Research on the Inversion of Chlorophyll-a Concentration in the Hong Kong Coastal Area Based on Convolutional Neural Networks DOI Creative Commons
Weidong Zhu, Shuai Liu, Kuifeng Luan

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 1119 - 1119

Published: July 3, 2024

Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations optically complex coastal waters presents significant challenge traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged power of five machine learning models, including convolutional neural network (CNN), to enhance inversion process near Hong Kong. The CNN model demonstrated superior performance with on-site validation, outperforming other four models (R2 = 0.810, RMSE 1.165 μg/L, MRE 35.578%). was estimate from images captured over study area April October 2022, resulting creation thematic map illustrating spatial distribution levels. indicated high northeast southwest areas Kong Island low southeast facing open sea. Analysis patch size effects on accuracy that 7 × 9 patches yielded most optimal results across tested sizes. Shapley additive explanations were provide post-hoc interpretations best-performing model, highlighting features B6, B12, B8 important during process. This can serve as reference developing invert quality parameters.

Language: Английский

Spatial patterns of water quality and remote sensing indices from UAV-based multispectral imagery across an irrigation pond DOI Creative Commons
Seok Min Hong, Barbara J. Morgan, Matthew Stocker

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(4), P. e42622 - e42622

Published: Feb. 1, 2025

Water quality of irrigation water is an essential factor for public safety and farm sustainability. Imaging surface sources from unmanned aerial vehicles (UAVs) has become important source information. variables (WQVs) in ponds have been shown to persistent spatial patterns. The objective this work was test the hypothesis that (a) patterns can be found reflectance remote sensing indices UAV-based multispectral imagery ponds, (b) those significantly correlate with WQVs. We utilized data sampling, in-situ sensing, imaging a commercial 4-ha pond Maryland. Seventeen were measured on permanent grid during season concurrently MicaSense RedEdge camera at five wavelengths. Twenty-four computed. Spatial determined using mean relative difference method. appeared reflect differences distances banks, closeness creek meeting pond, degree stagnancy, dominant wind directions, geese congregation site. High (>0.8) Spearman correlation coefficients turbidity, photosynthetic pigments, organic carbon water. These variables' had similarities AFAI, TCARI, TCI, MCARI. Patterns E. coli strongly correlated pattern red wavelength. Given high spatiotemporal variability WQVs determining useful design surveys or monitoring aspects quality.

Language: Английский

Citations

0
Comparative analysis of k-nearest neighbors distance metrics for retrieving coastal water quality based on concurrent in situ and satellite observations DOI
Bonyad Ahmadi, Mehdi Gholamalifard, Seyed Mahmoud Ghasempouri

et al.

Marine Pollution Bulletin, Journal Year: 2025, Volume and Issue: 214, P. 117816 - 117816

Published: March 13, 2025

Language: Английский

Citations

0
A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality DOI Creative Commons
Mir Talas Mahammad Diganta, Md Galal Uddin, Tomasz Dabrowski

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 957, P. 177180 - 177180

Published: Nov. 23, 2024

Language: Английский

Citations

3
Grazing intensity estimation in temperate typical grasslands of Inner Mongolia using machine learning models DOI Creative Commons

Jingru Su,

Hong Wang, Dingsheng Luo

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113318 - 113318

Published: March 1, 2025

Language: Английский

Citations

0
Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management DOI Creative Commons
Ying Deng, Yue Zhang,

Daiwei Pan

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4196 - 4196

Published: Nov. 11, 2024

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring management lake water quality. It critically evaluates performance various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, Hyperion, in assessing key quality parameters chlorophyll-a (Chl-a), turbidity, colored dissolved organic matter (CDOM). highlights specific advantages each platform, considering factors like spatial temporal resolution, spectral coverage, suitability these platforms different sizes characteristics. In addition to this paper explores application a wide range models, from traditional linear tree-based methods more advanced deep techniques convolutional neural networks (CNNs), recurrent (RNNs), generative adversarial (GANs). These are analyzed their ability handle complexities inherent data, high dimensionality, non-linear relationships, multispectral hyperspectral data. also discusses effectiveness predicting parameters, offering insights into most appropriate model–satellite combinations scenarios. Moreover, identifies challenges associated with data quality, model interpretability, integrating imagery models. emphasizes need advancements fusion techniques, improved generalizability, developing robust frameworks multi-source concludes by targeted recommendations future research, highlighting potential interdisciplinary collaborations enhance sustainable management.

Language: Английский

Citations

1
Research on the Inversion of Chlorophyll-a Concentration in the Hong Kong Coastal Area Based on Convolutional Neural Networks DOI Creative Commons
Weidong Zhu, Shuai Liu, Kuifeng Luan

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(7), P. 1119 - 1119

Published: July 3, 2024

Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations optically complex coastal waters presents significant challenge traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged power of five machine learning models, including convolutional neural network (CNN), to enhance inversion process near Hong Kong. The CNN model demonstrated superior performance with on-site validation, outperforming other four models (R2 = 0.810, RMSE 1.165 μg/L, MRE 35.578%). was estimate from images captured over study area April October 2022, resulting creation thematic map illustrating spatial distribution levels. indicated high northeast southwest areas Kong Island low southeast facing open sea. Analysis patch size effects on accuracy that 7 × 9 patches yielded most optimal results across tested sizes. Shapley additive explanations were provide post-hoc interpretations best-performing model, highlighting features B6, B12, B8 important during process. This can serve as reference developing invert quality parameters.

Language: Английский

Citations

0