, 11, 19, 2272. 10.3390/rs11192272
Fairbairn, David and de Rosnay, Patricia and Browne, Philip A. (2019). The New Stand-Alone Surface Analysis at ECMWF: Implications for Land–Atmosphere DA Coupling. Journal of Hydrometeorology, 20, 10, 2023-2042. 10.1175/JHM-D-19-0074.1
Fairbairn, David and de Rosnay, Patricia and Browne, Philip and Albergel, Clement and Isaksen, Lars (2019). H SAF root-zone soil moisture products from ASCAT assimilation.
Fan, Dong and Wu, Hua and Dong, Guotao and Jiang, Xiaoguang and Xue, Huazhu (2019). A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data. Remote Sensing, 11, 24, 2962. 10.3390/rs11242962
Fang, Bin and Lakshmi, Venkat and Bindlish, Rajat and Jackson, Thomas J and Liu, Pang-Wei (2019). Downscaling and Validation of SMAP Radiometer Soil Moisture in CONUS. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 6194-6197. 10.1109/IGARSS.2019.8897943
Ford, Trent W. and Quiring, Steven M. (2019). Comparison of Contemporary In Situ, Model, and Satellite Remote Sensing Soil Moisture With a Focus on Drought Monitoring. Water Resources Research, 55, 2, 1565-1582. 10.1029/2018WR024039
Fu, Haoyang and Zhou, Tingting and Sun, Chenglin (2019). Evaluation and Analysis of AMSR2 and FY3B Soil Moisture Products by an In Situ Network in Cropland on Pixel Scale in the Northeast of China. Remote Sensing, 11, 7, 868. 10.3390/rs11070868
Ghilain, Nicolas and Arboleda, Alirio and Batelaan, Okke and Ardö, Jonas and Trigo, Isabel and Barrios, Jose-Miguel and Gellens-Meulenberghs, Francoise (2019). A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation. Remote Sensing, 11, 17, 1968. 10.3390/rs11171968
Gruber, A., De Lannoy, G., & Crow, W. (2019). A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation. Remote Sensing of Environment, 228, 105-114. https://doi.org/10.1016/j.rse.2019.04.003
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., & Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology. Earth System Science Data, 11, 717-739. https://doi.org/10.5194/essd-11-717-2019
Gu, Y., Gao, M., & Zhao, G. (2019). Earth Observation Payloads and Data Applications of Tiangong-2 Space Laboratory: Technology, Method and Application. Springer. https://doi.org/10.1007/978-981-13-3501-3_1
Hongtao, J., Huanfeng, S., Xinghua, L., Chao, Z., Huiqin, L., & Fangni, L. (2019). Extending the SMAP 9-km soil moisture product using a spatio-temporal fusion model. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111224
Hu, T., Zhao, T., Zhao, K., & Shi, J. (2019). A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data. International Journal of Remote Sensing, 40, 6993-7016. https://doi.org/10.1080/01431161.2019.1597307
Kang, C.S., Kanniah, K.D., & Kerr, Y.H. (2019). Calibration of SMOS Soil Moisture Retrieval Algorithm: A Case of Tropical Site in Malaysia. IEEE Transactions on Geoscience and Remote Sensing, 57, 3827-3839. https://doi.org/10.1109/tgrs.2018.2888535
Kiyoung, K., Sungwon, J., & Yeongil, L. (2019). A Study for establishment of soil moisture station in mountain terrain (1): the representative analysis of soil moisture for construction of Cosmic-ray verification system. Journal of Korea Water Resources Association, 52, 51-60. https://doi.org/10.3741/JKWRA.2019.52.1.51
Kovács, K.Z., Hemment, D., Woods, M., van der Velden, N.K., Xaver, A., Giesen, R.H., Burton, V.J., Garrett, N.L., Zappa, L., Long, D., Dobos, E., & Skalsky, R. (2019). Citizen observatory based soil moisture monitoring – the GROW example. Hungarian Geographical Bulletin, 68, 119-139. https://doi.org/10.15201/hungeobull.68.2.2
Kumar, S., Newman, M., Wang, Y., & Livneh, B. (2019). Potential Reemergence of Seasonal Soil Moisture Anomalies in North America. Journal of Climate, 32, 2707-2734. https://doi.org/10.1175/jcli-d-18-0540.1
Liao, W., Wang, D., Wang, G., Xia, Y., & Liu, X. (2019). Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database. Journal of Meteorological Research, 33, 501-518. https://doi.org/10.1007/s13351-019-8121-2
Luo, W., Xu, X., Liu, W., Liu, M., Li, Z., Peng, T., Xu, C., Zhang, Y., & Zhang, R. (2019). UAV based soil moisture remote sensing in a karst mountainous catchment. Catena, 174, 478-489. https://doi.org/10.1016/j.catena.2018.11.017
Ma, H., Zeng, J., Chen, N., Zhang, X., Cosh, M.H., & Wang, W. (2019). Satellite surface soil moisture from SMAP, SMOS, AMSR2 and ESA CCI: A comprehensive assessment using global ground-based observations. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111215
Myeni, L., Moeletsi, M.E., & Clulow, A.D. (2019). Present status of soil moisture estimation over the African continent. Journal of Hydrology: Regional Studies, 21, 14-24. https://doi.org/10.1016/j.ejrh.2018.11.004
Nguyen, H.H., Jeong, J., & Choi, M. (2019). Extension of cosmic-ray neutron probe measurement depth for improving field scale root-zone soil moisture estimation by coupling with representative in-situ sensors. Journal of Hydrology, 571, 679-696. https://doi.org/10.1016/j.jhydrol.2019.02.018
Ochsner, T.E., Linde, E., Haffner, M., & Dong, J. (2019). Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging. Water Resources Research. https://doi.org/10.1029/2018wr024535
Pal, M., & Maity, R. (2019). Development of a spatially-varying Statistical Soil Moisture Profile model by coupling memory and forcing using hydrologic soil groups. Journal of Hydrology, 570, 141-155. https://doi.org/10.1016/j.jhydrol.2018.12.042
Quintana Seguí, Pere and Barella-Ortiz, Anaïs and Regueiro-Sanfiz, Sabela and Miguez-Macho, Gonzalo (2019). The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets. Water Resources Management. 10.1007/s11269-018-2160-9
Rodríguez-Fernández, N., de Rosnay, P., Albergel, C., Richaume, P., Aires, F., Prigent, C., & Kerr, Y. (2019). SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact. Remote Sensing, 11. https://doi.org/10.3390/rs11111334
Sadeghi, M., Tuller, M., Warrick, A.W., Babaeian, E., Parajuli, K., Gohardoust, M.R., & Jones, S.B. (2019). An analytical model for estimation of land surface net water flux from near-surface soil moisture observations. Journal of Hydrology, 570, 26-37. https://doi.org/10.1016/j.jhydrol.2018.12.038
Sun, H., Cai, C., Liu, H., & Yang, B. (2019). Microwave and Meteorological Fusion: A method of Spatial Downscaling of Remotely Sensed Soil Moisture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1107-1119. https://doi.org/10.1109/jstars.2019.2901921
Tian, J., Zhang, B., He, C., Han, Z., Bogena, H.R., & Huisman, J.A. (2019). Dynamic response patterns of profile soil moisture wetting events under different land covers in the Mountainous area of the Heihe River Watershed. Northwest China. Agricultural and Forest Meteorology, 271, 225-239. https://doi.org/10.1016/j.agrformet.2019.03.006
Tian, S., Renzullo, L.J., van Dijk, A.I.J.M., Tregoning, P., & Walker, J.P. (2019). Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response. Hydrology and Earth System Sciences, 23, 1067-1081. https://doi.org/10.5194/hess-23-1067-2019
Wang, C., Wang, Z., Kong, Y., Zhang, F., Yang, K., & Zhang, T. (2019). Most of the Northern Hemisphere Permafrost Remains under Climate Change. Sci Rep, 9, 3295. https://doi.org/10.1038/s41598-019-39942-4
