Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll-a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll-a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll-a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll-a concentration, with a root mean square error of 5.54 mg m−3 for chlorophyll-a concentrations in the range 2.27–81.17 mg m−3.

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► NIR-red models can accurately estimate chlorophyll-a concentrations from airborne data.
► Varying atmospheric effects in a multi-temporal dataset affect the performance of NIR-red models.
► QUick Atmospheric Correction performed reliably well.
► With reliable atmospheric correction, NIR-red models can be used for multi-temporal data.