{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:57:50Z","timestamp":1776711470077,"version":"3.51.2"},"reference-count":55,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000},"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 and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available\/total nitrogen content, and pH at 0\u201310 cm and 10\u201320 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.<\/jats:p>","DOI":"10.3390\/rs13224716","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T02:34:05Z","timestamp":1638326045000},"page":"4716","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2635-0688","authenticated-orcid":false,"given":"Wanxue","family":"Zhu","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Crop Sciences, University of G\u00f6ttingen, 37075 G\u00f6ttingen, Germany"},{"name":"Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 M\u00fcncheberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2603-8034","authenticated-orcid":false,"given":"Ehsan Eyshi","family":"Rezaei","sequence":"additional","affiliation":[{"name":"Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 M\u00fcncheberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7424-5030","authenticated-orcid":false,"given":"Hamideh","family":"Nouri","sequence":"additional","affiliation":[{"name":"Department of Crop Sciences, University of G\u00f6ttingen, 37075 G\u00f6ttingen, Germany"}]},{"given":"Ting","family":"Yang","sequence":"additional","affiliation":[{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Institute of Geographic Sciences and Natural Resources Research(CAS), University of Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Binbin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6436-2479","authenticated-orcid":false,"given":"Huarui","family":"Gong","sequence":"additional","affiliation":[{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Institute of Geographic Sciences and Natural Resources Research(CAS), University of Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yun","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Grassland Science, College of Grassland Science and Technology, China Agricultural University, Beijing 100193, China"}]},{"given":"Jinbang","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhigang","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Institute of Geographic Sciences and Natural Resources Research(CAS), University of Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106738","DOI":"10.1016\/j.agwat.2021.106738","article-title":"Effects of water quality, irrigation amount and nitrogen applied on soil salinity and cotton production under mulched drip irrigation in arid Northwest China","volume":"247","author":"Che","year":"2021","journal-title":"Agric. 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