Applicability of three remote sensing based soil moisture variables for mapping soil organic matter in areas with different vegetation densities

Abstract

Obtaining accurate spatial information on soil organic matter (SOM) is crucial for understanding global carbon cycle. Digital soil mapping (DSM) has become an effective method for mapping SOM, in which selection of influential environmental covariates plays an important role. Soil moisture (SM) can serve as a potential covariate, especially it can be estimated at large spatial scales thanks to remote sensing. The normalized shortwave-infrared difference bare soil moisture indices (NSDSIs) based on Landsat SWIR bands generated at bare soil period has been employed in SOM mapping previously. However, soil is usually covered by vegetation, it is thus necessary to develop new SM indices applicable to areas covered with vegetation, and examine how SM indices perform in areas with different vegetation densities. In this paper, we developed a new SM index by introducing NSDSIs to the Optical TRApezoid Model (OPTRAM-NSDSI), and compared it with the original OPTRAM with the shortwave infrared transformed reflectance (OPTRAM-STR), as well as NSDSIs. SM indices were generated across two study areas, i.e. Zhuxi, Fujian (104 samples and 43.93 km² with forestland and farmland as main land uses) and Heshan, Heilongjiang (106 samples and 60 km² with primarily farmland) in China. The Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation approach was utilized as the SOM prediction model. The results suggest that adding SM variables into the commonly-used environmental covariates improves the prediction accuracies. The highest accuracy improvement of 26.8% in terms of Lin’s concordance correlation coefficient in Zhuxi is obtained by NSDSIs, and the highest improvement of 56.7% in Heshan is obtained by OPTRAM-NSDSI. This may indicate that OPTRAM-NSDSI is more effective in areas with higher vegetation densities while NSDSIs in areas with lower densities. Furthermore, the optimal image dates for SM estimation are probably at the vegetation “green-up” stage. This study provides a reference for using SM information to improve SOM mapping in areas covered with vegetation.