Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

Liu, Weihua; Wang, Siyuan; Yang, Ruixia; Ma, Yao; Shen, Ming; You, Yanan; Hai, Kai; Baqa, Muhammad Fahad

doi:10.3390/rs11243010

Cited by 7 publications

(3 citation statements)

References 59 publications

(82 reference statements)

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“…A typical spectral index used in water quality remote sensing inversion is the doubleband combination, which can remove noise interference, emphasize the spectral features of water quality parameters, and substantially increase the accuracy of the water quality inversion model [47,48]. In this study, any two of the five single bands were handled using band sum, band difference, and band ratio, resulting in 45 double band combinations (Table 3).…”

Section: Methods 231 Calculation Of Spectral Indexmentioning

confidence: 99%

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

et al. 2022

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Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (CODMn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and CODMn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R2 values of the best inversion models for Chl-a, TN, TP, and CODMn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L−1, 0.189 mg L−1, 0.053 mg L−1, and 0.767 mg L−1, and the mean absolute errors were 1.272 μg L−1, 0.632 mg L−1, 0.045 mg L−1, and 0.674 mg L−1, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring.

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Section: Methods 231 Calculation Of Spectral Indexmentioning

confidence: 99%

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

et al. 2022

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“…The Pearson correlation coefficient method can determine the statistical linear correlation between two variables [25][26][27]. We here calculated the correlation coefficient between the spectral reflectance value of each image and the water quality parameter value to identify the most sensitive water body index to water quality parameters.…”

Section: Correlation Analysismentioning

confidence: 99%

Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Yan

Wang

et al. 2023

Sustainability

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Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

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“…In addition, advances in cloud computing provided by the Google Earth Engine (GEE) geospatial platform allow conducting robust spatial and temporal analysis based on a ready-to-use, pre-processed data catalog (Gorelick et al, 2017). The use of Sentinel-2A/B satellite images, in conjunction with cloud computing capabilities provided by GEE, has been demonstrated to be an interesting alternative for assessing OAC in large environmental studies (Bustamante et al, 2009;Caballero et al, 2019;Dörnhöfer & Oppelt, 2016;Fassoni-Andrade et al, 2017;Fraga et al, 2020;Garg et al, 2020;Liu et al, 2019;Potes et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Predictive model for monitoring water turbidity in a subtropical lagoon using Sentinel-2A/B MSI images

Caballero

Guedes

Fraga

et al. 2023

RBRH

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Ensuring prompt and effective water quality monitoring is increasingly important. Remote sensing has been shown to be an effective tool for simplifying and speeding up this process. The aim of this study is to develop an empirical model to map the spatial and temporal dynamics of turbidity in Mirim Lagoon, located in southern Brazil. To achieve this, Sentinel-2A/B MSI sensor data were combined with turbidity data collected in situ. The model was applied to monthly images (with cloud cover ≤ 20%) in 2019 and 2020 using the Google Earth Engine (GEE) platform. Mean turbidity values in the lagoon did not vary significantly, remaining between 30 and 75 NTU overall. However, there were differences in turbidity levels between the northern and southern regions of the lagoon in some months of the investigated years. By applying this methodology and analyzing the results, we were able to better understand the behavior of turbidity throughout the lagoon and gain insights into the quality of this important freshwater source.

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Remote Sensing Retrieval of Turbidity in Alpine Rivers based on high Spatial Resolution Satellites

Cited by 7 publications

References 59 publications

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Predictive model for monitoring water turbidity in a subtropical lagoon using Sentinel-2A/B MSI images

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