Wang, L., He, B., Bai, X., Xing, M. (2019). Assessment of Different Vegetation Parameters for Parameterizing the Coupled Water Cloud Model and Advanced Integral Equation Model for Soil Moisture Retrieval Using Time Series Sentinel-1A Data. Photogrammetric Engineering & Remote Sensing, 85, 7, 43-54(12). https://doi.org/10.14358/PERS.85.1.43
Wang, Q., van der Velde, R., Ferrazzoli, P., Chen, X., Bai, X., & Su, Z. (2019). Mapping soil moisture across the Tibetan Plateau plains using Aquarius active and passive L-band microwave observations. International Journal of Applied Earth Observation and Geoinformation, 77, 108-118. https://doi.org/10.1016/j.jag.2019.01.005
Wang, Y., Yang, J., Chen, Y., Fang, G., Duan, W., Li, Y., & De Maeyer, P. (2019). Quantifying the Effects of Climate and Vegetation on Soil Moisture in an Arid Area. China. Water, 11. https://doi.org/10.3390/w11040767
Xia, Y., Hao, Z., Shi, C., Li, Y., Meng, J., Xu, T., Wu, X., & Zhang, B. (2019). Regional and Global Land Data Assimilation Systems Innovations, Challenges, and Prospects. Journal of Meteorological Research, 33, 159-189. https://doi.org/10.1007/s13351-019-8172-4
Zaussinger, F., Dorigo, W., Gruber, A., Tarpanelli, A., Filippucci, P., & Brocca, L. (2019). Estimating irrigation water use over the contiguous United States by combining satellite and reanalysis soil moisture data. Hydrology and Earth System Sciences, 23, 897-923. https://doi.org/10.5194/hess-23-897-2019
Zeng, L., Hu, S., Xiang, D., Zhang, X., Li, D., Li, L., & Zhang, T. (2019). Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method. Remote Sensing, 11. https://doi.org/10.3390/rs11030284
Zhang, Q., Fan, K., Singh, V.P., Song, C., Xu, C.Y., & Sun, P. (2019). Is Himalayan-Tibetan Plateau "drying"? Historical estimations and future trends of surface soil moisture. Sci Total Environ, 658, 374-384. https://doi.org/10.1016/j.scitotenv.2018.12.209
Zhang, R., Kim, S., & Sharma, A. (2019). A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sensing of Environment, 223, 82-94. https://doi.org/10.1016/j.rse.2019.01.015
Zhang, S., Meurey, C., & Calvet, J.-C. (2019). Identification of soil-cooling rains in southern France from soil temperature and soil moisture observations. Atmospheric Chemistry and Physics, 19, 5005-5020. https://doi.org/10.5194/acp-19-5005-2019
Zhu, L., Wang, H., Tong, C., Liu, W., & Du, B. (2019). Evaluation of ESA Active, Passive and Combined Soil Moisture Products Using Upscaled Ground Measurements. Sensors (Basel), 19. https://doi.org/10.3390/s19122718
Al-Yaari, A., Dayau, S., Chipeaux, C., Aluome, C., Kruszewski, A., Loustau, D., & Wigneron, J.P. (2018). The AQUI Soil Moisture Network for Satellite Microwave Remote Sensing Validation in South-Western France. Remote Sensing, 10. https://doi.org/10.3390/rs10111839
Bao, Y., Lin, L., Wu, S., Kwal Deng, K.A., & Petropoulos, G.P. (2018). Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model. International Journal of Applied Earth Observation and Geoinformation, 72, 76-85. https://doi.org/10.1016/j.jag.2018.05.026
Belfort, B., Toloni, I., Ackerer, P., Cotel, S., Viville, D., & Lehmann, F. (2018). Vadose Zone Modeling in a Small Forested Catchment: Impact of Water Pressure Head Sampling Frequency on 1D-Model Calibration. Geosciences, 8. https://doi.org/10.3390/geosciences8020072
Benninga, H.-J.F., Carranza, C.D.U., Pezij, M., van Santen, P., van der Ploeg, M.J., Augustijn, D.C.M., & van der Velde, R. (2018). The Raam regional soil moisture monitoring network in the Netherlands. Earth System Science Data, 10, 61-79. https://doi.org/10.5194/essd-10-61-2018
Bogena, H.R., Montzka, C., Huisman, J.A., Graf, A., Schmidt, M., Stockinger, M., von Hebel, C., Hendricks-Franssen, H.J., van der Kruk, J., Tappe, W., Lücke, A., Baatz, R., Bol, R., Groh, J., Pütz, T., Jakobi, J., Kunkel, R., Sorg, J., & Vereecken, H. (2018). The TERENO-Rur Hydrological Observatory: A Multiscale Multi-Compartment Research Platform for the Advancement of Hydrological Science. Vadose Zone Journal, 17. https://doi.org/10.2136/vzj2018.03.0055
Cassardo, C., Park, S., O, S., & Galli, M. (2018). Projected Changes in Soil Temperature and Surface Energy Budget Components over the Alps and Northern Italy. Water, 10. https://doi.org/10.3390/w10070954
Dabrowska-Zielinska, K., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., & Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sensing, 10. https://doi.org/10.3390/rs10121979
Dirmeyer, P.A., Chen, L., Wu, J., Shin, C.S., Huang, B., Cash, B.A., Bosilovich, M.G., Mahanama, S., Koster, R.D., Santanello, J.A., Ek, M.B., Balsamo, G., Dutra, E., & Lawrence, D.M. (2018). Verification of land-atmosphere coupling in forecast models, reanalyses and land surface models using flux site observations. J Hydrometeorol, 19, 375-392. https://doi.org/10.1175/JHM-D-17-0152.1
Ebrahimi, M., Alavipanah, S.K., Hamzeh, S., Amiraslani, F., Neysani Samany, N., & Wigneron, J.-P. (2018). Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. Journal of Hydrology, 557, 740-752. https://doi.org/10.1016/j.jhydrol.2017.12.051
Esposito, G., Matano, F., & Scepi, G. (2018). Analysis of Increasing Flash Flood Frequency in the Densely Urbanized Coastline of the Campi Flegrei Volcanic Area. Frontiers in Earth Science, 6, Italy.. https://doi.org/10.3389/feart.2018.00063
Fang, B., Lakshmi, V., Bindlish, R., & Jackson, T. (2018). AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data. Remote Sensing, 10. https://doi.org/10.3390/rs10101575
Fersch, B., Jagdhuber, T., Schrön, M., Völksch, I., & Jäger, M. (2018). Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network. Water Resources Research, 54, 9364-9383. https://doi.org/10.1029/2018wr023337
Franz, T., Mengistu, M., Everson, C., & Vather, T. (2018). Cosmic ray neutrons provide an innovative technique for estimating intermediate scale soil moisture. South African Journal of Science, 114. https://doi.org/10.17159/sajs.2018/20170422
González-Zamora, Á., Sánchez, N., Pablos, M., & Martínez-Fernández, J. (2018). CCI soil moisture assessment with SMOS soil moisture and in situ data under different environmental conditions and spatial scales in Spain. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2018.02.010
Greifeneder, F., Khamala, E., Sendabo, D., Wagner, W., Zebisch, M., Farah, H., & Notarnicola, C. (2018). Detection of soil moisture anomalies based on Sentinel-1. Physics and Chemistry of the Earth Parts A/B/C. https://doi.org/10.1016/j.pce.2018.11.009
Gruber, A., Crow, W.T., & Dorigo, W.A. (2018). Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain. Water Resources Research, 54, 1353-1367. https://doi.org/10.1002/2017wr021277
Gumbricht, T. (2018). Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates. Remote Sensing, 10. https://doi.org/10.3390/rs10040611
Högström, E., Heim, B., Bartsch, A., Bergstedt, H., & Pointner, G. (2018). Evaluation of a MetOp ASCAT-Derived Surface Soil Moisture Product in Tundra Environments. Journal of Geophysical Research: Earth Surface, 123, 3190-3205. https://doi.org/10.1029/2018jf004658
Jeong, J., Cho, S., Baik, J., & Choi, M. (2018). A Study on the Establishment of a Korean Soil Moisture Network (2): Measurement of Intermediate-Scale Soil Moisture Using a Cosmic-Ray Sensor. Journal of the Korean Society of Hazard Mitigation, 18, 83-91. https://doi.org/10.9798/kosham.2018.18.7.83
Kang, J., Jin, R., Li, X., Zhang, Y., & Zhu, Z. (2018). Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression. Remote Sensing, 10. https://doi.org/10.3390/rs10020192
