{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:40:12Z","timestamp":1774942812818,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T00:00:00Z","timestamp":1597104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Nature Science Foundation Program of China","award":["41401232"],"award-info":[{"award-number":["41401232"]}]},{"name":"the Opening Foundation of Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs , P.R.China","award":["2016002"],"award-info":[{"award-number":["2016002"]}]},{"name":"financially supported by self-determined research funds of CCNU from the colleges\u2019 basic research and operation of MOE","award":["CCNU18TS002"],"award-info":[{"award-number":["CCNU18TS002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0\u201310, 10\u201320, and 20\u201330 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0\u201310 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI\u2019s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.<\/jats:p>","DOI":"10.3390\/rs12162587","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T10:56:12Z","timestamp":1597143372000},"page":"2587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Yan","family":"Nie","sequence":"first","affiliation":[{"name":"Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430062, China"}]},{"given":"Ying","family":"Tan","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430062, China"}]},{"given":"Yuqin","family":"Deng","sequence":"additional","affiliation":[{"name":"Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430062, China"}]},{"given":"Jing","family":"Yu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.1100217","article-title":"Regions of Strong Coupling between Soil Moisture and Precipitation","volume":"305","author":"Koster","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jin, H., Zhu, Q., Zhao, X., and Zhang, Y. 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