Chlorophyll-a Retrieval From Sentinel-2 Images Using Convolutional Neural Network Regression

In this letter, we explore harnessing the power of regression-oriented convolutional neural networks (CNN) for the assessment of surface water quality from remote sensing images. They are used to estimate the chlorophyll-a concentration of Lake Balik (Turkey), through multispectral Sentinel-2 images. The proposed approach is tested with a data set
$(n=320)$
of
in situ Chl-a
measurements acquired during 2017–2019. We investigate both 2-D and 3-D convolution strategies and report the results of a series of rigorous validation experiments, aiming to measure both spatial, short-term, and long-term temporal generalization performance, thus highlighting validation misconduct encountered often in the state-of-the-art. The regression-oriented CNNs outperform various alternatives, in all generalization scenarios with performances reaching 0.95, 0.93, and 0.76 in terms of
$R^{2}$
, respectively. It has been deployed as an online service producing regularly water quality maps for the lake under study as the first of its kind in Turkey.