Kim, H., Parinussa, R., Konings, A.G., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., & Choi, M. (2018). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. https://doi.org/10.1016/j.rse.2017.10.026
Kim, S., Jeong, J., Zohaib, M., & Choi, M. (2018). Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine. Stochastic Environmental Research and Risk Assessment, 32, 3455-3473. https://doi.org/10.1007/s00477-018-1620-3
Kolassa, J., Reichle, R.H., Liu, Q., Alemohammad, S.H., Gentine, P., Aida, K., Asanuma, J., Bircher, S., Caldwell, T., Colliander, A., Cosh, M., Collins, C.H., Jackson, T.J., Martinez-Fernandez, J., McNairn, H., Pacheco, A., Thibeault, M., & Walker, J.P. (2018). Estimating surface soil moisture from SMAP observations using a Neural Network technique. Remote Sens Environ, 204, 43-59. https://doi.org/10.1016/j.rse.2017.10.045
Lei, F., Crow, W.T., Holmes, T.R.H., Hain, C., & Anderson, M.C. (2018). Global Investigation of Soil Moisture and Latent Heat Flux Coupling Strength. Water Resources Research, 54, 8196-8215. https://doi.org/10.1029/2018wr023469
Lei, F., Crow, W.T., Shen, H., Su, C.-H., Holmes, T.R.H., Parinussa, R.M., & Wang, G. (2018). Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error. Remote Sensing of Environment, 205, 85-99. https://doi.org/10.1016/j.rse.2017.11.002
Lembrechts, J.J., Nijs, I., & Lenoir, J. (2018). Incorporating microclimate into species distribution models. Ecography. https://doi.org/10.1111/ecog.03947
Li, Y., Li, Y., Yuan, X., Zhang, L., & Sha, S. (2018). Evaluation of Model-Based Soil Moisture Drought Monitoring over Three Key Regions in China. Journal of Applied Meteorology and Climatology, 57, 1989-2004. https://doi.org/10.1175/jamc-d-17-0118.1
Martens, B., de Jeu, R., Verhoest, N., Schuurmans, H., Kleijer, J., & Miralles, D. (2018). Towards Estimating Land Evaporation at Field Scales Using GLEAM. Remote Sensing, 10. https://doi.org/10.3390/rs10111720
Meng, Q., Zhang, L., Xie, Q., Yao, S., Chen, X., & Zhang, Y. (2018). Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology, 1-11. https://doi.org/10.1155/2018/9315132
Mishra, V., Shah, R., Azhar, S., Shah, H., Modi, P., & Kumar, R. (2018). Reconstruction of droughts in India using multiple land-surface models (1951–2015). Hydrology and Earth System Sciences, 22, 2269-2284. https://doi.org/10.5194/hess-22-2269-2018
Murguia-Flores, F., Arndt, S., Ganesan, A.L., Murray-Tortarolo, G., & Hornibrook, E.R.C. (2018). Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geoscientific Model Development, 11, 2009-2032. https://doi.org/10.5194/gmd-11-2009-2018
Parinussa, R.M., Wang, G., Liu, Y., Lou, D., Hagan, D.F.T., Zhan, M., Su, B., & Jiang, T. (2018). Improved surface soil moisture anomalies from Fengyun-3B over the Jiangxi province of the People’s Republic of China. International Journal of Remote Sensing, 1-13. https://doi.org/10.1080/01431161.2018.1500729
Qin, M., Giménez, D., & Miskewitz, R. (2018). Temporal dynamics of subsurface soil water content estimated from surface measurement using wavelet transform. Journal of Hydrology, 563, 834-850. https://doi.org/10.1016/j.jhydrol.2018.06.023
Rowlandson, T.L., Berg, A.A., Roy, A., Kim, E., Pardo Lara, R., Powers, J., Lewis, K., Houser, P., McDonald, K., Toose, P., Wu, A., De Marco, E., Derksen, C., Entin, J., Colliander, A., Xu, X., & Mavrovic, A. (2018). Capturing agricultural soil freeze/thaw state through remote sensing and ground observations: A soil freeze/thaw validation campaign. Remote Sensing of Environment, 211, 59-70. https://doi.org/10.1016/j.rse.2018.04.003
Santi, E., Paloscia, S., Pettinato, S., Brocca, L., Ciabatta, L., & Entekhabi, D. (2018). Integration of microwave data from SMAP and AMSR2 for soil moisture monitoring in Italy. Remote Sensing of Environment, 212, 21-30. https://doi.org/10.1016/j.rse.2018.04.039
Spennemann, P.C., Salvia, M., Ruscica, R.C., Sörensson, A.A., Grings, F., & Karszenbaum, H. (2018). Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models. International Journal of Applied Earth Observation and Geoinformation, 64, 96-103. https://doi.org/10.1016/j.jag.2017.08.016
Stein, S., Eberhard, E., Grosse, M., Helming, K., Hierold, W., Hoffmann, C., Kühnert, T., Liess, M., Russel, D., & S., S. (2018). Report on available soil data for German agricultural areas. -
Tóth, E., Gelybó, G., Dencső, M., Kása, I., Birkás, M., & Horel, Á. (2018). Soil CO 2 Emissions in a Long-Term Tillage Treatment Experiment. Soil Management and Climate Change, 293-307
Um, M.-J., Kim, M., Kim, Y., & Park, D. (2018). Drought Assessment with the Community Land Model for 1951–2010 in East Asia. Sustainability, 10. https://doi.org/10.3390/su10062100
van der Schalie, R., de Jeu, R., Parinussa, R., Rodríguez-Fernández, N., Kerr, Y., Al-Yaari, A., Wigneron, J.-P., & Drusch, M. (2018). The Effect of Three Different Data Fusion Approaches on the Quality of Soil Moisture Retrievals from Multiple Passive Microwave Sensors. Remote Sensing, 10. https://doi.org/10.3390/rs10010107
Wang, X., Ciais, P., Wang, Y., & Zhu, D. (2018). Divergent response of seasonally dry tropical vegetation to climatic variations in dry and wet seasons. Glob Chang Biol. https://doi.org/10.1111/gcb.14335
Williamson, M., Rowlandson, T.L., Berg, A.A., Roy, A., Toose, P., Derksen, C., Arnold, L., & Tetlock, E. (2018). L-band radiometry freeze/ thaw validation using air temperature and ground measurements. Remote Sensing Letters, 9, 403-410. https://doi.org/10.1080/2150704x.2017.1422872
Wu, M., Scholze, M., Voßbeck, M., Kaminski, T., & Hoffmann, G. (2018). Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS. Remote Sensing, 11. https://doi.org/10.3390/rs11010027
Wu, M., Scholze, M., Voßbeck, M., Kaminski, T., & Hoffmann, G. (2018). Simultaneous Assimilation of Remotely Sensed Soil Moisture and FAPAR for Improving Terrestrial Carbon Fluxes at Multiple Sites Using CCDAS. Remote Sensing, 11. https://doi.org/10.3390/rs11010027
Xu, H., Yuan, Q., Li, T., Shen, H., Zhang, L., & Jiang, H. (2018). Quality Improvement of Satellite Soil Moisture Products by Fusing with In-Situ Measurements and GNSS-R Estimates in the Western Continental U.S. Remote Sensing, 10. https://doi.org/10.3390/rs10091351
Ye, K., & Lau, N.-C. (2018). Characteristics of Eurasian snowmelt and its impacts on the land surface and surface climate. Climate Dynamics. https://doi.org/10.1007/s00382-018-4180-9
Zhang, S., Calvet, J.-C., Darrozes, J., Roussel, N., Frappart, F., & Bouhours, G. (2018). Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France. Hydrology and Earth System Sciences, 22, 1931-1946. https://doi.org/10.5194/hess-22-1931-2018
Zhao, L., & Yang, Z.-L. (2018). Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation. Remote Sensing of Environment, 216, 13-27. https://doi.org/10.1016/j.rse.2018.06.033
Abdi, A., Boke-Olén, N., Tenenbaum, D., Tagesson, T., Cappelaere, B., & Ardö, J. (2017). Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030294
Afshar, M.H., & Yilmaz, M.T. (2017). The added utility of nonlinear methods compared to linear methods in rescaling soil moisture products. Remote Sensing of Environment, 196, 224-237. https://doi.org/10.1016/j.rse.2017.05.017
