Soil salinity inversion based on differentiated fusion of satellite image and ground spectra

Abstract

Due to the wide availability of remote sensing data from different sensors and platforms, soil salinity inversion based on the fusion of multisource remote sensing data is becoming a reality. However, existing fusion methods mainly use the average relationship from samples, which does not consider the differences in the relationships among samples. In this paper, a differentiated fusion method for determining satellite and ground spectral variables of soil salinity according to the differences among samples is proposed to increase the regional inversion precision. Nonnegative matrix factorization was employed to decompose soil salinity spectral variables from the Sentinel-2A Multi-Spectral Instrument (MSI) image and the simulated spectra on ground spectra. Then, the base spectra matrix of soil salinity was from the simulated data, and the weight coefficient matrix was obtained from the Sentinel-2A MSI data as the differentiated correction coefficients. By multiplying the base matrix and weight matrix, the spectral variables were reconstructed. The results indicate that this differentiated fusion method can not only enhance the correlation between soil salinity and Sentinel-2A MSI data but also improve the precision of regional soil salinity inversion models. For the differentiated fused model, the validation R2, RMSE, and RPD were 0.71, 7.02 g/kg, and 1.49, respectively; compared with the unfused model, the validation R2 increased by 0.09, the RMSE decreased by 0.80 g/kg, and the RPD increased by 0.18. Furthermore, the differentiated fused model performed better than the average-ratio adjusted model. These findings have practical implications for the use of multisource optical remote sensing data for regional soil salinity mapping and analysis.

Keywords

• Remote sensing
• Hyperspectra
• Data fusion
• Soil salinity
• Sentinel-2A MSI