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

Abstract

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n=320)$ </tex-math></inline-formula> of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ Chl-a</i> 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> , 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.