Al-Yaari, A., Wigneron, J.P., Kerr, Y., Rodriguez-Fernandez, N., O'Neill, P.E., Jackson, T.J., De Lannoy, G.J.M., Al Bitar, A., Mialon, A., Richaume, P., Walker, J.P., Mahmoodi, A., & Yueh, S. (2017). Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets. Remote Sensing of Environment, 193, 257-273. https://doi.org/10.1016/j.rse.2017.03.010
Anoop, S., Maurya, D.K., Rao, P.V.N., & Sekhar, M. (2017). Validation and Comparison of LPRM Retrieved Soil Moisture Using AMSR2 Brightness Temperature at Two Spatial Resolutions in the Indian Region. IEEE Geoscience and Remote Sensing Letters, 14, 1561-1564. https://doi.org/10.1109/lgrs.2017.2722542
Baguis Pierre, & Emmanuel, R. (2017). Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data. Remote Sensing, 9. https://doi.org/10.3390/rs9080820
Brocca, L., Crow, W.T., Ciabatta, L., Massari, C., de Rosnay, P., Enenkel, M., Hahn, S., Amarnath, G., Camici, S., Tarpanelli, A., & Wagner, W. (2017). A Review of the Applications of ASCAT Soil Moisture Products. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2285-2306. https://doi.org/10.1109/jstars.2017.2651140
Candy, B., Saunders, R.W., Ghent, D., & Bulgin, C.E. (2017). The Impact of Satellite-Derived Land Surface Temperatures on Numerical Weather Prediction Analyses and Forecasts. Journal of Geophysical Research: Atmospheres, 122, 9783-9802. https://doi.org/10.1002/2016jd026417
Cerlini, P.B., Meniconi, S., & Brunone, B. (2017). Groundwater Supply and Climate Change Management by Means of Global Atmospheric Datasets. Preliminary Results. Procedia Engineering, 186, 420-427. https://doi.org/10.1016/j.proeng.2017.03.245
De Santis, D., & Biondi, D. (2017). A quality assessment of the soil water index by the propagation of ASCAT soil moisture error estimates through an exponential filter. International Journal of Remote Sensing, 39, 232-257. https://doi.org/10.1080/01431161.2017.1382745
Dong, J., & Crow, W. (2017). An improved triple collocation analysis algorithm for decomposing auto-correlated and white soil moisture retrieval errors. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027387
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P.D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y.Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S.I., Smolander, T., & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185-215. https://doi.org/10.1016/j.rse.2017.07.001
Dwevedi, A., Kumar, P., Kumar, P., Kumar, Y., Sharma, Y.K., & Kayastha, A.M. (2017). 15 - Soil sensors: detailed insight into research updates, significance, and future prospects A2 - Grumezescu, Alexandru Mihai. New Pesticides and Soil Sensors. Academic Press, 561-594
Emery, C. (2017). Contribution de la future mission altim´etrique `a large fauch´ee SWOT pour la mod´elisation hydrologique `a grande ´echelle. -
Fernandez-Moran, R., Wigneron, J.P., De Lannoy, G., Lopez-Baeza, E., Parrens, M., Mialon, A., Mahmoodi, A., Al-Yaari, A., Bircher, S., Al Bitar, A., Richaume, P., & Kerr, Y. (2017). A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm. International Journal of Applied Earth Observation and Geoinformation, 62, 27-38. https://doi.org/10.1016/j.jag.2017.05.013
Gasch, C.K., Brown, D.J., Brooks, E.S., Yourek, M., Poggio, M., Cobos, D.R., & Campbell, C.S. (2017). A pragmatic, automated approach for retroactive calibration of soil moisture sensors using a two-step, soil-specific correction. Computers and Electronics in Agriculture, 137, 29-40. https://doi.org/10.1016/j.compag.2017.03.018
Gruber, A., Dorigo, W.A., Crow, W., & Wagner, W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing, 55, 6780-6792. https://doi.org/10.1109/TGRS.2017.2734070
Hartmann, A., Gleeson, T., Wada, Y., & Wagener, T. (2017). Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity. Proc Natl Acad Sci U S A, 114, 2842-2847. https://doi.org/10.1073/pnas.1614941114
Ji, P., Yuan, X., & Liang, X.-Z. (2017). Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling?. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027366
Jung, C., Lee, Y., Cho, Y., & Kim, S. (2017). A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging. Remote Sensing, 9. https://doi.org/10.3390/rs9080870
Kapilaratne, R.G.C.J., & Lu, M. (2017). Automated general temperature correction method for dielectric soil moisture sensors. Journal of Hydrology, 551, 203-216. https://doi.org/10.1016/j.jhydrol.2017.05.050
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D.N., & Wood, E.F. (2017). Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons. Advances in Water Resources, 109, 236-252. https://doi.org/10.1016/j.advwatres.2017.09.010
Kim, H., Parinussa, R., Konings, A.G., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., Choi, M. (2017). Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products. Remote Sensing of Environment, 204, 260-275. dx.doi.org/10.1016/j.rse.2017.10.026
Kim, S., Balakrishnan, K., Liu, Y., Johnson, F., & Sharma, A. (2017). Spatial Disaggregation of Coarse Soil Moisture Data by Using High-Resolution Remotely Sensed Vegetation Products. IEEE Geoscience and Remote Sensing Letters, 14, 1604-1608. https://doi.org/10.1109/lgrs.2017.2725945
Kolassa, J., Gentine, P., Prigent, C., Aires, F., & Alemohammad, S.H. (2017). Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation. Remote Sensing of Environment, 195, 202-217. https://doi.org/10.1016/j.rse.2017.04.020
Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C.J., Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel, M., Van Roozendael, M., Wenzel, S., & Willén, U. (2017). Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203, 9-39. https://doi.org/10.1016/j.rse.2017.01.007
Lee, J.H., Zhao, C., & Kerr, Y. (2017). Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products. Remote Sensing, 9. https://doi.org/10.3390/rs9080847
Leeper, R.D., Bell, J.E., Vines, C., & Palecki, M. (2017). An Evaluation of the North American Regional Reanalysis Simulated Soil Moisture Conditions during the 2011–13 Drought Period. Journal of Hydrometeorology, 18, 515-527. https://doi.org/10.1175/jhm-d-16-0132.1
Liangjing, Z. (2017). Terrestrial water storage from GRACE gravity data for hydrometeorological applications. -
Lievens, H., Martens, B., Verhoest, N.E.C., Hahn, S., Reichle, R.H., & Miralles, D.G. (2017). Assimilation of global radar backscatter and radiometer brightness temperature observations to improve soil moisture and land evaporation estimates. Remote Sensing of Environment, 189, 194-210. https://doi.org/10.1016/j.rse.2016.11.022
Lievens, H., Reichle, R.H., Liu, Q., De Lannoy, G.J.M., Dunbar, R.S., Kim, S.B., Das, N.N., Cosh, M., Walker, J.P., & Wagner, W. (2017). Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters, 44, 6145-6153. https://doi.org/10.1002/2017gl073904
Lin, X., Wen, J., Tang, Y., Ma, M., You, D., Dou, B., Wu, X., Zhu, X., Xiao, Q., & Liu, Q. (2017). A web-based land surface remote sensing products validation system (LAPVAS): application to albedo product. International Journal of Digital Earth, 11, 308-328. https://doi.org/10.1080/17538947.2017.1320593
Liu, Q., Hao, Y., Stebler, E., Tanaka, N., & Zou, C.B. (2017). Impact of plant functional types on coherence between precipitation and soil moisture - a wavelet analysis. Geophysical Research Letters.. https://doi.org/10.1002/2017gl075542
Liu, Z., Li, P., & Yang, J. (2017). Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal. Remote Sensing, 9. https://doi.org/10.3390/rs9111197
