Mapping ocean surface algal blooms with SWIR-derived satellite remote sensing reflectance

Wei, Jianwei; Wang, Menghua H.

doi:10.1016/j.jag.2024.103921

i

Mapping ocean surface algal blooms with SWIR-derived satellite remote sensing reflectance

2024
By Wei, Jianwei ; Wang, Menghua H.

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Journal Title:

International Journal of Applied Earth Observation and Geoinformation
Personal Author:

Wei, Jianwei ; Wang, Menghua H.
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; JPSS (Joint Polar Satellite System Program Office)
Description:

This study exploits the potential of satellite remote sensing reflectance spectra (Rrs(λ)) for detecting ocean surface algal blooms. Three types of floating surface algae are examined: Sargassum, Ulva, and Trichodesmium. The satellite images are processed with a shortwave infrared (SWIR)-based atmospheric correction processor from the Visible Infrared Imaging Radiometer Suite (VIIRS). We calculate the red-edge reflectance anomaly from the Rrs(λ) data to delineate the notable spectral difference at 671 and 862 nm. The new data have generated floating algal maps comparable with historical methods relying on Rayleigh-corrected reflectance data. The Rrs(λ) spectra are found to have high quality scores over Sargassum and Trichodesmium waters, suggesting less uncertainty from atmospheric correction. With problems to be addressed, this preliminary study finds it promising to use the VIIRS reflectance products for floating algae detection and mapping. Continuous efforts are highly recommended as the new data and approach can not only facilitate a retrospective analysis over global oceans but also benefit a greater application to next-generation hyperspectral satellites.
Keywords:

Algae Remote Sensing Sargassum Trichodesmium Ulva Remote Sensing Floating Surface Algae Red-edge Anomaly Remote Sensing Reflectance
Source:

International Journal of Applied Earth Observation and Geoinformation 131 (2024) 103921
DOI:

https://doi.org/10.1016/j.jag.2024.103921
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PDF
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
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Submitted
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urn:sha-512:0f5382775cda96799d76c2d839b85f84725f4fd56266bc66ea74628c917b266b0958b2686a83aaf6e7cb2f8929fe01dae57b70cf24b20751400f27189d80272d
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