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accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0\u201320 cm and 20\u201340 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0\u201320 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0\u201320 cm is higher than that of 20\u201340 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.<\/jats:p>","DOI":"10.3390\/rs15215254","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T13:24:53Z","timestamp":1699277093000},"page":"5254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiawei","family":"Cui","sequence":"first","affiliation":[{"name":"College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"},{"name":"Tianjin Water Planning Survey and Design Company Limited, Tianjin 300204, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"},{"name":"College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"},{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3804-4071","authenticated-orcid":false,"given":"Xin","family":"Cui","sequence":"additional","affiliation":[{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"},{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weitong","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4388-0821","authenticated-orcid":false,"given":"Guang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Water-Saving Agriculture in Arid Regions of China, Northwest A&F University, Yangling 712100, China"},{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"ref_1","first-page":"250","article-title":"Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia","volume":"17","author":"Asfaw","year":"2016","journal-title":"J. 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