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Estimating water reflectance accurately from satellite optical data requires implementing an accurate atmospheric correction (AC) scheme, a particularly challenging task over optically complex water bodies, where the signal that comes from the water prevents using the near-infrared (NIR) bands to separate the perturbing atmospheric signal. In the present work, we propose a new AC scheme specially designed for the Río de la Plata—a funnel-shaped estuary in the Argentine–Uruguayan border—highly scattering turbid waters. This new AC scheme uses far shortwave infrared (SWIR) bands but unlike previous algorithms relates the atmospheric signal in the SWIR to the signal in the near-infrared (NIR) and visible (VIS) bands based on the decomposition into principal components of the atmospheric signal. We describe the theoretical basis of the algorithm, analyze the spectral features of the simulated principal components, theoretically address the impact of noise on the results, and perform match-ups exercises using in situ measurements and Moderate Resolution Imaging Spectrometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) imagery over the region. Plausible water reflectance retrievals were obtained in the NIR and VIS bands from both simulations and match-ups using field data—with better performance (i.e., lowest errors and offsets, and slopes closest to 1) compared to existing AC schemes implemented in the NASA Data Analysis Software (SeaDAS). Moreover, retrievals over images in the VIS and NIR bands showed low noise, and the correlation was low between aerosol and water reflectance spatial fields.

remote sensing

Atmospheric Correction of Satellite Optical Imagery over the Río de la Plata Highly Turbid Waters Using a SWIR-Based Principal Component Decomposition Technique