Abstract
An adaptive selection of the near/shortwave infrared (NIR/SWIR) reflectance correction and the quasi-analytic algorithms (QAAs) is proposed for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) to utilize the strengths of different correction algorithms and QAAs in a single satellite scene with water types ranging from turbid coastal to clear open ocean waters. A blended satellite product is generated by merging three atmospheric-correction algorithms(AD-ATCOR): 1) iterative NIR correction; 2) management unit of the north sea mathematical models (MUMM); and 3) SWIR, using a spectral threshold-based selection for different water types. The validation analysis of a blended remote sensing reflectance product showed overall good agreement with AERONET-OC observations followed by NASA bio-optical marine algorithm data set (NOMAD) at the blue wavelengths and the estuarine data set at the green and red wavelengths. The results suggest that the adaptive method is a better alternative to address the challenging problem of selecting different correction algorithms for different water types in a single satellite scene. Likewise, an adaptive selection of a QAA (AD-QAA) used the QAA-v5 and the QAA-V to obtain merged inherent optical property (IOP) products in a single MODIS-Aqua scene with varying water types. As a case study, the two adaptive selection procedures were sequentially applied to the MODIS-Aqua imagery representing four environmental conditions in the northern Gulf of Mexico. Improved retrievals of the total absorption and backscattering coefficients along an estuarine to ocean continuum demonstrated the effectiveness of this method in an optically complex and dynamic river-dominated system.
Highlights
B IOGEOCHEMICAL water constituents, such as phytoplankton, mineral particles, and colored dissolved organic matter (CDOM), play important roles in marine biogeochemical cycles and can be directly or indirectly linked to various local and global phenomena such as harmful algal blooms, pollutant transport, and climate change [1]–[5]
To utilize the strengths of three well-known NIR- and short-wave infrared (SWIR)-correction algorithms and two quasi-analytic algorithm (QAA) in a single satellite scene in which water type varies from turbid coastal to clear open ocean waters, we propose a methodology for a pixel-by-pixel selection of the reflectance correction algorithm based on the spectral criteria of different water types and for blending two QAAs, QAA-v5, and QAA-V, for the MODIS-Aqua
We proposed a methodology for AD-ATCOR and AD-QAA algorithms for the MODIS-Aqua sensor to utilize the strengths of different NIR/SWIR correction algorithms and QAA algorithms within a satellite scene with contrasting water types, ranging from turbid coastal to the clear open ocean waters
Summary
B IOGEOCHEMICAL water constituents, such as phytoplankton, mineral particles, and colored dissolved organic matter (CDOM), play important roles in marine biogeochemical cycles and can be directly or indirectly linked to various local and global phenomena such as harmful algal blooms, pollutant transport, and climate change [1]–[5]. The short-wave infrared (SWIR) correction algorithm by Wang and Shi [18] is the GW94 but uses two SWIR wavelengths instead of NIR; it has demonstrated its efficacy in highly turbid waters with the assumption of zero water-leaving radiance in the SWIR region in highly reflecting waters due to strong water absorption [19], [25], [26] These correction algorithms have provided reasonable estimates of ocean color products in a variety of water types throughout the world, their application is limited to a subset of satellite imagery and often fail to perform satisfactorily and consistently in waters with varying color changes—from brown (e.g., turbid estuarine) to green (e.g., productive nearshore) and onto blue (e.g., clear open ocean) waters. The performance of AD-ATCOR and AD-QAA is evaluated under different environmental conditions in the nGoM
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