Mahecha, M.D., Gans, F., Sippel, S., Donges, J.F., Kaminski, T., Metzger, S., Migliavacca, M., Papale, D., Rammig, A., & Zscheischler, J. (2017). Detecting impacts of extreme events with ecological in-situ monitoring networks. Biogeosciences Discussions, 1-33. https://doi.org/10.5194/bg-2017-130
Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., & Verhoest, N.E.C. (2017). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10, 1903-1925. https://doi.org/10.5194/gmd-10-1903-2017
Martinez, G., Brocca, L., Gerke, H.H., & Pachepsky, Y.A. (2017). Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2017.07.0145
Massari, C., Su, C.-H., Brocca, L., Sang, Y.-F., Ciabatta, L., Ryu, D., & Wagner, W. (2017). Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques. Remote Sensing of Environment, 198, 17-29. https://doi.org/10.1016/j.rse.2017.05.037
McCabe, M.F., Rodell, M., Alsdorf, D.E., Miralles, D.G., Uijlenhoet, R., Wagner, W., Lucieer, A., Houborg, R., Verhoest, N.E.C., Franz, T.E., Shi, J., Gao, H., & Wood, E.F. (2017). The Future of Earth Observation in Hydrology. Hydrology and Earth System Sciences Discussions, 1-55. https://doi.org/10.5194/hess-2017-54
Miyaoka, K., Gruber, A., Ticconi, F., Hahn, S., Wagner, W., Figa-Saldana, J., & Anderson, C. (2017). Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2274-2284. https://doi.org/10.1109/jstars.2016.2632306
Mohanty, B.P., Cosh, M.H., Lakshmi, V., & Montzka, C. (2017). Soil Moisture Remote Sensing: State-of-the-Science. Vadose Zone Journal, 16. https://doi.org/10.2136/vzj2016.10.0105
Montzka, C., Bogena, H., Zreda, M., Monerris, A., Morrison, R., Muddu, S., & Vereecken, H. (2017). Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes. Remote Sensing, 9. https://doi.org/10.3390/rs9020103
Murguia-Flores, F., Arndt, S., Ganesan, A.L., Murray-Tortarolo, G.N., & Hornibrook, E.R.C. (2017). Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil. Geoscientific Model Development Discussions, 1-38. https://doi.org/10.5194/gmd-2017-124
Nguyen, H.H., Kim, H., & Choi, M. (2017). Evaluation of the soil water content using cosmic-ray neutron probe in a heterogeneous monsoon climate-dominated region. Advances in Water Resources, 108, 125-138. https://doi.org/10.1016/j.advwatres.2017.07.020
Nilawar, A., Calderella, C., Lakhankar, T., Waikar, M., & Munoz, J. (2017). Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA. Hydrology, 4. https://doi.org/10.3390/hydrology4040045
Pan, X., Kornelsen, K.C., & Coulibaly, P. (2017). Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks. JAWRA Journal of the American Water Resources Association, 53, 220-237. https://doi.org/10.1111/1752-1688.12491
Park, S., Im, J., Park, S., & Rhee, J. (2017). Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology, 237-238, 257-269. https://doi.org/10.1016/j.agrformet.2017.02.022
Park, S., Park, S., Im, J., Rhee, J., Shin, J., & Park, J. (2017). Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees. Water, 9. https://doi.org/10.3390/w9050332
Peng, J., Loew, A., Merlin, O., & Verhoest, N.E.C. (2017). A review of spatial downscaling of satellite remotely sensed soil moisture. Reviews of Geophysics, 55, 341-366. https://doi.org/10.1002/2016RG000543
Petropoulos, G.P., & McCalmont, J.P. (2017). An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network. Sensors (Basel), 17. https://doi.org/10.3390/s17071481
Phillips, T.J., Klein, S.A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I.N., Santanello, J.A., Cook, D.R., & Torn, M.S. (2017). Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2017jd027141
Pierdicca, N., Fascetti, F., Pulvirenti, L., & Crapolicchio, R. (2017). Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4522-4530. https://doi.org/10.1109/jstars.2017.2714025
Przeździecki, K., Zawadzki, J., Cieszewski, C., & Bettinger, P. (2017). Estimation of soil moisture across broad landscapes of Georgia and South Carolina using the triangle method applied to MODIS satellite imagery. Silva Fennica, 51. https://doi.org/10.14214/sf.1683
Rains, D., Han, X., Lievens, H., Montzka, C., & Verhoest, N.E.C. (2017). SMOS brightness temperature assimilation into the Community Land Model. Hydrology and Earth System Sciences, 21, 5929-5951. https://doi.org/10.5194/hess-21-5929-2017
Ran, Y., Li, X., Jin, R., Kang, J., & Cosh, M.H. (2017). Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape. Water Resources Research, 53, 283-301. https://doi.org/10.1002/2015wr018182
Ray, R., Fares, A., He, Y., & Temimi, M. (2017). Evaluation and Inter-Comparison of Satellite Soil Moisture Products Using In Situ Observations over Texas, U.S. Water, 9. https://doi.org/10.3390/w9060372
Reichle, R.H., Draper, C.S., Liu, Q., Girotto, M., Mahanama, S.P.P., Koster, R.D., & Lannoy, G.J.M.D. (2017). Assessment of MERRA-2 Land Surface Hydrology Estimates. Journal of Climate, 30, 2937-2960. https://doi.org/10.1175/jcli-d-16-0720.1
Rodríguez-Fernández, N.J., Muñoz Sabater, J., Richaume, P., de Rosnay, P., Kerr, Y.H., Albergel, C., Drusch, M., & Mecklenburg, S. (2017). SMOS near-real-time soil moisture product: processor overview and first validation results. Hydrology and Earth System Sciences, 21, 5201-5216. https://doi.org/10.5194/hess-21-5201-2017
Scholze, M., Buchwitz, M., Dorigo, W., Guanter, L., & Quegan, S. (2017). Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems. Biogeosciences Discussions, 1-49. https://doi.org/10.5194/bg-2016-557
Sun, G., Peng, F., & Mu, M. (2017). Uncertainty assessment and sensitivity analysis of soil moisture based on model parameter errors – Results from four regions in China. Journal of Hydrology, 555, 347-360. https://doi.org/10.1016/j.jhydrol.2017.09.059
Sun, Y., Huang, S., Ma, J., Li, J., Li, X., Wang, H., Chen, S., & Zang, W. (2017). Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product over China Using In Situ Data. Remote Sensing, 9. https://doi.org/10.3390/rs9030292
Tobin, K.J., Torres, R., Crow, W.T., & Bennett, M.E. (2017). Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS. Hydrology and Earth System Sciences, 21, 4403-4417. https://doi.org/10.5194/hess-21-4403-2017
van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al-Yaari, A., Parinussa, R.M., Mecklenburg, S., & Drusch, M. (2017). The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment, 189, 180-193. https://doi.org/10.1016/j.rse.2016.11.026
Varikoden, H., & Revadekar, J.V. (2017). Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon. Pure and Applied Geophysics, 175, 1187-1196. https://doi.org/10.1007/s00024-017-1740-6
Williamson, M., Adams, J.R., Berg, A.A., Derksen, C., Toose, P., & Walker, A. (2017). Plot-scale assessment of soil freeze/thaw detection and variability with impedance probes: implications for remote sensing validation networks. Hydrology Research. https://doi.org/10.2166/nh.2017.183
Xing, C., Chen, N., Zhang, X., & Gong, J. (2017). A Machine Learning Based Reconstruction Method for Satellite Remote Sensing of Soil Moisture Images with In Situ Observations. Remote Sensing, 9. https://doi.org/10.3390/rs9050484
Yao, P., Shi, J., Zhao, T., Lu, H., & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing, 9. https://doi.org/10.3390/rs9010035
Yuan, S., & Quiring, S.M. (2017). Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations. Hydrology and Earth System Sciences, 21, 2203-2218. https://doi.org/10.5194/hess-21-2203-2017
