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What water color parameters could be mapped using MODIS land reflectance products?

Satellite ocean color instruments have been used to characterize physical, chemical, and biological variabilities in oceanic, coastal, and inland waters. However, the massive loss and large uncertainty results of remote sensing reflectance data in difficulty in monitoring nearshore coastal and inland waters. The ocean color community has been pursuing easy-access and reliable reflectance products for observing nearshore coastal and inland waters.

Most of the coastal and inland waters are generally turbid; in these areas, the contribution of water to the signals of the top of the atmosphere is raised, suggesting the possibility reality for oceanographers and limnologists to use land reflectance products for water monitoring. MODIS surface reflectance product (R_land) has been used to monitor water clarity and suspended solids in waters yet the applications to retrieve phytoplankton pigments and colored dissolved organic carbon has rarely been addressed. To date, its applicability in aquatic remote sensing has not been sufficiently assessed. Some fundamental questions such as the following need to be addressed: How does the R_land product perform in global inland and coastal waters? What water color parameters can be mapped using R_land?

Recently, a research group led by Prof. Hongtao Duan and Ronghua Ma from the Nanjing Institute of Geography and Limnology of the Chinese Academy of Sciences provided a comprehensive evaluation of the performance of MODIS R_land products against a field optical dataset containing 4143 reflectance spectra, 2320 chlorophyll-a samples, and 1467 suspended particulate matter samples across global nearshore coastal and inland waters.

This work was published in Earth-Science Reviews.

"Despite the ease of using this product and its higher spatial resolution than the MODIS ocean bands, according to our assessment, R_land might not be an optimal data source for monitoring inland and coastal waters," said Prof. Duan.

The results showed that R_land significantly overestimated remote sensing reflectance, particularly in 469 nm and 859 nm bands. Such a global assessment was consistent with the results published in Lake Taihu and Chesapeake Bay before. In addition, land reflectance showed evident overestimations compared to the ocean color products derived using SeaDAS software in the east China Sea.

The study also reported noticeable negative values and patchiness in the R_land imagery. The proportion of R_land at 555 nm is even beyond 5% in the coastal area of Australia and Africa. R_land was frequently negative in the pixels covered by cyanobacterial scums, e.g., >20% negatives in Lake Taihu. "The negatives and patchiness in R_land possibly resulted from the unsuitable mechanism to remove aerosols in generating R_land over waters, " said Prof. Ma.

Existing algorithms did not estimate satisfactory Chla and suspended solids from R_land across the global inland and coastal waters. Machine learning models outperformed the state-of-the-art algorithms for SPM retrievals in global turbid waters from R_land. But, all models, including machine learning models, cannot retrieve reliable chlorophyll-a from R_land with approximately 55% uncertainty due to the limited spectral information and uncertainty of R_land products. This implicated that R_land might be able to quantify the parameters closely related to suspended solids (e.g., water clarity and extinction coefficients) in most waters; however, it is challenging to quantify pigments like Chla in waters from R_land.

This study comprehensively evaluated the accuracy of R_land for the first time in global inland and coastal waters. MODIS R_land does not contain sufficient information that makes these existing algorithms usable. Consequently, various water color parameters from R_land are difficult to retrieve except for several parameters, such as SPM, turbidity, and water clarity. The results are anticipated to provide a benchmark of R_land in various waters worldwide.

 

Link at https://doi.org/10.1016/j.earscirev.2022.104154

 

 

 

 

 

 

Contact

TAN Lei

Nanjing Institute of Geography and Limnology

E-mail: ltan@niglas.ac.cn

 

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