Estimating the spatial distribution of soil organic carbon and soil total nitrogen concentrations by remote sensing methods: a case study in a forest-dominated Alpine Watershed, Western China

In ecologically fragile areas, accurate estimation of soil organic carbon (SOC) and soil nitrogen (STN) concentrations is a prerequisite for sustainable development. On the basis of field sampling data and remote sensing technology, this study divided the topsoil (0-30 cm) into three soil layers of 0-10 cm, 10-20 cm, and 20-30 cm to carry out SOC and STN concentrations estimation experiments in the Qilian Mountains in western China. To estimate SOC and STN concentrations, a stepwise multiple linear regression model was used. A total of 119 topsoil samples and nine remotely sensed environmental variables were collected and used for model development and validation. The results show that the stepwise multiple linear regression model has stable simulation performance. The modified soil-adjusted vegetation index (MSAVI), perpendicular vegetation index (PVI), aspect, elevation, and solar radiation were the key environmental variables affecting soil organic carbon and total nitrogen content. The ranking of SOC concentrations in the 0-30 cm soil layer was bush woods > spruce forests > sabina forests > alpine meadows > steppes, with mean concentrations of 117.98 g/kg, 101.35 g/kg, 83.09 g/kg, 78.76 g/kg, and 37.08 g/kg, respectively. The ranking of STN concentrations in the 0-30 cm soil layer was bush woods > alpine meadows >spruce forests > sabina forests > steppes, with mean concentrations of 4.77 g/kg, 3.71 g/kg, 3.19 g/kg, 3.27 g/kg, and 1.92 g/kg, respectively. Both SOC and STN concentrations decreased significantly with increasing soil depth.