Abstract
Using remote sensing images to predict the spatial distribution of SOM content is a classic digital soil mapping problem. The purpose of this study is to compare the SOM mapping performance of Landsat 8 OLI, Landsat 7 ETM +, Sentinel-2 MSI and MODIS. We take the dry land in the black soil area of Northeast China as the study area and 791 soil samples were collected in the field to directly measure the SOM content, which serves as the evaluation dataset. We used four images to synthesize soil images for May over multi-year periods. May was identified as the best time of year to image the bare soil in Northeast China. All images used for the median synthesis consisted of cloud masked datasets acquired in May. Then, the image band, spectral index were used as inputs, and the SOM mapping results derived from the different satellite images was evaluated using the random forest regression algorithm. In addition, we use simulation methods to evaluate the impact of temporal, spatial, and spectral differences on SOM mapping. The results show that (1) Landsat-8 is currently the best choice for SOM mapping, Sentinel-2 images have the most potential for future SOM mapping, Landsat-7 and MODIS images can provide support for determining the historical spatial distribution of SOM content; (2) improving the temporal, spatial, and spectral resolution of images can improve the accuracy of SOM prediction, and temporal resolution is the most important because higher temporal resolution makes the selection of image pixels more conducive to accurate SOM prediction, thereby reducing the impact of the field environment; and (3) there is little difference in the spatial distribution trends and average values identified from the SOM products obtained from the four satellites, and can be generalized in some application scenarios.
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