Surface Soil Moisture Determination of Irrigated and Drained Agricultural Lands with the OPTRAM Method and Sentinel-2 Observations

Surface soil moisture (SSM) is one of the factors affecting plant growth. Methods involving direct soil moisture measurement in the field or requiring laboratory tests are commonly used. These methods, however, are laborious and time-consuming and often give only point-by-point results. In contrast, SSM can vary across a field due to uneven precipitation, soil variability, etc. An alternative is using satellite data, for example, optical data from Sentinel-2 (S2). The main objective of this study was to assess the accuracy of SSM determination based on S2 data versus standard measurement techniques in three different agricultural areas (with irrigation and drainage systems). In the field, we measured SSM manually using non-destructive techniques. Based on S2 data, we estimated SSM using the optical trapezoid model (OPTRAM) and calculated eighteen vegetation indices. Using the OPTRAM model gave a high SSM estimating accuracy (R-2 = 0.67, RMSE = 0.06). The use of soil porosity in the OPTRAM model significantly improved the results. Among the vegetation indices, at the NDVI <= 0.2, the highest value of R-2 was obtained for the STR to OPTRAM index, while at the NDVI > 0.2, the shadow index had the highest R-2 comparable with OPTRAM.

Automated summary

The study aims to assess the accuracy of determining surface soil moisture (SSM) using Sentinel-2 data compared to standard measurement techniques. The results show that the use of the optical trapezoid model (OPTRAM) yields high SSM estimation accuracy, and incorporating soil porosity in the OPTRAM model significantly improves the results.