Regional mapping of soil organic matter and particle size in Northeast China using hyperspectral satellite images

Abstract

Soil organic matter (SOM) and particle size are key indicators for evaluating cultivated soil quality. Conventional soil quality surveys based on field sampling are resource-intensive and can only obtain the data at sampling points, making it difficult to meet the needs of plot-level cultivated land management. In recent years, a series of hyperspectral satellite sensors have provided an important data source for the estimation of cultivated soil parameters. In this study, we took all 1.26 million km2 of cultivated land (including paddy fields, dry land, and irrigated fields) in Northeast China as the study area. In April 2021, we conducted a synchronized sample collection experiment utilizing ZY1-02D satellite hyperspectral data, gathering a total of 171 soil samples. More than 1,400 hyperspectral images of satellites including GF5, ZY1-02D and ZY1-02E covering the entire study area were collected and preprocessed. Firstly, we developed a bare soil identification method by combining cultivated soil spectral library, spatial-spectral filtering, and spectral angle mapping. The average accuracy of bare soil identification results varies from 90% to 95%. Secondly, we analyzed the correlation between soil parameters and dual-band spectral indices using multi-platform observed data, as well as the radiation quality of massive satellite images. Combining the results of spectral band radiation quality analysis, the optimal spectral indices of SOM, sand, silt, and clay were constructed based on the collaborative observation of multi-source data. Then, we developed a soil parameter prediction model that combines topography and spectral information. In this research, a new feature selection technique called VIP-CARS-Frog based on multi-index evaluation, which combines three algorithms including variable importance plots, competitive self-organizing selection, and random frog, was proposed to select high-quality and stable features. These technologies have been successfully applied to hyperspectral satellite data such as GF5, ZY1-02D, and ZY1-02E to map the spatial distribution of SOM and particle size in cultivated land in Northeast China. The inversion results of SOM, sand, silt, and clay have R2 of 0.84, 0.9, 0.79 and 0.76, RMSE of 5.16g/kg, 7.16%, 7.25% and 4.7%, and RPD of 2.32, 2.87, 1.72, and 1.73, respectively. From the results, it can be seen that in Songnen Plain and Sanjiang Plain, where black soil and chernozem are concentrated, the SOM content is higher and the sand content is lower. On the contrary, in the southwestern region, the sand content is higher and the SOM content is lower. The results indicate that hyperspectral satellite images can be used to estimate SOM and particle size content at a regional scale, showing its great potential in cultivated land quality surveys and agricultural precision management.