Developing remote sensing methods for monitoring water quality of alpine rivers on the Tibetan Plateau

Wang, Siyuan; Ming, Shen; Liu, Weihua; Ma, Yao; Shi, Hao; Zhang, Jingting; Liu, Di

doi:10.1080/15481603.2022.2116078

Cited by 14 publications

(7 citation statements)

References 60 publications

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“…Therefore, it can be estimated from these optically active compounds. In recent decades, satellite remote-sensing data such as Landsat, and Sentinel data have been extensively used to estimate TP concentration in various rivers around the world [20,21].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Yang,

Feng,

Zhou

et al. 2024

Remote Sensing

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Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R2 = 0.71, MAPE = 15.78%) than that for the model without turbidity (R2 = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).

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Section: Introductionmentioning

confidence: 99%

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Yang,

Feng,

Zhou

et al. 2024

Remote Sensing

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“…Traditional monitoring methods based on manual sampling are costly and labor-intensive. In recent years, satellite images, such as MODIS, Landsat, and Sentinel, have emerged as a crucial means for detecting inland water quality parameters (WQPs) [2,3] due to their advantages of having a wide monitoring range [4], high efficiency, and low cost [5]. However, for non-optically active parameters such as the total phosphorus (TP) and permanganate index (COD Mn ), the spectral characteristics within the visible-shortwave infrared range are imprecise, resulting in a limited accuracy from traditional satellite sensors for WQP inversion [6].…”

Section: Introductionmentioning

confidence: 99%

“…Fractional order derivation (FOD) has been adopted to explore more subtle spectral details beyond integer orders [16,23]. Combining FOD and discrete wavelet transform (DWT) methods to denoise hyperspectral images has demonstrated improvements, as the R 2 for the total nitrogen (TN) concentration based on preprocessed reflectance notably surpassed that obtained using original reflectance (OR) data [23].…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

Liu,

2024

Remote Sensing

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Efficient monitoring of water quality parameters (WQPs) is crucial for environmental health. Drone hyperspectral images have offered the potential for the flexible and accurate retrieval of WQPs. However, a machine learning (ML)-based multi-process strategy for WQP inversion has yet to be established. Taking a typical urban river in Guangzhou city, China, as the study area, this paper proposes a machine learning-based strategy combining spectral preprocessing and ML regression models with ground truth WQP data. Fractional order derivation (FOD) and discrete wavelet transform (DWT) methods were used to explore potential spectral information. Then, multiple methods were applied to select sensitive features. Three modeling strategies were constructed for retrieving four WQPs, including the Secchi depth (SD), turbidity (TUB), total phosphorus (TP), and permanganate index (CODMn). The highest R2s were 0.68, 0.90, 0.70, and 0.96, respectively, with corresponding RMSEs of 13.73 cm, 6.50 NTU, 0.06 mg/L, and 0.20 mg/L. Decision tree regression (DTR) was found to have the potential with the best performance for the first three WQPs, and eXtreme Gradient Boosting Regression (XGBR) for the CODMn. Moreover, tailored feature selection methods emphasize the importance of fitting processing strategies for specific parameters. This study provides an effective framework for WQP inversion that combines spectra mining and extraction based on drone hyperspectral images, supporting water quality monitoring and management in urban rivers.

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“…Cao et al [9] built a ML model based on Landsat8 to estimate the Chl-a content in inland lakes. Wang et al [10] proposed several regression methods and physical models based on hyperspectral satellite data and field survey to monitor the turbidity, TN, TP, and total organic carbon in alpine rivers. Taking Zhanghe River (Hubei Province, China) as the research region of interest, Xiao et al [11] established retrieval models for Chl-a, TN, TP, and chemical oxygen demand (COD) separately using the traditional regression algorithm, ML algorithm, and stacked ML algorithm to compare the applicability and accuracy of different models.…”

Section: Introductionmentioning

confidence: 99%

Remote-sensing retrieval of total phosphorus in Tiande Lake by optimizing an XGBoost machine learning model

LI,

Kang,

Yan

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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Traditional models for total phosphorus (TP) retrieval from remote-sensing images generally show low accuracy. Additionally, parameter adjustment and selection of parameter combinations of machine learning (ML) models exert significant influences on the regressive prediction effect and model performance. To solve these problems, this research proposed an extreme gradient boosting (XGBoost) model optimized by Bayesian optimization (BO), that is, BO-XGB. The optimal parameters of the model are sought automatically from a small sample size through BO, which shortens the training time. Taking Tiande Lake in Zhengzhou City (Henan Province, China) as the research region of interest, a BO-XGB model for TP retrieval is established based on GF1-WFV satellite data and TP water-quality data. Moreover, the accuracy of the model is compared with those of retrieval models established based on other four ML methods, namely, the XGBoost model, k-nearest neighbors (KNN), multilayer perceptron (MLP) and random forest (RF). Compared with the other four models, the BO-XGB TP retrieval model demonstrates the highest accuracy, with coefficients of determination R2 , root mean square error (RMSE), and mean relative error (MRE) separately of 0.923, 2.15 × 10-3 mg/L, and 1.81%. Finally, GF1-WFV satellite data are adopted to retrieve the spatial distribution of the TP concentration in Tiande Lake. The results show optimizing the XGBoost model using BO can significantly improve retrieval accuracy and the BO-XGB algorithm is more suitable for retrieving the TP mass concentration in Tiande Lake. The findings have implications for the inverse modelling of non-optical water quality parameters such as total nitrogen(TN).

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Developing remote sensing methods for monitoring water quality of alpine rivers on the Tibetan Plateau

Cited by 14 publications

References 60 publications

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

Monitoring Total Phosphorus Concentration in the Middle Reaches of the Yangtze River Using Sentinel-2 Satellites

A Machine-Learning-Based Framework for Retrieving Water Quality Parameters in Urban Rivers Using UAV Hyperspectral Images

Remote-sensing retrieval of total phosphorus in Tiande Lake by optimizing an XGBoost machine learning model

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