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<p>Inland fisheries play a significant role in global fish catch and food security, with Bangladesh being a major contributor due to its extensive wetlands and diverse water bodies. Effective management of these fisheries depends heavily on water quality, where Chlorophyll-a (Chl-a) serves as a key indicator of aquatic health. This study exploits Google Earth Engine (GEE) cloud computing platform to analyze Sentinel-2 and Landsat-8 satellite imagery, coupled with in situ Chl-a measurements. Various band combinations and remote sensing algorithms, including Two-Band Algorithm, Three-Band Algorithm, Four-Band Algorithm, Five-Band Algorithm, Six-Band Algorithm, Normalized Difference Chlorophyll Index (NDCI), and Maximum Chlorophyll Index (MCI) were adapted to sensor characteristics and evaluated. The results show that Sentinel-2 data yields better performance in Chl-a retrieval algorithms, with the highest coefficient of determination (R²) reaching 0.62. In contrast, Landsat-8 data, with coarser resolution, showed lower performance, with the highest R² value of 0.17. The study highlights the superior performance of Sentinel-2 in Chl-a retrieval due to its finer spectral and spatial resolution and the inclusion of visible red edge bands. The findings underscore the efficacy of these remote sensing tools in overcoming the limitations of traditional water quality monitoring methods, offering a scalable solution for managing inland fisheries.</p>

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Comparative analysis of chlorophyll-a retrieval algorithms for inland waterbodies of Bangladesh using Sentinel-2 and Landsat-8 imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Mohammad Imrul Islam,
  • M. Mahmudur Rahman,
  • M. Ziaul Islam

摘要

Inland fisheries play a significant role in global fish catch and food security, with Bangladesh being a major contributor due to its extensive wetlands and diverse water bodies. Effective management of these fisheries depends heavily on water quality, where Chlorophyll-a (Chl-a) serves as a key indicator of aquatic health. This study exploits Google Earth Engine (GEE) cloud computing platform to analyze Sentinel-2 and Landsat-8 satellite imagery, coupled with in situ Chl-a measurements. Various band combinations and remote sensing algorithms, including Two-Band Algorithm, Three-Band Algorithm, Four-Band Algorithm, Five-Band Algorithm, Six-Band Algorithm, Normalized Difference Chlorophyll Index (NDCI), and Maximum Chlorophyll Index (MCI) were adapted to sensor characteristics and evaluated. The results show that Sentinel-2 data yields better performance in Chl-a retrieval algorithms, with the highest coefficient of determination (R²) reaching 0.62. In contrast, Landsat-8 data, with coarser resolution, showed lower performance, with the highest R² value of 0.17. The study highlights the superior performance of Sentinel-2 in Chl-a retrieval due to its finer spectral and spatial resolution and the inclusion of visible red edge bands. The findings underscore the efficacy of these remote sensing tools in overcoming the limitations of traditional water quality monitoring methods, offering a scalable solution for managing inland fisheries.