{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:41:02Z","timestamp":1776102062449,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T00:00:00Z","timestamp":1612656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018ZDPY07"],"award-info":[{"award-number":["2018ZDPY07"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results (R\u00af) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum R\u00af of 0.82 \u00b1 0.007 for Criterion 2 and of 0.75 \u00b1 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 \u00d7 104 km2 and 667.44 \u00d7 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.<\/jats:p>","DOI":"10.3390\/rs13040589","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2163-4550","authenticated-orcid":false,"given":"Lina","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7763-1108","authenticated-orcid":false,"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"},{"name":"Department of Geography &amp; Earth System Science, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium"}]},{"given":"Ting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"given":"Longqian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"given":"Jianlin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Geology Engineering and Geomatics, Chang\u2019an University, Yanta Road 120, Xi\u2019an 710054, China"}]},{"given":"Weiqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Environment and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5644-2102","authenticated-orcid":false,"given":"Liang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"},{"name":"Henry Fok College of Biology and Agriculture, Shaoguan University, Daxue Road 26, Shaoguan 512005, China"}]},{"given":"Sai","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Humanities and Law, Jiangsu Ocean University, Cangwu Road 59, Lianyungang 222005, China"}]},{"given":"Longhua","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Research and Development, Shanghai Gongjing Environmental Protection Co., Ltd., Yuanjiang Road 525, Shanghai 201100, China"}]},{"given":"Mingxin","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2017.10.016","article-title":"Information Theoretic Evaluation of Satellite Soil Moisture Retrievals","volume":"204","author":"Kumar","year":"2018","journal-title":"Remote Sens. 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