Prediction of soil salt content based on the random forest algorithm
Received date: 2023-01-06
Revised date: 2023-06-12
Online published: 2023-08-24
Soil salinization caused by natural and anthropogenic factors is an environmental hazard that is especially important in arid and semi-arid regions of the world. The accumulation of salts in soil is a major threat to crop production and global agriculture. Therefore, the rapid and precise detection of salt-affected lands is highly critical for sustaining soil productivity. This paper aims to analyze the performance of the random forest algorithm in mapping soil salinity in the Yinchuan Plain using Landsat-8 OLI, Sentinel-2A satellite images, and ground-based soil salt content (SSC) measurements with the aid of the Google Earth Engine (GEE) platform. We estimated SSC by establishing the relationship between spectral index characteristics and ground-measured soil salt content. The results show that GEE can provide reliable data support for soil salinity prediction. The random forest model established with Sentinel-2A as the data source performed better (R2 = 0.789, RMSE = 1.487) than and can therefore be used for the estimation of soil salinity using high-resolution remote sensing, which can provide theoretical support for large-scale soil salinity monitoring.
Key words： soil salt content; Google Earth Engine; random forest; predict; Yinchuan Plain
Xiaoyu LI , Keli JIA , Huimin WEI , Ruihua CHEN , Yijing WANG . Prediction of soil salt content based on the random forest algorithm[J]. Arid Zone Research, 2023 , 40(8) : 1258 -1267 . DOI: 10.13866/j.azr.2023.08.06
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