Title: Improved turbidity estimates in complex inland waters using combined NIR–SWIR atmospheric correction approach for Landsat 8 OLI data
Abstract: Turbidity is one of the important water quality parameters, essentially a proxy to assess eutrophication state in inland coastal systems. In this article, a method of combined near-infrared–shortwave infrared (NIR–SWIR) atmospheric correction for Landsat 8 (L8) Operational Land Imager data is proposed to improve the turbidity retrieval in optically complex waters. From the extremely turbid to moderately turbid waters, the relative ranges in water-leaving reflectance in band 3 () are found to be 19–92% and 31–79% in band 4 (). The SWIR reflectances in and are 57% and 66% higher than that of standard NIR correction in extremely turbid waters. However, this method has resulted in ~30% higher reflectances than the NIR method in relatively less turbid waters; the latter method is still good in moderately turbid waters. Using Rayleigh corrected reflectances, a turbidity index, , was computed to discriminate the productive and/or turbid waters. The SWIR method was applied for water having Tind > 1.5 threshold and the NIR method in the other regions. A new turbidity algorithm has been developed using L8 two band ratio () optimized with in situ turbidity data from four data buoys for 2014. The Landsat 8 band-weighted in situ reflectances for bands 3 and 4 are used to derive turbidity using the present algorithm and validated against in situ turbidity, providing a good coefficient of determination of R2 = 0.87. As compared to the NIR-based correction, the turbidity obtained from the combined (NIR + SWIR) correction in extremely turbid waters is around 80–90% (absolute percentage difference (APD)) different. Whereas in the moderately turbid waters, the APD between the two corrections was around 50–75%. There are no obvious data discontinuities in using the combined approach. Comparisons were made with available single-band turbidity algorithms and found that the present turbidity algorithm performed well in the optically complex lagoon environment.
Publication Year: 2018
Publication Date: 2018-05-09
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 11
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