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Atmospheric Correction Algorithms Assessment for Sentinel-2A Imagery over Inland Waters of China: Case Study, Qiandao Lake

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Abstract

Around 90% of the oceanic and inland waters’ reflectance registered in satellite detectors comes from the atmospheric contribution. Hence the water-leaving radiances in the Near-InfraRed (NIR) region are above the zero value over inland waters because of sediments and dissolved organic particles, this radiance cannot be ignored. To accurately retrieve water quality parameters from water-leaving reflectance, atmospheric correction is the most important step. This study evaluated five reliable atmospheric correction algorithms (AC) known as: (ACOLITE, C2RCC, iCOR, 6SV, and Sen2Cor) against optical in-situ measurements collected above the water in Qiandao Lake, China using Sentinel-2 Multi-Spectral Imager. 60 in-situ water samples and optical measurements (range 400–900 nm) above the water were collected at different points in Qiandao Lake. The spectra measurements were used to validate the atmospheric correction processors. All ACs that were evaluated showed high levels of uncertainty. ACOLITE and ICOR performed the best statistics with root mean square differences (RMSD) (0.006 sr−1) while Sen2Cor achieved the lowest RMSD (0.023 sr−1) across the different modules. ACOLITE, had a better performance when applied to meso- and hypereutrophic waters, compared with oligotrophic, while C2RCC performs better at the wavelength of 833 nm (0.007 sr−1). Finally, 6S performs better at the wavelength of 665 nm (0.015 sr−1). This study introduces insights and addresses a significant research gap in the field of atmospheric correction for satellite imagery over inland waters. Prior studies have primarily focused on atmospheric correction algorithms for coastal and open ocean environments while few studies focused on the unique characteristics and challenges associated with inland water bodies. The findings of this study are crucial for researchers, remote sensing experts, and environmental scientists working with Sentinel-2A imagery, as it enables them to make more accurate and reliable interpretations of water quality and other environmental parameters derived from satellite data.

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Data Availability

The data presented in this study are available on request from the corresponding website.

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Funding

This research was funded by FY-3 Lot 03 Meteorological Satellite Engineering Ground Application System Ecological Monitoring and Assessment Application Project (Phase I): ZQC-R22227, National Natural Science Foundation of China (42201384), and National Natural Science Foundation of China (42171357).

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Authors and Affiliations

  1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China

    Mona Allam & Qingyan Meng

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Qingyan Meng

  3. Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya, 572029, China

    Qingyan Meng

  4. Environment and Climate Changes Research Institute, National Water Research Centre, El Qanater El Khairia, 13621/5, Egypt

    Mona Allam

  5. Department of Water Resources, Faculty of Environmental Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Mohamed Elhag

  6. State Key Laboratory of Remote Sensing, Aerospace Information Institute, Chinese Academy of Science, Beijing, 100101, China

    Mohamed Elhag

  7. Department of Geoinformation in Environmental Management, CI-HEAM/Mediterranean Agronomic Institute of Chania, 73100, Chania, Greece

    Mohamed Elhag

  8. Department of Applied Geosciences, Faculty of Science, German University of Technology in Oman, Muscat, 1816, Oman

    Mohamed Elhag

  9. CNR-Institute for Electromagnetic Sensing of the Environmental, Via A. Corti 12, 20133, Milan, Italy

    Claudia Giardino & Nicola Ghirardi

  10. Department of Environmental Science and Engineering, Fudan University, Shanghai, 200433, China

    Yi Su

  11. School of Computer Science and Technology, Data Mining and High-Performance Computing Lab, University of Chinese Academy of Sciences, Beijing, 101400, China

    Mohammed A. M. Al-Hababi

  12. Geosciences and Remote Sensing Department, Delft University of Technology, Stevinweg 12628 CN, Delft, The Netherlands

    Massimo Menenti

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  1. Mona Allam
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  2. Qingyan Meng
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  3. Mohamed Elhag
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  4. Claudia Giardino
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  5. Nicola Ghirardi
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  6. Yi Su
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  7. Mohammed A. M. Al-Hababi
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Correspondence to Qingyan Meng.

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Appendix 1

Appendix 1

See Table 3.

Table 3 Statistics for the match-ups in the Qiandao Lake
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Allam, M., Meng, Q., Elhag, M. et al. Atmospheric Correction Algorithms Assessment for Sentinel-2A Imagery over Inland Waters of China: Case Study, Qiandao Lake. Earth Syst Environ 8, 105–119 (2024). https://doi.org/10.1007/s41748-023-00366-w

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