Inversion of soil pH during the dry and wet seasons in the Yinbei region of Ningxia, China, based on multi-source remote sensing data

Abstract

Dynamic monitoring of soil pH changes across different seasons is crucial to understanding soil alkalization and preventing land degradation in arid regions. Here, we explored the potential use of ground-measured hyperspectral data and Landsat 8 OLI remote-sensing images in the inversion of soil pH during the dry and wet seasons. The measured and image data of spectral reflectance and corresponding soil pH data obtained in the laboratory were used to analyze the spatiotemporal patterns of soil pH in the Yinbei region, Ningxia, China. First, the hyperspectral data were resampled to match the range of the image bands; then 11 spectral indices were calculated based on the two sets of spectral data. Principal component analysis (PCA), stepwise regression (SR), and gray relational analysis (GRA) were used to select feature variables (sensitive bands and spectral indices) from the spectral data. The back propagation neural network (BPNN), support vector machine (SVM), ridge regression (RR), and geographically weighted regression (GWR) were used to construct soil pH inversion models. A total of 24 models established using the different methods were compared in terms of the determination coefficients of the calibration set (Rc2) and prediction set (Rp2), root mean square error (RMSE), and relative percent deviation (RPD). The results revealed that the mean soil pH in the study area was higher during the dry season (9.28) than the wet season (9.11). The resampled hyperspectral data correlated well with the image data under different levels of soil alkalization (R2 > 0.9652). Of the different models examined, the global regression models (BPNN, SVM, and RR) were superior to the local regression model (GWR), with better inversion results obtained in the wet than the dry season. The BPNN and SVM models performed better than the RR model, and the PCA-SVM model based on measured data in the dry season achieved the best overall performance (Rp2 = 0.9724 and RPD = 5.76). These findings provide valuable information on the distribution of Solonetzs land in the study area, facilitating management of soil alkalization and degradation in the Yinbei region.