Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

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 (<;0.15g 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.