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In this study, we proposed a multi-depth soil salinity prediction model (0\u201310 cm, 10\u201320 cm, 20\u201340 cm, and 40\u201360 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets.<\/jats:p>","DOI":"10.3390\/rs15245640","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T03:48:41Z","timestamp":1701834521000},"page":"5640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Mapping Multi-Depth Soil Salinity Using Remote Sensing-Enabled Machine Learning in the Yellow River Delta, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3619-9572","authenticated-orcid":false,"given":"Haoran","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China"}]},{"given":"Xin","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China"},{"name":"Zhongke Shandong Dongying Institute of Geographic Sciences, Dongying 257509, China"}]},{"given":"Yanna","family":"Zhang","sequence":"additional","affiliation":[{"name":"Rural Economic Management Service Station of Shandong Province, Jinan 250013, China"}]},{"given":"Zhaishuo","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5004-9609","authenticated-orcid":false,"given":"Hengcai","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhenghe","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Water Conservancy and Environment, University of Jinan, Jinan 250022, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.pce.2010.12.004","article-title":"Characterizing soil salinity in irrigated agriculture using a remote sensing approach","volume":"55\u201357","author":"Abbas","year":"2013","journal-title":"Phys. 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