A downscaling and fusion framework for fine-resolution gross primary productivity estimation

Precise quantification of gross primary productivity (GPP) at fine resolution is crucial for regional carbon cycle analysis. However, existing GPP products frequently fail to capture fine-scale variability due to an inherent trade-off between satellite spatiotemporal resolution and the coarse-scale design of most existing GPP models. To address this issue, a machine learning-based downscaling and correction framework was designed to generate high-accuracy monthly GPP at 30-m resolution (DS-FC-GPP) through fusing multiple remote sensing model-based products along with eddy covariance (EC) data. Moderate resolution imaging spectroradiometer (MODIS) and global land surface satellite (GLASS) GPP products were simultaneously downscaled from 500 m to 30 m resolution using a fine-resolution vegetation index time series. Subsequently, a unified fusion and correction framework was established based on EC data. The analysis revealed that DS-FC-GPP exhibited enhanced spatial patterns with finer details and maintained consistency with the original products when aggregated back to coarse resolution. In the spatial validation, the leave-one-site-out validation achieved a coefficient of determination (R2) of 0.828 with a mean absolute error (MAE) of 39.497 gC·m−2·month−1, while the leave-one-region-out scheme yielded a comparable performance with an R2 of 0.812 and an MAE of 42.506 gC·m−2·month−1. The consistently strong performance across both validation strategies demonstrates the robustness of the proposed framework. Moreover, DS-FC-GPP better captured intra-annual peaks and valleys, maintaining stable accuracy throughout the year. The transfer experiment over China indicated that the DS-FC-GPP framework exhibits robust generalization and transferability. The present work highlights the potential of multisource data fusion and downscaling to promote fine-resolution GPP estimation, offering valuable insights into terrestrial carbon cycle processes.