For assessing forest resource quality and carbon sequestration, both optical and synthetic aperture radar (SAR) remote sensing data have been widely used to map forest aboveground carbon storage (AGC), demonstrating considerable potential across diverse forest types. However, the fusion approaches between SAR and optical data remain technically challenging, particularly when combining multi-source optical and multi-temporal SAR datasets. In this study, multiple optical datasets with varying spatial resolutions and spectral bands (Landsat-9, Sentinel-2, GF-6 PMS, and GF-6 WFV) and time-series Sentinel-1 data acquired within the same year were employed to develop an optical–SAR fusion framework for mapping forest AGC in mixed broadleaf–coniferous forests. Firstly, a multi-level collaborative fusion strategy (MLC) was developed using multi-source optical data by integrating the strengths of both pixel-level and feature-level fusion. Subsequently, a multi-temporal SAR combining approach was designed based on seasonal variation patterns using one-year time-series Sentinel-1 data. Finally, an optical–SAR modeling approach was established to map forest AGC using multiple machine learning models combined with the sequential forward feature selection method. The results demonstrate that the proposed MLC fused method for multi-source optical data offers significant advantages in enhancing estimation accuracy and improving model robustness. Furthermore, when multi-temporal Sentinel-1 data were integrated with the MLC-fused optical data, the optical–SAR collaborative approach further improved the coefficient of determination (R2), effectively mitigating the saturation effect commonly observed in optical data. The highest performance was achieved using spring-acquired multi-temporal Sentinel-1 data within the SVR model, yielding an R2 of 0.69 and reducing rRMSE to 18.03%. It is indicated that an appropriate fusing strategy for integrating optical and SAR data can substantially enhance both accuracy and reliability in mapping forest AGC.
Xu et al. (Thu,) studied this question.