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Here, we demonstrate how a combination of three multivariate statistic techniques can identify key environmental factors affecting the seasonal and spatial variability of chlorophyll-a (Chl-a) in a productive tropical estuarine-lagoon system. Remote estimation of Chl-a was carried out using a NIR-Red model based on MODIS bands, which is highly consistent with the in situ measurement of Chl-a with root mean square error (RMSE) of 15.24 mg m−3 and 13.43 mg m−3 for two independent datasets used for the model’s calibration and validation, respectively. Our findings suggest that the river discharges and hydraulic residence time of the lagoons promote a stronger effect on the spatial variability of Chl-a in the coastal lagoons, while wind, solar radiation and temperature have a secondary importance. The results also indicate a slight seasonal variability of Chl-a in Mundau lagoon, which are different the from Manguaba lagoon. The multivariate approach was able to fully understand the relative importance of key environmental factors on the spatiotemporal variability of Chl-a of the aquatic ecosystem, providing a powerful tool for reducing dimensionality and analyzing large amounts of satellite-derived Chl-a data.

remote sensing

A Multivariate Analysis Framework to Detect Key Environmental Factors Affecting Spatiotemporal Variability of Chlorophyll-a in a Tropical Productive Estuarine-Lagoon System