干旱区生态环境知识资源中心(Arid): Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

Arid

DOI	10.3390/rs16111870
	Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods
	Oliveira Santos, Victor; Guimaraes, Bruna Monallize Duarte Moura; Neto, Iran Eduardo Lima; de Souza Filho, Francisco de Assis; Costa Rocha, Paulo Alexandre; The, Jesse Van Griensven; Gharabaghi, Bahram
通讯作者	Gharabaghi, B
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2024
卷号	16 期号:11
英文摘要	It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Cear & aacute;, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 mu g/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.
英文关键词	chlorophyll-a Sentinel-2 satellite machine learning freshwater reservoirs eutrophication
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001246648400001
WOS关键词	SUPPORT VECTOR MACHINES ; SNOW COVER ; RANDOM FORESTS ; WATER-QUALITY ; TIME-SERIES ; SENTINEL-2 ; VEGETATION ; REGRESSION ; SATELLITE ; INDEXES
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405300
推荐引用方式 GB/T 7714	Oliveira Santos, Victor,Guimaraes, Bruna Monallize Duarte Moura,Neto, Iran Eduardo Lima,et al. Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods[J],2024,16(11).
APA	Oliveira Santos, Victor.,Guimaraes, Bruna Monallize Duarte Moura.,Neto, Iran Eduardo Lima.,de Souza Filho, Francisco de Assis.,Costa Rocha, Paulo Alexandre.,...&Gharabaghi, Bahram.(2024).Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods.REMOTE SENSING,16(11).
MLA	Oliveira Santos, Victor,et al."Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods".REMOTE SENSING 16.11(2024).