Publications

Wu, XD; Pan, F; Zeng, QC; Wen, JG; Yin, GF; Lin, XW; You, DQ; Xiao, Q (2025). A Pixel-by-Pixel Error Correction Framework of Satellite Products Against Pixel-Scale Ground Truth From Sparse Observation Networks: A Case Study of MCD43A3 v061 Across the Globe. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 63, 4410017.

Abstract
Satellite products have served as the foundation for subsequent analysis, modeling, and decision-making. However, the errors or inconsistencies of satellite products may bias or even mislead the conclusions and decisions based on them. Using ground-based observation data to directly correct the errors in satellite products provides a more relaxed and direct method for constraining the errors of satellite products. However, it is challenged by the sparsity of ground station distribution and the spatial scale mismatch between ground observations and satellite pixels. To address this issue, this study pioneers an integrated and comprehensive methodological framework for pixel-by-pixel error correction based on sparse in situ site observation data across the globe. This methodological framework comprises several core components: the error correction models over the regions with in situ sites based on the pixel-scale ground truth, the spatial extension model to allocate optimal error correction model for regions without in situ sites, and finally the pixel-by-pixel error correction of satellite products. MCD43A3 v061 was taken as an example to illustrate the methodology as well as its effectiveness. The RMSE of error-corrected MCD43A3 based on the optimal correction model was reduced from 0.05 to approximately 0.02. To conclude, the results and comparative analysis shown in this study suggested that the proposed framework for pixel-by-pixel error correction of satellite products based on ground observations from sparse networks has the potential to further improve the quality of satellite products across the globe.

DOI:
10.1109/TGRS.2025.3568188

ISSN:
1558-0644