Estimation of Soil Salinity Under Various Soil Moisture Conditions Using Laboratory Based Thermal Infrared Spectra

Soil salinization is a world-wide phenomenon that threatens ecological environment and agricultural production. Modeling soil salt content (SSC) is a big challenge because of its huge spatiotemporal variation and the interference of soil water content (SWC) and soil salt types. Prior studies showed more interest in the use of hyperspectral reflectance, while few studies focused on thermal infrared band domain. In this study, we arranged samples with three salt types and several levels of SWC and measured the soil emissivity for each sample at each level of SWC. We employed both original and derivate emissivity to figure out the relationship between SSC and soil thermal infrared spectra, then used partial least squares regression to estimate SSC. Finally, the optimal model was determined with the evaluation criteria, RPD (ratio of performance to deviation) for predictive ability and AICc (corrected Akaike Information Criterion) for simplicity. The models were applied to estimate SSC and coefficient of determination (R 2 ) of 0.67, 0.71, 0.69 and 0.7, and root mean relative error of 4.03, 3.78, 3.92, 3.86 (g/100 g) was obtained, respectively, for NaCl, Na 2 SO 4 , Na 2 CO 3 and all salt types. The study provided a comparison result of three salt types for soil salinity estimation and a criterion for modeling effectively and succinctly and should have potential applications in the future.