Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods
Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To add...
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MDPI AG
2025-06-01
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| author | Hongran Li Nuo Wang Zixuan Du Deyu Huang Mengjie Shi Zhaoman Zhong Dongqing Yuan |
| author_facet | Hongran Li Nuo Wang Zixuan Du Deyu Huang Mengjie Shi Zhaoman Zhong Dongqing Yuan |
| author_sort | Hongran Li |
| collection | DOAJ |
| description | Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an <i>R</i><sup>2</sup> of 0.9938 for TN, a mean absolute error (<i>MAE</i>) of 0.0728 for DO, a root mean square error (<i>RMSE</i>) of 0.3881 for total TSS, and a mean absolute percentage error (<i>MAPE</i>) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring. |
| format | Article |
| id | doaj-art-2b85dc34dcc24733835010fd7b21ec39 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2b85dc34dcc24733835010fd7b21ec392025-08-20T02:36:33ZengMDPI AGRemote Sensing2072-42922025-06-011713219110.3390/rs17132191Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning MethodsHongran Li0Nuo Wang1Zixuan Du2Deyu Huang3Mengjie Shi4Zhaoman Zhong5Dongqing Yuan6School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaSchool of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaWater quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an <i>R</i><sup>2</sup> of 0.9938 for TN, a mean absolute error (<i>MAE</i>) of 0.0728 for DO, a root mean square error (<i>RMSE</i>) of 0.3881 for total TSS, and a mean absolute percentage error (<i>MAPE</i>) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring.https://www.mdpi.com/2072-4292/17/13/2191UAV hyperspectral imagingmulti-parameter regressionecological heterogeneitywater quality monitoringtransformerLSTM |
| spellingShingle | Hongran Li Nuo Wang Zixuan Du Deyu Huang Mengjie Shi Zhaoman Zhong Dongqing Yuan Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods Remote Sensing UAV hyperspectral imaging multi-parameter regression ecological heterogeneity water quality monitoring transformer LSTM |
| title | Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods |
| title_full | Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods |
| title_fullStr | Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods |
| title_full_unstemmed | Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods |
| title_short | Multi-Parameter Water Quality Inversion in Heterogeneous Inland Waters Using UAV-Based Hyperspectral Data and Deep Learning Methods |
| title_sort | multi parameter water quality inversion in heterogeneous inland waters using uav based hyperspectral data and deep learning methods |
| topic | UAV hyperspectral imaging multi-parameter regression ecological heterogeneity water quality monitoring transformer LSTM |
| url | https://www.mdpi.com/2072-4292/17/13/2191 |
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