You et al. (2026) Bridging cloud-induced gaps in MODIS leaf area index products using high-frequency geostationary satellite observations

Identification

• Journal: International Journal of Applied Earth Observation and Geoinformation
• Year: 2026
• Date: 2026-09-23
• Authors: Jiakai You, Yinghui Zhang, Zhongwen Hu, Jingzhe Wang, Guofeng Wu
• DOI: 10.1016/j.jag.2026.105611

Research Groups

• School of Architecture and Urban Planning, Shenzhen University, China
• MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, China
• School of Artificial Intelligence, Shenzhen Polytechnic University, China

Short Summary

This study develops an observation-complementary framework that integrates high-frequency Himawari-8 Advanced Himawari Imager (AHI) observations with MODIS LAI products to achieve LAI reconstruction in cloud-prone regions. The framework reconstructs missing pixels using authentic high-frequency geostationary measurements instead of mathematical inference.

Objective

• To bridge the observational gap between polar-orbiting satellite observations and geostationary satellites by developing a spatiotemporal fusion framework that integrates high-frequency Himawari-8 AHI observations with MODIS LAI products.
• To evaluate whether such a synergistic framework can improve the temporal continuity, numerical stability, and practical usability of LAI products under persistent cloud contamination.

Study Configuration

• Spatial Scale: 2 km (AHI) to 500 m (MODIS)
• Temporal Scale: 10 minutes (AHI) to 4-day compositing period (MODIS)

Methodology and Data

• Models used:
  • Random Forest model for LAI retrieval from AHI observations
  • Spatial weight-based downscaling algorithm for downsampling AHI LAI from 2 km to 500 m
• Data sources:
  • Himawari-8 Advanced Himawari Imager (AHI) Level 1 Gridded product
  • MODIS LAI product (MCD15A3H)
  • Sentinel-2 LAI dataset
  • GLASS LAI product
  • GEOV2 LAI product

Main Results

• The fused AHI-MODIS LAI product showed improved spatiotemporal continuity and data availability compared to the original MODIS LAI product.
• The study demonstrated that integrating high-frequency geostationary observations with polar-orbiting satellite products can improve the temporal continuity, numerical stability, and practical usability of LAI products under persistent cloud contamination.

Contributions

• This study provides a novel approach for bridging the observational gap between polar-orbiting satellite observations and geostationary satellites by developing a spatiotemporal fusion framework.
• The study demonstrates the potential of integrating high-frequency geostationary observations with polar-orbiting satellite products to improve the temporal continuity, numerical stability, and practical usability of LAI products.

Funding

• This research was supported by the National Natural Science Foundation of China (Grant No. 42122005)
• This research was also supported by the Guangdong Provincial Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services

Citation

@article{You2026Bridging,
  author = {You, Jiakai and Zhang, Yinghui and Hu, Zhongwen and Wang, Jingzhe and Wu, Guofeng},
  title = {Bridging cloud-induced gaps in MODIS leaf area index products using high-frequency geostationary satellite observations},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2026},
  doi = {10.1016/j.jag.2026.105611},
  url = {https://doi.org/10.1016/j.jag.2026.105611}
}