Retrieval of Soil Organic Matter Contents in Black Soil Region Based on Hyperspectral Remote Sensing and Machine Learning Techniques

Objective The aims were to explore the predictive ability of spaceborne hyperspectral images for soil organic matter (SOM) in black soil areas, the SOM inversion model was developed based hyperspectral and machine learning techniques.

Method This study selected Youyi County, a typical black soil area in Northeast China, as the research area. Based on the high-resolution remote sensing data of the ZY-1 02D satellite, nine different spectral preprocessing methods were combined and three models were constructed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR), to estimate SOM content in Youyi County.

Result The predictive performance of two nonlinear machine learning models (i.e., RF and SVM) for SOM in the study area was significantly higher than that of the linear model of PLSR. Among them, the RF model with SG spectral preprocessing technique had the highest accuracy (R² = 0.52, RMSE = 7.25 g kg⁻¹), and estimated the SOM content in Youyi County well.

Conclusion The overall trend of SOM changes in the area showed a spatial distribution pattern of high values in the northeast and low values in the southwest. This study provides not only relevant method support for the spatial distribution of SOM in black soil areas, but also scientific basis and reliable ideas for other studies with hyperspectral data for predicting soil attribute content.