Version 1
: Received: 22 December 2023 / Approved: 25 December 2023 / Online: 25 December 2023 (13:20:14 CET)
Version 2
: Received: 19 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (11:44:18 CET)
How to cite:
Oliveira Santos, V.; Guimarães, B. M. D. M.; Lima Neto, I. E.; Souza Filho, F. D. A.; Costa Rocha, P. A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v1
Oliveira Santos, V.; Guimarães, B. M. D. M.; Lima Neto, I. E.; Souza Filho, F. D. A.; Costa Rocha, P. A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints 2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v1
Oliveira Santos, V.; Guimarães, B. M. D. M.; Lima Neto, I. E.; Souza Filho, F. D. A.; Costa Rocha, P. A.; Van Griensven Thé, J.; Gharabaghi, B. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints2023, 2023121854. https://doi.org/10.20944/preprints202312.1854.v1
APA Style
Oliveira Santos, V., Guimarães, B. M. D. M., Lima Neto, I. E., Souza Filho, F. D. A., Costa Rocha, P. A., Van Griensven Thé, J., & Gharabaghi, B. (2023). Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning. Preprints. https://doi.org/10.20944/preprints202312.1854.v1
Chicago/Turabian Style
Oliveira Santos, V., Jesse Van Griensven Thé and Bahram Gharabaghi. 2023 "Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing and Machine Learning" Preprints. https://doi.org/10.20944/preprints202312.1854.v1
Abstract
Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in the state of Ceará, Brazil. Several machine learning models, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and support vector machine models were trained and tested. A stepwise approach determined the best subset of input parameters. The best-performing model was the GMDH, achieving an R2 of 0.91 and RMSE of 20.38 mg/L, which is a value consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.