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

Aptoula E., Ariman S.

IEEE Geoscience and Remote Sensing Letters, vol.19, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2022
  • Doi Number: 10.1109/lgrs.2021.3070437
  • Journal Name: IEEE Geoscience and Remote Sensing Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Lakes, Water quality, Estimation, Remote sensing, Training, Protocols, Monitoring, Chlorophyll-a (Chl-a), convolutional neural network (CNN), deep learning (DL), regression, sentinel 2, water quality, DEMONSTRATIONS, MSI
  • Kocaeli University Affiliated: No


© 2004-2012 IEEE.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.