A novel hyperspectral remote sensing estimation model for surface soil texture using AHSI/ZY1-02D satellite image

Abstract

Soil texture is an essential attribute of soil structure, which plays an important role in evaluating soil fertility and carrying out agricultural production. This study developed a novel soil texture estimation model using ZiYuan-1-02D (ZY1-02D) satellite Advanced Hyperspectral Imager (AHSI), based on the mechanism of soil spectral mixing, that enables simultaneous estimation of the three soil texture attributes (clay, silt, and sand). Study area is located in the north-eastern region of China covering 1683.31 km². To reduce data redundancy, we used correlation analysis and Competitive Adaptive Reweighted Sampling (CARS) algorithms to select sensitive spectral features of soil texture, and excluded spectral bands that are strongly influenced by other soil physicochemical properties. Finally, the spatial distribution map and classification map of soil texture have been generated for the study area. We also used AHSI/GaoFen-5 (GF-5) satellite images to further validate the generalizability of the model. The results suggest that the model can be used in the estimation of soil texture, and the developed novel model can effectively reflect the spatial distribution characteristics of surface soil texture attributes. The R² values of all outcomes for inverting three texture attributes were larger than 0.5, with silt exhibiting the best estimation effect (R² = 0.79, RMSE = 6.46 %, RPD = 2.19). The Max-divergence between the estimated surface soil texture attributes based on the two satellite images (AHSI/ZY1-02D and AHSI/GF-5) and the measured data were less than 4 %. The novel spectral mixture model of soil texture is suitable for spaceborne remote sensing data and has broad application prospects in surface soil texture mapping.