Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon

Soil organic carbon (SOC) is a reliable indicator of soil productivity and land management. Despite the widespread use of remote sensing data in estimating SOC variation, the predictive power of single-date and multi-temporal as well as the individual and combined effects of radar and optical images have rarely been investigated. This study created six two-month interval composite layers including VH and VV polarizations of Sentinel-1 (S1), spectral bands of Sentinel-2 (S2), normalized difference vegetation index (NDVI), slope and altitude for the target year of 2019-2020 to estimate the SOC variation. Based on 80 soil samples (0-20 cm) taken in a field survey from northern Iran, random forest (RF) and support vector regression (SVR) were calibrated to estimate the SOC content. All models were fine-tuned using grid search and evaluated using spatial cross-validation. The results showed that multi-temporal data were more predictive than single-date data, and SVR outperformed the RF algorithm. SVR model using the multi-temporal S2 achieved the highest R-2 and RMSE values of 57.59% and 0.94%, respectively. Feature selection has shown no benefit of adding S1 data and identified band-2 (green), band-3 (red), and band-10 (shortwave infrared) of S2 imagery as the most explanatory variables of SOC variation. The results of this study highlight the potential of freely available high-resolution S2 for mapping SOC variation using the SVR model. SVR is particularly suggested in small sample size studies, an issue which is dominant in soil studies.

Automated summary

This study utilized remote sensing data to estimate the variation of soil organic carbon (SOC). The results showed that multi-temporal data was more predictive than single-date data, and support vector regression (SVR) outperformed the random forest (RF) algorithm. The study also identified the most important variables for explaining SOC variation, which were band-2, band-3, and band-10 of Sentinel-2 imagery.