Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage DOI
JongCheol Pyo, Kyung Hwa Cho, Kyunghyun Kim

et al.

Water Research, Journal Year: 2021, Volume and Issue: 203, P. 117483 - 117483

Published: July 31, 2021

Language: Английский

A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes DOI
Zhigang Cao, Ronghua Ma, Hongtao Duan

et al.

Remote Sensing of Environment, Journal Year: 2020, Volume and Issue: 248, P. 111974 - 111974

Published: July 15, 2020

Language: Английский

Citations

305
A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges DOI Creative Commons

Haibo Yang,

Jialin Kong,

Huihui Hu

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(8), P. 1770 - 1770

Published: April 7, 2022

Water pollution has become one of the most serious issues threatening water environments, as a resource and human health. The urgent effective measures rely on dynamic accurate quality monitoring large scale. Due to their temporal spatial advantages, remote sensing technologies have been widely used retrieve data. With development hyper-spectral sensors, unmanned aerial vehicles (UAV) artificial intelligence, there significant advancement in remotely sensed retrieval owing various data availabilities methodologies. This article presents application for retrieval, mainly discusses research progress terms sources modes. In particular, we summarize some algorithms several specific variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic (CDOM), chemical oxygen demand (COD), nitrogen (TN) phosphorus (TP). We also discuss challenges atmospheric correction, resolution, model applicability domains spatial, complexity. Finally, propose possible solutions these challenges. review can provide detailed references future retrieval.

Language: Английский

Citations

204
Transfer learning in environmental remote sensing DOI Creative Commons
Yuchi Ma, Shuo Chen, Stefano Ermon

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 301, P. 113924 - 113924

Published: Nov. 28, 2023

Language: Английский

Citations

125
Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery DOI
Chao Niu, Kun Tan, Xiuping Jia

et al.

Environmental Pollution, Journal Year: 2021, Volume and Issue: 286, P. 117534 - 117534

Published: June 6, 2021

Language: Английский

Citations

104
Using convolutional neural network for predicting cyanobacteria concentrations in river water DOI
JongCheol Pyo,

Lan Joo Park,

Yakov Pachepsky

et al.

Water Research, Journal Year: 2020, Volume and Issue: 186, P. 116349 - 116349

Published: Aug. 26, 2020

Language: Английский

Citations

87
Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method DOI
Xingfeng Chen, Gerrit de Leeuw, Antti Arola

et al.

Remote Sensing of Environment, Journal Year: 2020, Volume and Issue: 249, P. 112006 - 112006

Published: July 31, 2020

Language: Английский

Citations

86
Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China DOI Creative Commons

Qun’ou Jiang,

Xu Lidan,

Siyang Sun

et al.

Ecological Indicators, Journal Year: 2021, Volume and Issue: 124, P. 107356 - 107356

Published: Feb. 3, 2021

Monitoring the water pollution level in real time is most critical issue for protecting quality of reservoirs. Due to restrictions on flight areas Unmanned Arial Vehicles (UAV), four sensitive regions with area 1–2 km2 were first selected this study based spatial distribution total nitrogen (TN) concentration changes estimated by Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso (Lasso), Elastic Net (EN), Linear (LR), Decision Tree (DTR), K Neighbors (KNR), Random Forest (RFR), Extra Trees (ETR), AdaBoost (ABR) and Gradient Boosting (GBR) compared construct a more accurate retrieval model using UAV hyper spectral ground monitoring TN was after process dimensionality reduction compressed denoing. Finally, heterogeneity analyzed Miyun reservoir. The results demonstrated that among tested best suitable construction basis data, its absolute squared error 0.000065. showed highest within Bulaotun village Houbajiazhuang village, while it relatively low Chao river dam Bai dam. Additionally, no significant differences regarding concentrations shown single except Houbajia which indicated reservoir stable changed small interval. These conclusions can provide scientific references management

Language: Английский

Citations

69
A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir DOI
Yongeun Park,

Han Kyu Lee,

Jae-Ki Shin

et al.

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 288, P. 112415 - 112415

Published: March 26, 2021

Language: Английский

Citations

62
A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods DOI
Diego Gómez, Pablo Salvador, J. Sanz

et al.

Environmental Pollution, Journal Year: 2021, Volume and Issue: 286, P. 117489 - 117489

Published: May 31, 2021

Language: Английский

Citations

60
Random forest: An optimal chlorophyll-a algorithm for optically complex inland water suffering atmospheric correction uncertainties DOI
Ming Shen, Juhua Luo, Zhigang Cao

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 615, P. 128685 - 128685

Published: Nov. 9, 2022

Language: Английский

Citations

45