{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T09:54:33Z","timestamp":1784714073605,"version":"3.55.0"},"reference-count":113,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T00:00:00Z","timestamp":1618617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41961059"],"award-info":[{"award-number":["41961059"]}]},{"name":"National Natural Science Foundation of China","award":["41771470"],"award-info":[{"award-number":["41771470"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0\u201310 cm) were collected from farmland (2.5 \u00d7 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.<\/jats:p>","DOI":"10.3390\/rs13081562","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T06:35:53Z","timestamp":1618814153000},"page":"1562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7855-6525","authenticated-orcid":false,"given":"Xiangyu","family":"Ge","sequence":"first","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianli","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-6247","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8332-7997","authenticated-orcid":false,"given":"Jingzhe","family":"Wang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Key Laboratory of Urban Informatics, Shenzhen University, Shenzhen 518060, China"},{"name":"Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyue","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41586-021-03325-5","article-title":"Soil moisture\u2014Atmosphere feedback dominates land carbon uptake variability","volume":"592","author":"Humphrey","year":"2021","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4892","DOI":"10.1038\/s41467-020-18631-1","article-title":"Soil moisture dominates dryness stress on ecosystem production globally","volume":"11","author":"Liu","year":"2020","journal-title":"Nat. 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