High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model

Accurate digital soil organic carbon mapping is of great significance for regulating the global carbon cycle and addressing climate change. With the advent of the remote sensing big data era, multi-source and multi-temporal remote sensing techniques have been extensively applied in Earth observation. However, how to fully mine multi-source remote sensing time-series data for high-accuracy digital SOC mapping remains a key challenge. To address this challenge, this study introduced a new idea for mining multi-source remote sensing time-series data. We used 413 topsoil organic carbon samples from southern Xinjiang, China, as an example. By mining multi-source (Sentinel-1/2) remote sensing time-series data from 2017 to 2023, we revealed the temporal variation pattern of the correlation between Sentinel-1/2 time-series data and SOC, thereby identifying the optimal time window for monitoring SOC using Sentinel-1/2 data. By integrating environmental covariates and a super ensemble model, we achieved high-accuracy mapping of SOC in Southern Xinjiang, China. The results showed the following aspects: (1) The optimal time windows for monitoring SOC using Sentinel-1/2 data were July–September and July–August, respectively; (2) the modeling accuracy using multi-source sensor data integrated with environmental covariates was superior to using single-source sensor data integrated with environmental covariates alone. In the optimal model based on multi-source data, the cumulative contribution rate of Sentinel-2 data is 51.71% higher than that of Sentinel-1 data; (3) the stacking super ensemble model’s predictive performance outperformed the weight average and simple average ensemble models. Therefore, mining the optimal time windows of multi-source remote sensing data and environmental covariates, driven a super ensemble model, represents a high-accuracy strategy for digital SOC mapping.