{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:32:24Z","timestamp":1773081144851,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite retrieval and land surface models have become the mainstream methods for monitoring soil moisture (SM) over large regions; however, the uncertainty and coarse spatial resolution of these products limit their applications at the regional and local scales. We proposed a hybrid approach combining the triple collocation (TC) and the long short-term memory (LSTM) network, which was designed to generate a high-quality SM dataset from satellite and modeled data. We applied the proposed approach to merge SM data from Soil Moisture Active Passive (SMAP), Global Land Data Assimilation System-Noah (GLDAS-Noah), and the land component of the fifth generation of European Reanalysis (ERA5-Land), and we then downscaled the merged SM data from 0.36\u00b0 to 0.01\u00b0 resolution based on the relationship between the SM data and auxiliary environmental variables (elevation, land surface temperature, vegetation index, surface albedo, and soil texture). The merged and downscaled SM results were validated against in situ observations. The results showed that: (1) the TC-based validation results were consistent with the in situ-based validation, indicating that the TC method was reasonable for the comparison and evaluation of satellite and modeled SM data. (2) TC-based merging was superior to simple arithmetic average merging when the parent products had large differences. (3) Downscaled SM of the TC-based merged product had better performance than that of the parent products in terms of ubRMSE and bias values, implying that the fusion of satellite and model-based SM data would result in better downscaling accuracy. (4) Downscaled SM of TC-based merged data not only improved the representation of the SM spatial variability but also had satisfactory accuracy with a median of R (0.7244), ubRMSE (0.0459 m3\/m3), and bias (\u22120.0126 m3\/m3). The proposed approach was effective for generating a SM dataset with fine resolution and reliable accuracy for wide hydrometeorological applications.<\/jats:p>","DOI":"10.3390\/rs14071744","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T11:26:07Z","timestamp":1649157967000},"page":"1744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Hybrid Triple Collocation-Deep Learning Approach for Improving Soil Moisture Estimation from Satellite and Model-Based Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Wenting","family":"Ming","sequence":"first","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650504, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5186-594X","authenticated-orcid":false,"given":"Xuan","family":"Ji","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming 650504, China"}]},{"given":"Mingda","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yunnan Climate Center, Yunnan Meteorological Bureau, Kunming 650034, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3194-3621","authenticated-orcid":false,"given":"Yungang","family":"Li","sequence":"additional","affiliation":[{"name":"Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming 650504, China"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650504, China"}]},{"given":"Yinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650504, China"}]},{"given":"Jiqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650504, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1029\/2018RG000618","article-title":"Ground, Proximal, and Satellite Remote Sensing of Soil Moisture","volume":"57","author":"Babaeian","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1029\/2020GL091459","article-title":"Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique","volume":"48","author":"Song","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","first-page":"25","article-title":"Global Flash Drought Monitoring Using Surface Soil Moisture","volume":"57","author":"Sehgal","year":"2021","journal-title":"Water Resour. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.5194\/hess-14-1881-2010","article-title":"Improving runoff prediction through the assimilation of the ASCAT soil moisture product","volume":"14","author":"Brocca","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s43016-020-0028-7","article-title":"Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields","volume":"1","author":"Rigden","year":"2020","journal-title":"Nat. Food"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6495","DOI":"10.1029\/2018GL078131","article-title":"Soil Moisture Stress as a Major Driver of Carbon Cycle Uncertainty","volume":"45","author":"Trugman","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jhydrol.2012.06.021","article-title":"A review of the methods available for estimating soil moisture and its implications for water resource management","volume":"458","author":"Dobriyal","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1029\/2011RG000372","article-title":"Upscaling Sparse Ground-Based Soil Moisture Observations for the Validation of Coarse-Resolution Satellite Soil Moisture Products","volume":"50","author":"Crow","year":"2012","journal-title":"Rev. Geophys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.measurement.2014.04.007","article-title":"A critical review of soil moisture measurement","volume":"54","author":"Lekshmi","year":"2014","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.jhydrol.2018.06.081","article-title":"A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression","volume":"563","author":"Zhao","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1127\/0941-2948\/2013\/0399","article-title":"The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications","volume":"22","author":"Wagner","year":"2013","journal-title":"Meteorol. Z."