Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Soil Science submitted time 2023-08-26 Cooperative journals: 《干旱区研究》
Abstract： Soil salinization caused by natural and anthropogenic factors is an environmental hazard that isespecially important in arid and semi-arid regions of the world. The accumulation of salts in soil is a major threatto crop production and global agriculture. Therefore, the rapid and precise detection of salt- affected lands ishighly critical for sustaining soil productivity. This paper aims to analyze the performance of the random forestalgorithm in mapping soil salinity in the Yinchuan Plain using Landsat-8 OLI, Sentinel-2A satellite images, andground-based soil salt content (SSC) measurements with the aid of the Google Earth Engine (GEE) platform. Weestimated SSC by establishing the relationship between spectral index characteristics and ground-measured soilsalt content. The results show that GEE can provide reliable data support for soil salinity prediction. The randomforest model established with Sentinel-2A as the data source performed better (R2 = 0.789, RMSE = 1.487) thanand can therefore be used for the estimation of soil salinity using high- resolution remote sensing, which canprovide theoretical support for large-scale soil salinity monitoring.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Soil Science submitted time 2023-02-27 Cooperative journals: 《干旱区地理》
Abstract： Soil salinization can hinder agricultural development. In this study, the degree of regional soil salinization was obtained to provide a theoretical reference for improving agricultural land quality. Using Yinchuan Plain of China as the study area with a grid size of 5 km×5 km, the soil salinity data of 166 sampling points at different depths were obtained. Combined with the Landsat 8 OLI image corresponding to the sampling time, the salt influence factor and salt index were used as input parameters, respectively, and soil salinity at field sampling points was used as output layer parameters. Support vector machine, back propagation neural network, and Bayesian neural network (BNN) were established as soil salinity inversion models. The determination coefficient and root mean square error of the different models were compared to screen the best model. Finally, soil salinization inversion at different depths was performed in the study area. The following results were obtained: (1) In the 0-20 cm soil salinity inversion model, the BNN model based on the influence factor variable group of salinization was the best, with a coefficient of determination (R2 ) and root mean square error (RMSE) of 0.618 and 2.986, respectively; the best inversion result of 20-40 cm soil salinity was the BNN model based on the salt index variable group (R2 =0.651; RMSE=1.947); the comparative analysis of the modeling and verification effects of different variables of the selected algorithms revealed that the BNN model was the best inversion model with a better fitting degree than the other two models, and the introduction of a neural network had certain advantages in the model construction. (2) Non- salinized and mildly salinized soils were the main soil types in Yinchuan Plain. Soil salinization showed a low trend in the south and a high trend in the north. The 20-40 cm soil salinization was found to be lighter than the 0-20 cm soil salinization.
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