Spatial modeling of soil organic carbon using remotely sensed indices and environmental field inventory variables

The relationship between soil organic carbon (SOC) and environmental parameters was investigated in the Galazchai Watershed, Iran. Therefore, correlating the SOC amounts with remote sensing (RS) indices, topographic variables, and soil texture was analyzed. Some 125 soil samples gather from the upper 30 cm, and the weight of each sample was about 0.5 kg. The RS indices, consisting of difference vegetation index (DVI), enhanced vegetation index (EVI), optimized soil adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI), and soil adjusted vegetation index (SAVI), were used. Topographic variables included slope, elevation, aspect, and topographical wetness index (TWI), as well as clay and silt contents. The ordinary least square (OLS) and the geographically weighted regression (GWR) were employed to develop the SOC relationship considering different combinations of the variables. Results showed that none of the combinations of variables accurately estimated SOC (R-2 < 0.32 and p value > 0.001). However, EVI with GWR (R-2 = 0.291) and OSAVI, clay, slope, and aspect with GWR (R-2= 0.32) better estimated SOC. Therefore, results showed that the study remotely sensed indices and environmental field inventory variables could not favorably predict the SOC content. These results can be attributed to the low SOC values varying from 0.917 to 3.355%, with a mean of 2.194 +/- 0.522 in the study watershed. However, studies using more uniformly distributed and denser sampling in the study area and other methods to investigate the relationship between variables are recommended.

Automated summary

The relationship between soil organic carbon (SOC) and environmental parameters was investigated in the Galazchai Watershed, Iran. The study analyzed the correlation between SOC amounts and remote sensing indices, topographic variables, and soil texture. The results showed that none of the combinations of variables accurately estimated SOC, but specific remote sensing indices and geographically weighted regression methods performed better. Future studies should consider more uniform and denser sampling and explore alternative methods to investigate the relationship between variables.