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","article-title":"The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle","volume":"98","author":"Kerr","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1016\/j.rse.2017.08.025","article-title":"Development and assessment of the SMAP enhanced passive soil moisture product","volume":"204","author":"Chan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chew, C., and Small, E. (2020). Description of the UCAR\/CU Soil Moisture Product. Remote Sens., 12.","DOI":"10.3390\/rs12101558"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.rse.2020.112225","article-title":"Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method","volume":"255","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2017.10.026","article-title":"Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products","volume":"204","author":"Kim","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Amer. Meteorol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.5194\/essd-13-4349-2021","article-title":"ERA5-Land: A state-of-the-art global reanalysis dataset for land applications","volume":"13","author":"Dutra","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2021.112610","article-title":"Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain","volume":"264","author":"Peng","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s11269-020-02743-w","article-title":"Evaluation of Soil Moisture Climatology and Anomaly Components Derived From ERA5-Land and GLDAS-2.1 in China","volume":"35","author":"Wu","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6780","DOI":"10.1109\/TGRS.2017.2734070","article-title":"Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals","volume":"55","author":"Gruber","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1029\/2011WR011682","article-title":"An objective methodology for merging satellite- and model-based soil moisture products","volume":"48","author":"Yilmaz","year":"2012","journal-title":"Water Resour. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"425","DOI":"10.5194\/hess-15-425-2011","article-title":"Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals","volume":"15","author":"Liu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jhydrol.2020.124993","article-title":"A two-step fusion framework for quality improvement of a remotely sensed soil moisture product: A case study for the ECV product over the Tibetan Plateau","volume":"587","author":"Cui","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_25","first-page":"1835","article-title":"Blending Noah, SMOS, and in Situ Soil Moisture Using Multiple Weighting and Sampling Schemes","volume":"22","author":"Zhang","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2021.112509","article-title":"A triple collocation-based 2D soil moisture merging methodology considering spatial and temporal non-stationary errors","volume":"263","author":"Zhou","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Toward the true near-surface wind speed: Error modeling and calibration using triple collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res.-Oceans"},{"key":"ref_28","first-page":"200","article-title":"Recent advances in (soil moisture) triple collocation analysis","volume":"45","author":"Gruber","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wu, X.T., Lu, G.H., Wu, Z.Y., He, H., Scanlon, T., and Dorigo, W. (2020). Triple Collocation-Based Assessment of Satellite Soil Moisture Products with In Situ Measurements in China: Understanding the Error Sources. Remote Sens., 12.","DOI":"10.3390\/rs12142275"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2020.112248","article-title":"In-situ and triple-collocation based evaluations of eight global root zone soil moisture products","volume":"254","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1029\/2019EA000841","article-title":"Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques","volume":"7","author":"Mousa","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1109\/TGRS.2010.2089526","article-title":"An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval","volume":"49","author":"Das","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2013.05.0089","article-title":"Passive Microwave Soil Moisture Downscaling Using Vegetation Index and Skin Surface Temperature","volume":"12","author":"Fang","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1109\/TGRS.2011.2161318","article-title":"Improving Spatial Soil Moisture Representation Through Integration of AMSR-E and MODIS Products","volume":"50","author":"Kim","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TGRS.2011.2120615","article-title":"Downscaling SMOS-Derived Soil Moisture Using MODIS Visible\/Infrared Data","volume":"49","author":"Piles","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abbaszadeh, P., and Moradkhani, H. (2019, January 9\u201313). Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning. Proceedings of the Agu Fall Meeting, San Francisco, CA, USA.","DOI":"10.1029\/2018WR023354"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.advwatres.2020.103601","article-title":"Generating high-resolution daily soil moisture by using spatial downscaling techniques: A comparison of six machine learning algorithms","volume":"141","author":"Liu","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.rse.2019.111364","article-title":"Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution","volume":"233","author":"Long","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lv, A.F., Zhang, Z.L., and Zhu, H.C. (2021). A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province. Remote Sens., 13.","DOI":"10.3390\/rs13081583"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2362","DOI":"10.1109\/TGRS.2017.2778420","article-title":"Downscaling AMSR-2 Soil Moisture Data With Geographically Weighted Area-to-Area Regression Kriging","volume":"56","author":"Jin","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1109\/TGRS.2011.2175000","article-title":"Disaggregation of SMOS Soil Moisture in Southeastern Australia","volume":"50","author":"Merlin","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.advwatres.2012.08.007","article-title":"Assimilation and downscaling of satellite observed soil moisture over the Little River Experimental Watershed in Georgia, USA","volume":"52","author":"Sahoo","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1109\/TGRS.2019.2944421","article-title":"Spatially Explicit Model for Statistical Downscaling of Satellite Passive Microwave Soil Moisture","volume":"58","author":"Xu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed soil moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_45","first-page":"8","article-title":"Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications","volume":"28","author":"Chatterjee","year":"2020","journal-title":"Sust. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"ElSaadani, M., Habib, E., Abdelhameed, A.M., and Bayoumi, M. (2021). Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations. Front. Artif. Intell., 4.","DOI":"10.3389\/frai.2021.636234"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.agwat.2020.106649","article-title":"A hybrid CNN-GRU model for predicting soil moisture in maize root zone","volume":"245","author":"Yu","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1002\/joc.3977","article-title":"Variability of extreme precipitation over Yunnan Province, China 1960-2012","volume":"35","author":"Li","year":"2015","journal-title":"Int. J. Climatol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1080\/19475705.2019.1683082","article-title":"Performance evaluation of the CHIRPS precipitation dataset and its utility in drought monitoring over Yunnan Province, China","volume":"10","author":"Wu","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/s00704-019-02859-z","article-title":"Drought variability at various timescales over Yunnan Province, China: 1961-2015","volume":"138","author":"Li","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jhydrol.2020.125083","article-title":"Long-term changes in surface soil moisture based on CCI SM in Yunnan Province, Southwestern China","volume":"588","author":"Ma","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_54","unstructured":"Jackson, T.J., O\u2019Neill, P., Njoku, E., Chan, S., Bindlish, R., Colliander, A., Chen, F., Burgin, M., Dunbar, S., and Piepmeier, J. (2016). Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L2\/3_SM_P Version 3 Data Products, Jet Propulsion Laboratory. (SMAP Project), JPL D-93720."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(00)00205-4","article-title":"Narrowband to broadband conversions of land surface albedo I Algorithms","volume":"76","author":"Liang","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"249","DOI":"10.14358\/PERS.72.3.249","article-title":"A global assessment of the SRTM performance","volume":"72","author":"Rodriguez","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.agsy.2015.07.003","article-title":"Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications","volume":"139","author":"Jones","year":"2015","journal-title":"Agric. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.05.008","article-title":"Global-scale evaluation of SMAP, SMOS and ASCAT soil moisture products using triple collocation","volume":"214","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"6229","DOI":"10.1002\/2014GL061322","article-title":"Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target","volume":"41","author":"McColl","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_62","unstructured":"Gauss, C. (1963). Theory of the Motion of the Heavenly Bodies Moving about the Sun in Conic Sections, Dover."