Zhang, X., Zhang, T., Zhou, P., Shao, Y., & Gao, S. (2017). Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements. Remote Sensing, 9. https://doi.org/10.3390/rs9020104
Zhao, W., Li, A., Jin, H., Zhang, Z., Bian, J., & Yin, G. (2017). Performance Evaluation of the Triangle-Based Empirical Soil Moisture Relationship Models Based on Landsat-5 TM Data and In Situ Measurements. IEEE Transactions on Geoscience and Remote Sensing, 55, 2632-2645. https://doi.org/10.1109/tgrs.2017.2649522
Zhao, W., Li, A., & Zhao, T. (2017). Potential of Estimating Surface Soil Moisture With the Triangle-Based Empirical Relationship Model. IEEE Transactions on Geoscience and Remote Sensing, 55, 6494-6504. https://doi.org/10.1109/tgrs.2017.2728815
Zhou, H., Chang, J., Sun, J., Shang, C., Han, F., & Hu, D. (2017). Spatial variation of temperature of surface soil layer adjacent to constructions: A theoretical framework for atmosphere-building-soil energy flow systems. Building and Environment, 124, 143-152. https://doi.org/10.1016/j.buildenv.2017.08.002
Al-Yaari, A., Wigneron, J. P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., … Ducharne, A. (2016). Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations. Remote Sensing of Environment, 180, 453–464. https://doi.org/10.1016/j.rse.2015.11.022
An, R., Zhang, L., Wang, Z., Quaye-Ballard, J. A., You, J., Shen, X., … Ke, Z. (2016). Validation of the ESA CCI soil moisture product in China. International Journal of Applied Earth Observation and Geoinformation, 48, 28–36. https://doi.org/10.1016/j.jag.2015.09.009
Bi, H., Ma, J., Zheng, W., & Zeng, J. (2016). Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau: EVALUATE GLDAS SOIL MOISTURE OVER TP. Journal of Geophysical Research: Atmospheres, 121, 6, 2658–2678. https://doi.org/10.1002/2015JD024131
Chen, X., Su, Y., Liao, J., Shang, J., Dong, T., Wang, C., … Liu, L. (2016). Detecting significant decreasing trends of land surface soil moisture in eastern China during the past three decades (1979-2010): China’s 32 Year Soil Moisture. Journal of Geophysical Research: Atmospheres, 121, 10, 5177–5192. https://doi.org/10.1002/2015JD024676
Cissé, S., Eymard, L., Ottlé, C., Ndione, J., Gaye, A., & Pinsard, F. (2016). Rainfall Intra-Seasonal Variability and Vegetation Growth in the Ferlo Basin (Senegal). Remote Sensing, 8, 1, 66. https://doi.org/10.3390/rs8010066
Du, J., Kimball, J. S., & Jones, L. A. (2016). Passive Microwave Remote Sensing of Soil Moisture Based on Dynamic Vegetation Scattering Properties for AMSR-E. IEEE Transactions on Geoscience and Remote Sensing, 54, 1, 597–608. https://doi.org/10.1109/TGRS.2015.2462758
Enenkel, M., Reimer, C., Dorigo, W., Wagner, W., Pfeil, I., Parinussa, R., & De Jeu, R. (2016). Combining satellite observations to develop a global soil moisture product for near-real-time applications. Hydrology and Earth System Sciences, 20, 10, 4191–4208. https://doi.org/10.5194/hess-20-4191-2016
Faridani, F., Farid, A., Ansari, H., & Manfreda, S. (2016). Estimation of the Root-Zone Soil Moisture Using Passive Microwave Remote Sensing and SMAR Model. Journal of Irrigation and Drainage Engineering, 4016070. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001115
Fascetti, F., Pierdicca, N., Crapolicchio, R., Pulvirenti, L., & Muoz-Sabater, J. (2016). An assessment of SMOS version 6.20 products through Triple and Quadruple Collocation techniques considering ASCAT, ERA/Interim LAND, ISMNand SMAP soil moisture data. IEEE, 91–94. https://doi.org/10.1109/MICRORAD.2016.7530511
Fascetti, F., Pierdicca, N., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2016). A comparison of ASCAT and SMOS soil moisture retrievals over Europe and Northern Africa from 2010 to 2013. International Journal of Applied Earth Observation and Geoinformation, 45, 135–142. https://doi.org/10.1016/j.jag.2015.09.008
Fernandez-Moran, R., Wigneron, J.-P., De Lannoy, G., Lopez-Baeza, E., Mialon, A., Mahmoodi, A., … Kerr, Y. (2016). Calibrating the effective scattering albedo in the SMOS algorithm: Some first results. IEEE, 826–829. https://doi.org/10.1109/IGARSS.2016.7729209
Gonzalez-Zamora, A., Sanchez, N., & Martinez-Fernandez, J. (2016). Validation of Aquarius Soil Moisture Products Over the Northwest of Spain: A Comparison With SMOS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 6, 2763–2769. https://doi.org/10.1109/JSTARS.2016.2517401
González-Zamora, Á., Sánchez, N., Martínez-Fernández, J., & Wagner, W. (2016). Root-zone plant available water estimation using the SMOS-derived soil water index. Advances in Water Resources, 96, 339–353. https://doi.org/10.1016/j.advwatres.2016.08.001
Griesfeller, A., Lahoz, W. A., Jeu, R. A. M. d., Dorigo, W., Haugen, L. E., Svendby, T. M., & Wagner, W. (2016). Evaluation of satellite soil moisture products over Norway using ground-based observations. International Journal of Applied Earth Observation and Geoinformation, 45, 155–164. https://doi.org/10.1016/j.jag.2015.04.016
Gruber, A., Su, C.-H., Crow, W. T., Zwieback, S., Dorigo, W. A., & Wagner, W. (2016). Estimating error cross-correlations in soil moisture data sets using extended collocation analysis: EXTENDED COLLOCATION ANALYSIS. Journal of Geophysical Research: Atmospheres, 121, 3, 1208–1219. https://doi.org/10.1002/2015JD024027
Han, M., Lu, H., & Yang, K. (2016). Development of passive microwave retrieval algorithm for estimation of surface soil temperature from AMSR-E data. IEEE, 1671–1674. https://doi.org/10.1109/IGARSS.2016.7729427
Kędzior, M., & Zawadzki, J. (2016). Comparative study of soil moisture estimations from SMOS satellite mission, GLDAS database, and cosmic-ray neutrons measurements at COSMOS station in Eastern Poland. Geoderma, 283, 21–31. https://doi.org/10.1016/j.geoderma.2016.07.023
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. https://doi.org/10.1016/j.rse.2016.02.042
Kerr, Y. H., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., … Wigneron, J.-P. (2016). Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation. Remote Sensing of Environment, 180, 40–63. http://doi.org/10.1016/j.rse.2016.02.042
Kim, S., Parinussa, R., Liu, Y., Johnson, F., & Sharma, A. (2016). Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach. Remote Sensing, 8, 6, 518. https://doi.org/10.3390/rs8060518
Koch, F., Schlenz, F., Prasch, M., Appel, F., Ruf, T., & Mauser, W. (2016). Soil Moisture Retrieval Based on GPS Signal Strength Attenuation. Water, 8, 7, 276. https://doi.org/10.3390/w8070276
Leng, P., Song, X., Duan, S.-B., & Li, Z.-L. (2016). Preliminary validation of two temporal parameter-based soil moisture retrieval models using a satellite product and in situ soil moisture measurements over the REMEDHUS network. International Journal of Remote Sensing, 37, 24, 5902–5917. https://doi.org/10.1080/01431161.2016.1253896
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Férnandez-Prieto, D., … Verhoest, N. E. C. (2016). GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development Discussions, 1–36. https://doi.org/10.5194/gmd-2016-162
Martens, B., Miralles, D., Lievens, H., Fernández-Prieto, D., & Verhoest, N. E. C. (2016). Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture. International Journal of Applied Earth Observation and Geoinformation, 48, 146–162. https://doi.org/10.1016/j.jag.2015.09.012
