{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T04:58:20Z","timestamp":1775710700734,"version":"3.50.1"},"reference-count":167,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"USDA National Institute for Food and Agriculture","doi-asserted-by":"publisher","award":["1000065-COL00797"],"award-info":[{"award-number":["1000065-COL00797"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"USDA National Institute for Food and Agriculture","doi-asserted-by":"publisher","award":["2312319"],"award-info":[{"award-number":["2312319"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1000065-COL00797"],"award-info":[{"award-number":["1000065-COL00797"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2312319"],"award-info":[{"award-number":["2312319"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study explores machine learning for estimating soil moisture at multiple depths (0\u20135 cm, 0\u201310 cm, 0\u201320 cm, 0\u201350 cm, and 0\u2013100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0\u20135 cm depth to 0.86 at 0\u2013100 cm depth), lower root mean squared errors (from 0.024 cm3\/cm3 at 0\u2013100 cm depth to 0.039 cm3\/cm3 at 0\u20135 cm depth), higher Nash\u2013Sutcliffe Efficiencies (from 0.551 at 0\u20135 cm depth to 0.694 at 0\u2013100 cm depth), and higher Kling\u2013Gupta Efficiencies (0.511 at 0\u20135 cm depth to 0.696 at 0\u2013100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP.<\/jats:p>","DOI":"10.3390\/rs16193699","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T10:20:52Z","timestamp":1728037252000},"page":"3699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Shukran A.","family":"Sahaar","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Colorado State University, Campus Delivery 1372, Fort Collins, CO 80523-1372, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2988-0879","authenticated-orcid":false,"given":"Jeffrey D.","family":"Niemann","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Colorado State University, Campus Delivery 1372, Fort Collins, CO 80523-1372, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2016.02.064","article-title":"Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of the SMOS Derived Soil Water Deficit Index","volume":"177","author":"Gumuzzio","year":"2016","journal-title":"Remote Sens. 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