4.7 Article

Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

期刊

REMOTE SENSING
卷 13, 期 2, 页码 -

出版社

MDPI
DOI: 10.3390/rs13020184

关键词

remote sensing reflectance; data quality; the Indian Ocean; accuracy

资金

  1. National Key R&D Program of China [2017YFC1405105, 2016YFA0600102]
  2. National Natural Science Foundation of China [41506204, 41876206, 41706209, 61890964]
  3. China-Korea joint ocean research project [PI-2019-1-01]
  4. Global Change and Air-Sea Interaction Program [GASI-02-IND-STSaut, GASI-02-IND-STSwin]
  5. Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies [2020B1212060025]
  6. Dragon-4 project [32405]

向作者/读者索取更多资源

This study evaluates and compares the quality of R-rs(lambda) in the Indian Ocean, finding low-quality data in the Bay of Bengal and oligotrophic gyre zones, possibly influenced by aerosols and sun-glint. Further research is recommended to improve corrections for sun-glint and aerosols in these regions.
Spectral remote sensing reflectance (R-rs(lambda), sr(-1)) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where R-rs(lambda) accuracy has not been well documented, the quality of R-rs(lambda) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual R-rs(lambda) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of R-rs(lambda) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly R-rs(lambda) over the IO indicates good quality with the average scores of 0.93 +/- 0.02, 0.92 +/- 0.02 and 0.92 +/- 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (similar to 0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (similar to 0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the R-rs(lambda) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean-atmosphere environment.

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