Assessing spatial variations in soil organic carbon and C:N ratio in Northeast China's black soil region: Insights from Landsat-9 satellite and crop growth information

Abstract

Monitoring soil properties, especially soil organic carbon (SOC) and Carbon-to-Nitrogen (C:N) ratios, is vital for understanding degradation and developing black soil conservation strategies. Most soil mapping research has leveraged satellite imagery from limited bare-soil periods. The predictive accuracy using time-series images capturing crop growth is underexplored. Moreover, while the new Landsat-9 satellite surpasses Landsat-8, its use in soil mapping is mostly uncharted. In this study, we compared the performance of single-date bare soil imagery (Landsat-8, Landsat-9, and Sentinel-2) and multi-temporal images (Landsat-8 and Landsat-9 combined, and Sentinel-2) using three machine learning techniques (Boosted Regression Tree, BRT; Random Forest, RF; Extreme Gradient Boosting, XGBoost) for mapping SOC and C:N ratio in a typical Northeast China black soil cropland region. The results revealed that single-date Landsat-9 exhibited great potential for soil mapping with the optimal XGBoost model, improving the prediction accuracy (in terms of R2) of SOC and C:N ratio by 15.01% and 30.07%, respectively, compared to Landsat-8, while also delivering performance slightly inferior to single-date Sentinel-2. Moreover, predictors derived from multi-temporal images significantly outperformed those derived from single-date images. The XGBoost models that utilized multi-temporal Sentinel-2 predictors achieved the highest prediction accuracy for both SOC (R2 = 0.676, RMSE = 1.928 g/kg, MAE = 1.580 g/kg, RPD = 1.535) and C:N ratio (R2 = 0.713, RMSE = 0.585, MAE = 0.484, RPD = 1.718). Interestingly, the combination of Landsat-8 and Landsat-9 demonstrated similar prediction accuracy but lower uncertainties in SOC and C:N ratio mapping compared to multi-temporal Sentinel-2 images. In addition, a comparison of the contemporary prediction soil maps with historical soil data revealed a continual decrease in SOC content and C:N ratio, suggesting a concerning trajectory that is detrimental to organic matter accumulation. Overall, this study highlights the efficacy of Landsat-9 and multi-temporal imagery incorporating crop growth information in accurately predicting soil properties and assessing their spatial variability.