How Can I Accurately Account For The Effects Of Adjacency Radiance And Aerosol Scattering In Sentinel-2 Multi-spectral Imagery To Improve The Retrieval Of Water Quality Parameters, Such As Chlorophyll-a And Total Suspended Matter, In Shallow And Turbid Wetlands With Dense Vegetation?

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To accurately account for adjacency radiance and aerosol scattering in Sentinel-2 imagery and improve water quality parameter retrieval in shallow, turbid wetlands with dense vegetation, follow this structured approach:

1. Atmospheric Correction

  • Tool Selection: Use ACOLITE, a water-specific atmospheric correction tool, to estimate aerosol optical thickness and correct reflectance data. This step is crucial for reducing the impact of atmospheric scattering.

2. Adjacency Radiance Correction

  • Adjacency Removal: Implement an adjacency radiance correction algorithm to account for light reflected from nearby land. This may involve calculating reflectance from surrounding land pixels and adjusting water pixels accordingly.

3. Vegetation and Water Signal Separation

  • Spectral Unmixing: Apply spectral unmixing techniques to separate vegetation and water signals. Use indices like NDVI to identify vegetation and SWIR bands to enhance water detection.
  • Water Masking: Create a water mask using SWIR bands to accurately identify water pixels and reduce land adjacency effects.

4. Retrieval Model for Water Quality Parameters

  • Model Selection: Use a semi-analytical model like C2R or machine learning approaches trained with in-situ data to retrieve chlorophyll-a and TSM, considering the complex optical properties of turbid waters.

5. Validation with In-Situ Data

  • Collect ground truth measurements of chlorophyll-a and TSM to validate the accuracy of corrections and retrieval models, ensuring reliable results.

6. Considerations and Additional Techniques

  • Band Selection: Optimize band usage, such as using coastal aerosol bands for atmospheric correction and red-edge/NIR bands for vegetation.
  • BRDF Effects: While less critical for water, consider BRDF effects from adjacent land surfaces.
  • Computational Resources: Ensure adequate resources for processing-intensive tasks, especially machine learning models.

7. Documentation and Reproducibility

  • Maintain detailed documentation of each processing step and validate each correction to ensure reproducibility and facilitate troubleshooting.

By systematically addressing each factor and validating each step, you can enhance the accuracy of water quality parameter retrieval in challenging wetland environments.