Spatial distribution dependency of soil organic carbon content to important environmental variables

Soil organic carbon (SOC) has a vital role in the physical, chemical and biological behavior of the soil, and therefore prediction of the amount and mapping spatial distribution of SOC is necessary for sustainable soil management. However, the relationship between SOC and remotely sensed and easily accessible variables have been rarely reported. The main objective of the present study is, therefore, to estimate SOC using the remote sensing of satellite images as well as some field variables for the Shazand Watershed, Iran. Towards that, 140 soil samples were taken from the top 30-cm of the soil from homogeneous units representing an area >1 km² to measure SOC. The potential relationship between SOC and some remote sensing-based indices including Enhanced Vegetation Index (EVI), Difference vegetation index (DVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI), Coloration Index (CI), Normalized Difference Moisture Index (NDMI), and some topographical and soil texture factors viz. elevation, slope, aspect, Topographical Wetness Index (TWI), clay and silt content of soil were assessed for the Shazand Watershed. In this regard, the relationships between SOC and environmental variables were analyzed using Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), and Random Forest (RF) analyses. The results showed that none of the models indicated a good predictive performance due to low R². However, CI was found as a better predictor (R² = 0.258 for GWR and R² = 0.040 for OLS; p-value <0.05) for SOC in comparison with other indices. Accordingly, it was concluded that the remote sensing-based and environmental indices did not have enough potential for estimating SOC to generate managerial plans for the study watershed. The low SOC content (0.63 ± 0.29%) and low variation across the Shazand Watershed were found as determinant factors on the low fitness of the study relationships. Further studies are therefore needed to find out reliable predictive models for SOC for the conditions under considerations.