Authors: Yue Pan*,
Topics: Agricultural Geography, Soils, Remote Sensing
Keywords: Soil organic matter; Remote sensing image; Topography factor; Subregion
Session Type: Paper
Presentation File: No File Uploaded
To improve the accuracy of remote sensing inversion modeling of soil organic matter (SOM) at the field scale, this study selected a 41.3-hectare field in the black soil region of northeastern China as the study area and analyzed the relationships among the spectral indices, topographic factors and SOM. Spatial differences in the soil physical and chemical properties, reflectance spectra and topographic factors in the study area were analyzed. Multiscale segmentation and hierarchical clustering were also conducted based on measured SOM samples, bare-soil Sentinel-2A images and 4-m-resolution digital elevation model (DEM) data. Then, SOM prediction models were constructed for the entire study area and for the study area subregions. The results led to the following conclusions: 1) SOM presents significant spatial heterogeneity in the study area; 2) the accuracy of the established SOM prediction model for the entire study area is low (R2 = 0.16, RMSEcal = 1.61, and RMSEval = 1.45); and 3) the entire study area can be divided into four subregions, i.e., the "sedimentary area," "deposition-buffer area," "erosion-buffer area" and "erosion area." The SOM prediction model was established for each subregion according to the SOM content and spectral and topographic characteristics of each subregion. The SOM prediction accuracy for the entire study area clearly improved (R2 = 0.58, RMSEcal = 1.17, and RMSEval = 1.30). In this study, prediction models employing the subregion method were established and represent a new approach for SOM inversion research in the future.