McNally, A., Shukla, S., Arsenault, K. R., Wang, S., Peters-Lidard, C. D., & Verdin, J. P. (2016). Evaluating ESA CCI soil moisture in East Africa. International Journal of Applied Earth Observation and Geoinformation, 48, 96–109. https://doi.org/10.1016/j.jag.2016.01.001
Nair, A., & Indu, J. (2016). Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture. Remote Sensing, 8, 12, 976. https://doi.org/10.3390/rs8120976
Orth, R., Dutra, E., & Pappenberger, F. (2016). Improving Weather Predictability by Including Land Surface Model Parameter Uncertainty. Monthly Weather Review, 144, 4, 1551–1569. https://doi.org/10.1175/MWR-D-15-0283.1
Pablos, M., Martínez-Fernández, J., Piles, M., Sánchez, N., Vall-llossera, M., & Camps, A. (2016). Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations. Remote Sensing, 8, 7, 587. https://doi.org/10.3390/rs8070587
Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30, 6, 1973–1986. https://doi.org/10.1007/s11269-016-1263-4
Pal, M., Maity, R., & Dey, S. (2016). Statistical Modelling of Vertical Soil Moisture Profile: Coupling of Memory and Forcing. Water Resources Management, 30, 6, 1973–1986. http://doi.org/10.1007/s11269-016-1263-4
Parinussa, R., de Jeu, R., van der Schalie, R., Crow, W., Lei, F., & Holmes, T. (2016). A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input. Climate, 4, 4, 50. https://doi.org/10.3390/cli4040050
Piles, M., Petropoulos, G. P., Sánchez, N., González-Zamora, Á., & Ireland, G. (2016). Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180, 403–417. https://doi.org/10.1016/j.rse.2016.02.048
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., … Pulliainen, J. (2016). SMOS prototype algorithm for detecting autumn soil freezing. Remote Sensing of Environment, 180, 346–360. https://doi.org/10.1016/j.rse.2016.01.012
Santi, E., Paloscia, S., Pettinato, S., Brocca, L., & Ciabatta, L. (2016). Robust Assessment of an Operational Algorithm for the Retrieval of Soil Moisture From AMSR-E Data in Central Italy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 6, 2478–2492. https://doi.org/10.1109/JSTARS.2016.2575361
Santi, E., Paloscia, S., Pettinato, S., & Fontanelli, G. (2016). Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation, 48, 61–73. https://doi.org/10.1016/j.jag.2015.08.002
Schalie, R. va. der, Kerr, Y. H., Wigneron, J. P., Rodríguez-Fernández, N. J., Al-Yaari, A., & Jeu, R. A. M. d. (2016). Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model. International Journal of Applied Earth Observation and Geoinformation, 45, 125–134. https://doi.org/10.1016/j.jag.2015.08.005
Scholze, M., Kaminski, T., Knorr, W., Blessing, S., Vossbeck, M., Grant, J. P., & Scipal, K. (2016). Simultaneous assimilation of SMOS soil moisture and atmospheric CO2 in-situ observations to constrain the global terrestrial carbon cycle. Remote Sensing of Environment, 180, 334–345. https://doi.org/10.1016/j.rse.2016.02.058
Shin, Y., Lim, K., Park, K., & Jung, Y. (2016). Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture. Water, 8, 8, 311. https://doi.org/10.3390/w8070311
Su, C.-H., Ryu, D., Dorigo, W., Zwieback, S., Gruber, A., Albergel, C., … Wagner, W. (2016). Homogeneity of a global multisatellite soil moisture climate data record: HOMOGENEITY OF SOIL MOISTURE CDR. Geophysical Research Letters.. https://doi.org/10.1002/2016GL070458
Wang, L., Li, X., Chen, Y., Yang, K., Chen, D., Zhou, J., … Huang, J. (2016). Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology, 218–219, 288–297. https://doi.org/10.1016/j.agrformet.2016.01.003
Wu, Q., Liu, H., Wang, L., & Deng, C. (2016). Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network. International Journal of Applied Earth Observation and Geoinformation, 45, 187–199. https://doi.org/10.1016/j.jag.2015.10.011
Zawadzki, J., & Kędzior, M. (2016). Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data. International Journal of Applied Earth Observation and Geoinformation, 45, 110–124. https://doi.org/10.1016/j.jag.2015.03.005
Zeng, J., Chen, K.-S., Bi, H., & Chen, Q. (2016). A Preliminary Evaluation of the SMAP Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements. IEEE Transactions on Geoscience and Remote Sensing, 54, 8, 4929–4940. https://doi.org/10.1109/TGRS.2016.2553085
Zhang, D., Madsen, H., Ridler, M. E., Kidmose, J., Jensen, K. H., & Refsgaard, J. C. (2016). Multivariate hydrological data assimilation of soil moisture and groundwater head. Hydrology and Earth System Sciences, 20, 10, 4341–4357. https://doi.org/10.5194/hess-20-4341-2016
Zhao, L., Yang, Z.-L., & Hoar, T. J. (2016). Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System. Journal of Hydrometeorology, 17, 9, 2431–2454. https://doi.org/10.1175/JHM-D-15-0218.1
Zwieback, S., Su, C.-H., Gruber, A., Dorigo, W. A., & Wagner, W. (2016). The Impact of Quadratic Nonlinear Relations between Soil Moisture Products on Uncertainty Estimates from Triple Collocation Analysis and Two Quadratic Extensions. Journal of Hydrometeorology, 17, 6, 1725–1743. https://doi.org/10.1175/JHM-D-15-0213.1
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., … Vitart, F. (2015). ERA-Interim/Land: a global land surface reanalysis data set. Hydrology and Earth System Sciences, 19, 1, 389–407. http://doi.org/10.5194/hess-19-389-2015
Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., & Albergel, C. (2015). Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction. Remote Sensing of Environment, 163, -8, 111–126. http://doi.org/10.1016/j.rse.2015.03.009
Brocca, L., Massari, C., Ciabatta, L., Moramarco, T., Penna, D., Zuecco, G., … Martínez-Fernández, J. (2015). Rainfall estimation from in situ soil moisture observations at several sites in Europe: an evaluation of the SM2RAIN algorithm. Journal of Hydrology and Hydromechanics, 63, 3. http://doi.org/10.1515/johh-2015-0016
Calvet, J.-C., Fritz, N., Berne, C., Piguet, B., Maurel, W., & Meurey, C. (2015). Impact of gravels and organic matter on the thermal properties of grassland soils in southern France. SOIL Discussions, 2, 1, 737–765. http://doi.org/10.5194/soild-2-737-2015
Cammalleri, C., Micale, F., & Vogt, J. (2015). On the value of combining different modelled soil moisture products for European drought monitoring. Journal of Hydrology, 525, 547–558. http://doi.org/10.1016/j.jhydrol.2015.04.021
Chappell, A., Weaver, J., Purohit, S., Smith, W., Schuchardt, K., West, P., … Fox, P. (2015). Enhancing the impact of science data toward data discovery and reuse. IEEE, 271–277. http://doi.org/10.1109/ICIS.2015.7166605
Coopersmith, E. J., Cosh, M. H., Bindlish, R., & Bell, J. (2015). Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN). Advances in Water Resources, 85, 79–85. http://doi.org/10.1016/j.advwatres.2015.09.003
Dorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., … Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162, 380–395. http://doi.org/10.1016/j.rse.2014.07.023
Fernandez-Moran, R., Wigneron, J.-P., Lopez-Baeza, E., Al-Yaari, A., Bircher, S., Coll-Pajaron, A., … Kerr, Y. (2015). Analyzing the impact of using the SRP (Simplified roughness parameterization) method on soil moisture retrieval over different regions of the globe . IEEE, 5182–5185. http://doi.org/10.1109/IGARSS.2015.7327001
