Predicting Soil Salt Content Over Partially Vegetated Surfaces Using Non-Negative Matrix Factorization

Remote sensing has been widely applied to map soil salinity in the last few decades. However, a notable decrease in the accuracy of soil salt content (SSC) predictions occurred when the soil surfaces were partially vegetated. To minimize the influence of partial vegetation cover on spectral reflectance, we applied a spectral separation method, non-negative matrix factorization (NMF), to extract soil spectral information from a controlled field experiment with three varying factors [vegetation coverage, soil moisture content (SMC), and SSC]. The method was applied without prior knowledge of, or restrictions on the mixed and source spectra. Soil samples and spectral reflectance collected on three periods were used to determine the effectiveness of NMF-extracted soil spectra with partial least squares regression (PLSR). The results indicated that SSC can be predicted by bare soil spectra. NMF effectively separated soil spectra from the observed spectra, and the SSC was successfully predicted from the extracted soil spectra within a wide range of vegetation cover (0%–64.7%) within defined moisture levels (
${<}0.15\;\hbox{g g}^{-1}$
by weight). The approach proposed in this study will improve the prediction accuracy of SSC for partially vegetated surfaces and will expand the application of remote sensing.