Regional mapping of soil organic matter content using multitemporal synthetic Landsat 8 images in Google Earth Engine

Abstract

Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development, especially in fertile black soil areas. The present study proposed a regional-scale high spatial resolution (30 m) SOM mapping method based on multitemporal synthetic images. The study area is located on the Songnen Plain of Northeast China. First, all available Landsat 8 surface reflectance (SR) data during the bare soil period (April and May) from 2014 to 2019 in the study area were screened in the Google Earth Engine (GEE), and the cloud mask was constructed. The median, average, maximum, and minimum values of the image set were synthesized according to single-year multimonth, multiyear single-month and multiyear multimonth time ranges, and the spectral index of the synthesized image was constructed. Second, the bands and spectral indices of different synthetic images were used as input to establish a random forest (RF) model of SOM prediction, and the accuracies of different spatial prediction models of SOM were compared to evaluate the optimal regional remote sensing prediction model of SOM. The following results were show. 1) The use of the spectral index combined with the image band as input had a greater improvement in the accuracy of SOM prediction than the use of only the image band. 2) Compared to the average, maximum and minimum synthesized images, the median synthesized image had higher accuracy in SOM prediction. 3) More years of synthesized images provided more robust SOM prediction results. 4) May was the best time window for SOM mapping on the Songnen Plain. This study presents a large-scale and high spatial resolution SOM mapping method that is suitable for black soil areas in Northeast China and extends the application of GEE in digital soil mapping.