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1088\/1741-2552\/abf473","article-title":"Edge deep learning for neural implants: A case study of seizure detection and prediction","volume":"18","author":"Liu","year":"2021","journal-title":"J. Neural Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.scitotenv.2021.146948","article-title":"Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model","volume":"783","author":"Wu","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.5194\/hess-25-3555-2021","article-title":"Using Long Short-Term Memory networks to connect water table depth anomalies to precipitation anomalies over Europe","volume":"25","author":"Ma","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Bai, P., Liu, X., and Xie, J. (2021). Simulating runoff under changing climatic conditions: A comparison of the long short-term memory network with two conceptual hydrologic models. J. Hydrol., 592.","DOI":"10.1016\/j.jhydrol.2020.125779"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1007\/s11269-018-1989-2","article-title":"Evaluation of Multiple Satellite-Based Soil Moisture Products over Continental US Based on In Situ Measurements","volume":"32","author":"Jing","year":"2018","journal-title":"Water Resour. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1029\/2003JD003823","article-title":"The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system","volume":"109","author":"Mitchell","year":"2004","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2019.05.006","article-title":"Evaluation analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States","volume":"229","author":"Tavakol","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.agrformet.2020.108275","article-title":"Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches","volume":"297","author":"Cao","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"9899","DOI":"10.1109\/JSTARS.2021.3112623","article-title":"Spatial Gap-Filling of SMAP Soil Moisture Pixels Over Tibetan Plateau via Machine Learning Versus Geostatistics","volume":"14","author":"Tong","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1175\/JHM-D-13-0158.1","article-title":"Evaluation of Assumptions in Soil Moisture Triple Collocation Analysis","volume":"15","author":"Yilmaz","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2020.112052","article-title":"Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated\/dryland agriculture regions","volume":"251","author":"Kim","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"717","DOI":"10.5194\/essd-11-717-2019","article-title":"Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology","volume":"11","author":"Gruber","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2015.10.028","article-title":"Triple collocation: Beyond three estimates and separation of structural\/non-structural errors","volume":"171","author":"Pan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"He, X., Xu, T., Xia, Y., Bateni, S.M., Guo, Z., Liu, S., Mao, K., Zhang, Y., Feng, H., and Zhao, J. (2020). A Bayesian Three-Cornered Hat (BTCH) Method: Improving the Terrestrial Evapotranspiration Estimation. Remote Sens., 12.","DOI":"10.3390\/rs12050878"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5194\/hess-15-75-2011","article-title":"A dynamic approach for evaluating coarse scale satellite soil moisture products","volume":"15","author":"Loew","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wu, K., Ryu, D., Nie, L., and Shu, H. (2021). Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis. Remote Sens. Environ., 256.","DOI":"10.1016\/j.rse.2021.112324"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.agrformet.2017.02.022","article-title":"Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula","volume":"237","author":"Park","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"26933","DOI":"10.1109\/ACCESS.2020.2971348","article-title":"A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","article-title":"Rainfall\u2013runoff modelling using Long Short-Term Memory (LSTM) networks","volume":"22","author":"Kratzert","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Wang, F., Chen, Y.N., Li, Z., Fang, G.H., Li, Y.P., Wang, X.X., Zhang, X.Q., and Kayumba, P.M. (2021). Developing a Long Short-Term Memory (LSTM)-Based Model for Reconstructing Terrestrial Water Storage Variations from 1982 to 2016 in the Tarim River Basin, Northwest China. Remote Sens., 13.","DOI":"10.3390\/rs13050889"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.scitotenv.2020.142638","article-title":"Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model","volume":"755","author":"Dikshit","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"11030","DOI":"10.1002\/2017GL075619","article-title":"Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental US Using a Deep Learning Neural Network","volume":"44","author":"Fang","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4051","DOI":"10.1109\/JSTARS.2021.3069774","article-title":"Downscaling SMAP Soil Moisture Products With Convolutional Neural Network","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.5194\/essd-13-1385-2021","article-title":"Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013-2019","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_88","unstructured":"Shi, X.J., Chen, Z.R., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015, January 7\u201312). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jhydrol.2021.126698","article-title":"Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning","volume":"600","author":"Li","year":"2021","journal-title":"J. Hydrol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:31Z","timestamp":1760136511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":89,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071744"],"URL":"https:\/\/doi.org\/10.3390\/rs14071744","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,5]]}}}