4.7 Article

Development of a Practical Atmospheric Correction Algorithm for Inland and Nearshore Coastal Waters

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2021.3092839

关键词

Aerosols; Lakes; MODIS; Sea measurements; Land surface; NASA; Satellites; Aerosol optical depth (AOD); aerosol scattering; atmospheric correction (AC); Moderate Resolution Imaging Spectroradiometer (MODIS); Rrs; satellite signal in the solar spectrum-vector (6SV); Sea-viewing Wide Field-of view Sensor (SeaWiFS) Data Analysis System (SeaDAS)

资金

  1. National Natural Science Foundation of China [41890852, 41971304, 41890851]
  2. Stable Support Plan Program of Shenzhen Natural Science Fund [20200925155151006]
  3. Shenzhen Science and Technology Innovation Committee [JCYJ20190809155205559]

向作者/读者索取更多资源

The study proposes a practical Atmospheric Correction algorithm for inland and nearshore coastal waters (ACLANC), which uses interpolated aerosol optical depth products from nearby land surfaces and simulates aerosol reflectance spectrum. ACLANC outperforms existing atmospheric correction algorithms in accuracy and data coverage, providing high accuracy and wide coverage of remote-sensing reflectance for global inland and nearshore coastal waters.
A practical Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC) is proposed in this study. The ACLANC algorithm uses interpolated aerosol optical depth (AOD) products (AOD $_{{interp}})$ from nearby land surfaces and simulates the corresponding aerosol reflectance spectrum using a combination of the continental model in the satellite signal in the solar spectrum-vector (6SV) radiative transfer code and an approximate aerosol model (r85f20) in the Sea-viewing Wide Field-of view Sensor (SeaWiFS) Data Analysis System (SeaDAS). Validations with worldwide $in situ$ measurements show that the ACLANC-derived remote-sensing reflectance ( $R_{{rs}}$ ) for nine Moderate Resolution Imaging Spectroradiometer (MODIS) bands agreed well with the $in situ$ datasets, where the mean R-2 was 0.77 +/- 0.09 and the mean unbiased percent difference was 28.7% +/- 9.8%. ACLANC outperformed the existing atmospheric correction algorithms in not only the accuracy of the $R_{{rs}}$ retrievals but also data coverage. Vicarious calibration over the ACLANC algorithm showed minor improvement in the derived $R_{{rs}}$ products. Error budget analysis revealed that the uncertainties in AOD $_{{interp}}$ represent >50% of the errors for ACLANC and that this proportion increases with decreasing AOD. Further efforts can also be applied to improve the aerosol models, especially for turbid aerosol environments, where the fixed aerosol model in SeaDAS contributes up to 30% of the error budget. The ACLANC algorithm can potentially be implemented in ocean color missions other than MODIS to obtain $R_{{rs}}$ with high accuracy and wide coverage for global inland and nearshore coastal waters.

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