Radar-optical fusion of Sentinel-1/2 for high-resolution NDVI reconstruction and landscape-driven carbon flux assessment in Kuala Selangor, Malaysia (2020–2024)

Reliable quantification of carbon fluxes in humid tropical regions is constrained by persistent cloud cover, heterogeneous land mosaics, and the limited resolution of existing products. To address these challenges, this study developed a Cloud-Resilient Fusion Network (CRFNet) that integrates Sentinel-1 SAR backscatter with cloud-screened Sentinel-2 NDVI using a CNN–BiLSTM–multi-head attention architecture. The framework reconstructed 10 m NDVI time series in Kuala Selangor, Malaysia (2020–2024), achieving annual R2 above 0.82 and RMSE below 0.12, thereby improving temporal continuity under heavy cloud–rainfall interference. The reconstructed NDVI was used to drive a light-use-efficiency model for net ecosystem productivity (NEP) estimation, supported by temperature-based heterotrophic respiration. Results showed a 7.4 % decline in mean annual NEP across five years, with degraded mangroves and sloping croplands emerging as hotspots of sink-to-source transitions. Landscape analysis revealed strong structure–function coupling: stable forests and mangroves were characterized by large cohesive patches with largest patch index values above 40 % and edge density below 20 m ha-1, while croplands and degraded slopes exhibited higher patch numbers, reduced patch dominance, and greater edge complexity, which increased carbon source risk. By linking fine-scale NDVI reconstruction with process-based carbon modeling and landscape metrics, this study provides a transferable workflow for high-resolution carbon flux monitoring and a robust scientific basis for carbon budget assessment, ecosystem management, and carbon-neutrality planning in tropical monsoon regions.

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