Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset

Anisya Feby Efriana; Masita Dwi Mandini Manessa; Farida Ayu

doi:10.1117/12.3009622

29 January 2024 Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset

Anisya Feby Efriana, Masita Dwi Mandini Manessa, Farida Ayu

Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129770V (2024) https://doi.org/10.1117/12.3009622
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia

Abstract

Water quality is crucial for the long-term health of undersea biological ecosystems, including elements like Colored Dissolved Organic Matter (CDOM). Gathering field data to characterize CDOM is expensive and time-consuming. To address this, the optical aquatic research community has compiled the GLORIA dataset, which includes measurements of water quality indicators such as chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth. This dataset aids in routine monitoring of high-priority sites, algorithm development, and data validation. In this study, we employed the CDOM data from the GLORIA dataset to develop an empirical CDOM algorithm using Sentinel-2 imagery. The GLORIA dataset encompasses 7,572 stations globally, but for this study, only 92 stations were utilized to construct a tropical water CDOM algorithm. This algorithm was then calibrated with CDOM measurements from the Derawan Archipelago. The developed empirical algorithm is based on a random forest regression model. The algorithm, derived from the GLORIA dataset, demonstrated promising training data accuracy (RMSE = 0.42, R-Square = 0.37). However, the validation accuracy was lower (RMSE = 0.41, R-Square = 0.23), and the tests on the Derawan CDOM dataset indicated even poorer accuracy. These results highlight the challenges in developing a global CDOM algorithm based on multispectral imagery.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Anisya Feby Efriana, Masita Dwi Mandini Manessa, and Farida Ayu "Development of empirical CDOM algorithm for Sentinel-2 using the Gloria dataset", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129770V (29 January 2024); https://doi.org/10.1117/12.3009622

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KEYWORDS

Algorithm development

Remote sensing

Water quality

Data modeling

Machine learning

Random forests

Water

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