Gonzalez-Zamora, A., Sanchez, N., Martinez-Fernandez, J., & Gumuzzio, A. (2015). Validation of SMOS and Aquarius soil moisture using two in situ networks in Spain. IEEE, 4738–4741. http://doi.org/10.1109/IGARSS.2015.7326888
Hottenstein, J. D., Ponce-Campos, G. E., Moguel-Yanes, J., & Moran, M. S. (2015). Impact of Varying Storm Intensity and Consecutive Dry Days on Grassland Soil Moisture. Journal of Hydrometeorology, 16, 1, 106–117. http://doi.org/10.1175/JHM-D-14-0057.1
Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., & Sharma, A. (2015). A global comparison of alternate AMSR2 soil moisture products: Why do they differ?. Remote Sensing of Environment, 161, 43–62. http://doi.org/10.1016/j.rse.2015.02.002
Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., & Sharma, A. (2015). A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation: IMPROVING AMSR2 SOIL MOISTURE RETRIEVALS. Geophysical Research Letters, 42, 16, 6662–6670. http://doi.org/10.1002/2015GL064981
Kornelsen, K. C., & Coulibaly, P. (2015). Reducing multiplicative bias of satellite soil moisture retrievals. Remote Sensing of Environment, 165, 109–122. doi.org/10.1016/j.rse.2015.04.031
Lee, J., & Im, J. (2015). A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS) Soil Moisture: Retrieval Ensembles. Remote Sensing, 7, 12, 16045–16061. http://doi.org/10.3390/rs71215824
Leng, P., Song, X., Li, Z.-L., Wang, Y., & Wang, R. (2015). Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area. Remote Sensing, 7, 4, 4112–4138. http://doi.org/10.3390/rs70404112
Nicolai-Shaw, N., Hirschi, M., Mittelbach, H., & Seneviratne, S. I. (2015). Spatial representativeness of soil moisture using in situ, remote sensing, and land reanalysis data: SPATIAL REPRESENTATIVENESS OF SOIL MOISTURE. Journal of Geophysical Research: Atmospheres, 120, 19, 9955–9964. http://doi.org/10.1002/2015JD023305
Parinussa, R. M., Holmes, T. R. H., Wanders, N., Dorigo, W. A., & de Jeu, R. A. M. (2015). A Preliminary Study toward Consistent Soil Moisture from AMSR2. Journal of Hydrometeorology, 16, 2, 932–947. http://doi.org/10.1175/JHM-D-13-0200.1
Pierdicca, N., Fascetti, F., Pulvirenti, L., Crapolicchio, R., & Munoz-Sabater, J. (2015). Quadruple Collocation Analysis for Soil Moisture Product Assessment. IEEE Geoscience and Remote Sensing Letters, 12, 8, 1595–1599. http://doi.org/10.1109/LGRS.2015.2414654
Pierdicca, N., Fascetti, F., Pulvirenti, L., Crapolicchio, R., & Muñoz-Sabater, J. (2015). Analysis of ASCAT, SMOS, in-situ and land model soil moisture as a regionalized variable over Europe and North Africa. Remote Sensing of Environment, 170, 280–289. http://doi.org/10.1016/j.rse.2015.09.005
Spennemann, P. C., Rivera, J. A., Saulo, A. C., & Penalba, O. C. (2015). A Comparison of GLDAS Soil Moisture Anomalies against Standardized Precipitation Index and Multisatellite Estimations over South America. Journal of Hydrometeorology, 16, 1, 158–171. http://doi.org/10.1175/JHM-D-13-0190.1
Su, C.-H., Narsey, S. Y., Gruber, A., Xaver, A., Chung, D., Ryu, D., & Wagner, W. (2015). Evaluation of post-retrieval de-noising of active and passive microwave satellite soil moisture. Remote Sensing of Environment, 163, 127–139. http://doi.org/10.1016/j.rse.2015.03.010
Zwieback, S., Paulik, C., & Wagner, W. (2015). Frozen Soil Detection Based on Advanced Scatterometer Observations and Air Temperature Data as Part of Soil Moisture Retrieval. Remote Sensing, 7, 3, 3206–3231. http://doi.org/10.3390/rs70303206
Angevine, W. M., Bazile, E., Legain, D., and Pino, D. (2014). Land surface spinup for episodic modeling. Atmos. Chem. Phys., 14, 8165-8172. doi:10.5194/acp-14-8165-2014
Albergel, C., Dorigo, W.,Balsamo, G., Muñoz-Sabater, J., de Rosnay, P., Isaksen, L., Brocca, L., de Jeu,R., Wagner, W. (2013). Monitoring multi-decadal satellite earth observation of soil moisture products through land surface reanalyses. Remote Sensing of Environment, 138, 77-89. doi: 10.1016/j.rse.2013.07.009
Albergel, C., Dorigo, W., Reichle, R.H.,Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., Isaksen, L., de Jeu, R., Wagner,W. (2013). Skill and global trend analysis ofsoil moisture from reanalyses and microwave remote sensing. Journal of Hydrometeorology, 14, 1259-1277. doi:10.1175/JHM-D-12-0161.1
Albergel, C., De Rosnay, P., Balsamo, G., Isaksen, L., & Muñoz-Sabater, J. (2012). Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations. Journal of Hydrometeorology, 13, 1442-1460
Albergel, C., de Rosnay, P., Gruhier, C., Muñoz-Sabater, J., Hasenauer, S., Isaksen, L., Kerr, Y., & Wagner, W. (2012). Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations. Remote Sensing of Environment, 118, 215-226
Albergel, C.., G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, and S. Boussetta, (2012). A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data. Hydrol. Earth Syst. Sci., 16, 3607-3620
Collow, T.W., Robock, A., Basara, J.B., Illston, B.G. (2012). Evaluation of SMOS retrievals of soil moisture over the central United States with currently available in situ observations. Journal of Geophysical Research D: Atmospheres, 117, 9
dall''Amico, J.T.; Schlenz, F.; Loew, A.; Mauser, W. (2012). First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1507-1516
Liu, Y. Y., Dorigo, W. A., Parinussa, R. M., De Jeu, R. A. M., Wagner, W., McCabe, M. F., . . . Van Dijk, A. I. J. M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sensing of Environment, 123, 280-297
Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., . . . Zhou, X. H. (2012). A framework for benchmarking land models. Biogeosciences, 9, 10, 3857-3874
Mecklenburg, S., Drusch, M., Kerr, Y. H., Font, J., Martin-Neira, M., Delwart, S., . . . Crapolicchio, R. (2012). ESA's soil moisture and ocean salinity mission: Mission performance and operations. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1354-1366
Pan, M., Sahoo, A.K., Wood, E.F., Al Bitar, A., Leroux, D., Kerr, Y.H. (2012). An initial assessment of SMOS derived soil moisture over the continental United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 6211458, 1448-1457
Parrens, M., Zakharova, E., Lafont, S., Calvet, J.-C., Kerr, Y., Wagner, W., and Wigneron, J.-P. (2012). Comparing soil moisture retrievals from SMOS and ASCAT over France. Hydrol. Earth Syst. Sci., 16, 423-440. doi:10.5194/hess-16-423-2012
Schlenz, F.; dall''Amico, J.T.; Loew, A.; Mauser, W. (2012). Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment. IEEE Transactions on Geoscience and Remote Sensing, 50, 5, 1517-1529
Skierucha, W., Wilczek, A., & Szypłowska, A. (2012). Dielectric spectroscopy in agrophysics. International Agrophysics, 26, 2, 187-197
Van doninck, J., Peters, J., Lievens, H., De Baets, B., and Verhoest, N. E. C. (2012). Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series. Hydrol. Earth Syst. Sci., 16, 773-786
Wanders, N., Karssenberg, D., Bierkens, M., Parinussa, R., de Jeu, R., van Dam, J., & de Jong, S. (2012). Observation uncertainty of satellite soil moisture products determined with physically-based modeling. Remote Sensing of Environment, 127, 341-356
Brocca L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P., Latron, J., Martin, C., Bittelli, M. (2011). Soil Moisture estimation through ASCAT ans AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sensing of Environment, 115, 3390-3408
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436. doi:10.5194/hess-15-425-2011