Removal of environmental influences for estimating soil texture fractions based on ZY1 satellite hyperspectral images

Soil texture is one of the important factors affecting the physical and chemical properties of soil, and is generally divided into sand, silt, and clay. Understanding its spatial distribution is crucial for agricultural and environmental decision-making. Estimation of soil texture fractions (STFs) can be achieved through visible, nearinfrared, and shortwave infrared (VNIR-SWIR) spectra, making hyperspectral imaging technology particularly promising for low-cost and large-scale monitoring of STFs. However, the process of image acquisition is inevitably affected by soil environmental factors. In this study, a soil texture fractions estimation method for hyperspectral images was proposed based on Orthogonal Signal Correction (OSC). Firstly, the OSC algorithm was applied to eliminate the impact of soil environmental factors on the image spectra. Then, the spectral feature was enhanced by Fractional Order Derivative (FOD). Subsequently, a Partial Least Squares Regression (PLSR) model was established based on the optimal bands selected by Competitive Adaptive Reweighted Sampling (CARS). A total of 107 soil samples were obtained from the agricultural area in Lishu County, Jilin Province, China. The hyperspectral images of the ZY1-02D and ZY1-02E satellites were acquired simultaneously in this area. The results showed that the OSC algorithm effectively reduced the influence of soil environmental factors on image spectra, and significantly improved the accuracy of STFs estimation. After applying OSC to the original image spectra, the estimation accuracies (R2) for sand, silt, and clay content were improved from 0.52, 0.51, 0.50 to 0.81, 0.65, and 0.70 respectively. Compared to External Parameter Orthogonalization (EPO) correction, the model accuracy and stability were higher with OSC correction. The selected results from CARS indicated that the characteristic spectral bands of soil texture are concentrated around 500 nm, 750 nm, 900 nm, and 2200 nm. Further analysis indicates that the distribution trends of the soil texture fractions in this study were consistent with previous studies, demonstrating the good applicability of the estimation method.