{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T08:13:46Z","timestamp":1781165626837,"version":"3.54.1"},"reference-count":78,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T00:00:00Z","timestamp":1608076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFE0107000"],"award-info":[{"award-number":["2018YFE0107000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young and Middle-aged Innovative Talents Program of Xinjiang production and Construction Crops","award":["2020CB032"],"award-info":[{"award-number":["2020CB032"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.<\/jats:p>","DOI":"10.3390\/rs12244118","type":"journal-article","created":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T22:12:06Z","timestamp":1608156726000},"page":"4118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China"],"prefix":"10.3390","volume":"12","author":[{"given":"Nan","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-5594","authenticated-orcid":false,"given":"Jie","family":"Xue","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Plant Science, Tarim University, Alar 843300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0801-3546","authenticated-orcid":false,"given":"Asim","family":"Biswas","sequence":"additional","affiliation":[{"name":"School of Environmental Sciences, University of Guelph, Guelph, ON N1G2W1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-1757","authenticated-orcid":false,"given":"Yong","family":"He","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3914-5402","authenticated-orcid":false,"given":"Zhou","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"},{"name":"Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.geoderma.2019.06.040","article-title":"Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China","volume":"353","author":"Wang","year":"2019","journal-title":"Geoderma"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113935","DOI":"10.1016\/j.geoderma.2019.113935","article-title":"Analyzing spatiotemporal characteristics of soil salinity in arid irrigated agro-ecosystems using integrated approaches","volume":"356","author":"Ren","year":"2019","journal-title":"Geoderma"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.ecolind.2016.11.043","article-title":"Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey","volume":"74","author":"Gorji","year":"2017","journal-title":"Ecol. 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