Zhao et al. (2025) Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-12-13
• Authors: Longteng Zhao, Zhongping Lee, Xiaolong Yu, Tianhao Wang, Daosheng Wang, Shaoling Shang
• DOI: 10.1016/j.rse.2025.115195

Research Groups

• State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China

Short Summary

This study compiles a highest-quality remote sensing reflectance (Rrs) database from over 20 years of MODIS-Aqua measurements using novel quality control criteria. The resulting database significantly improves data consistency with in situ observations and reveals altered long-term Rrs trends in substantial oceanic regions compared to standard products.

Objective

• To compile a database of the highest quality remote sensing reflectance (Rrs) of oceanic waters (HQMODISA-Rrs) based on over 20 years of MODIS-Aqua measurements, using newly developed quality control criteria.

Study Configuration

• Spatial Scale: Global ocean, with criteria applicable to approximately 91% of the global ocean. Specific analyses highlighted for the South Pacific Ocean and oceanic areas between 50°S and 50°N.
• Temporal Scale: Over 20 years of measurements, focusing on long-term trends.

Methodology and Data

• Models used: Not applicable; the study focuses on data compilation and quality control rather than using predictive or simulation models.
• Data sources:
  • Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite (MODIS-Aqua) ocean color measurements.
  • Benchmark in situ Rrs datasets from Marine Optical BuoY (MOBY) and Aerosol Robotic Network - Ocean Color (AERONET-OC) for validation.

Main Results

• The developed criteria for highest-quality Rrs (CHQR) significantly improved the consistency of MODIS Rrs data when compared with benchmark in situ datasets (MOBY and AERONET-OC).
• In the South Pacific Ocean, applying CHQR reduced the coefficient of variation (CV) of Rrs among pixels from 0.042 (standard quality control) to 0.030, demonstrating effective filtering of abnormal data and enhanced temporal consistency.
• The HQMODISA-Rrs dataset revealed that approximately 21.0% of oceanic areas between 50°S and 50°N showed reversed long-term trends of Rrs compared to trends derived from the standard Rrs product.

Contributions

• Compilation of a novel, highest-quality remote sensing reflectance (HQMODISA-Rrs) database spanning over 20 years of MODIS-Aqua observations.
• Development and validation of robust criteria for filtering MODIS Rrs data, significantly enhancing its consistency and quality against in situ benchmarks.
• Demonstration that this improved dataset leads to different conclusions regarding long-term Rrs trends in significant oceanic regions, highlighting the impact of data quality on climate change studies.
• Provides a foundational dataset anticipated to improve the evaluation and understanding of long-term changes in Rrs-derivative bio-optical properties and facilitate consistent products across various satellite ocean color missions.

Funding

• Not specified in the provided text.

Citation

@article{Zhao2025Highest,
  author = {Zhao, Longteng and Lee, Zhongping and Yu, Xiaolong and Wang, Tianhao and Wang, Daosheng and Shang, Shaoling},
  title = {Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115195},
  url = {https://doi.org/10.1016/j.rse.2025.115195}
}