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

• CRFNet radar–optical fusion reconstructs seamless 10 m NDVI in cloudy tropics. • Method restores NDVI continuity across diverse land covers for landscape analysis. • NDVI drives land-cover-specific LUE and Rh models to map monthly NEP at 10 m. • Landscape metrics reveal fragmentation-driven carbon-source hotspots in NEP. • Workflow is transferable for fine-scale carbon monitoring in tropical regions. 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 R 2 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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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-11-29
DOI
https://doi.org/10.1016/j.jag.2025.104966
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

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

Helmi Zulhaidi Mohd Shafri, Shengrui Yu, Abdul Rashid Mohamed Shariff, Zhi Zheng et al.
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing in Agriculture
article

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

Helmi Zulhaidi Mohd Shafri, Shengrui Yu, Abdul Rashid Mohamed Shariff, Zhi Zheng, Seung-Han You, Pengfei Jia
article en

Abstract

• CRFNet radar–optical fusion reconstructs seamless 10 m NDVI in cloudy tropics. • Method restores NDVI continuity across diverse land covers for landscape analysis. • NDVI drives land-cover-specific LUE and Rh models to map monthly NEP at 10 m. • Landscape metrics reveal fragmentation-driven carbon-source hotspots in NEP. • Workflow is transferable for fine-scale carbon monitoring in tropical regions. 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 R 2 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.

International Journal of Applied Earth Observation and GeoinformationVol. 145
Universiti Putra Malaysia (MY), Chinese Academy of Sciences (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
Openalex Percentile: Top 64%
Remote Sensing in Agriculture
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