Abstract: To explore the ability of different sensors to estimate soil Na⁺ content, we got the mea-sured soil spectra and Sentinel-2B image spectra of the typical soil samples from the northern area of Ningxia. We filtered the sensitive parameters from the spectra data by means of stepwise regression (SR) and principal component regression analysis (PCA). We established the models to estimate soil Na⁺ content based on the measured spectra and image data using partial least square regression (PLSR), support vector machine (SVM) and back propagation neural network model (BPNN). The results showed that, except for Band9, there was significant correlation between the resampling data and the image data. The estimation accuracy of models based on SR-screening was generally higher than the PCA (excluding SVM model). The PCA-SVM model was the best image estimation model for soil Na⁺ content, with a prediction accuracy of 0.792. The SR-BPNN model was the best measured estimation model, with a prediction accuracy of 0.908. The estimating accuracy of the SR-PLSR image-spectra-based model increased from 0.481 to 0.798 after calibrated by the resampled measured spectrum model, which effectively enhanced the accuracy in estimating the soil Na⁺ content at large scale. We successfully made the spatial transformation of soil Na⁺ content from point to surface. Our results provided a scientific reference for Sentinel-2B image to monitor Na⁺ content in salinized soil.

Key words: hyperspectral, sentinel-2B image, soil Na⁺ content, model, calibration