Use of Hyperspectral Remote Sensing to Estimate Water Quality

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

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Hyperspectral remote sensing for estimating coastal water quality: case study on the coast of Black Sea, Romania

10.1117/12.2067415 ◽

2014 ◽

Cited By ~ 3

Author(s):

S. G. Ghezehegn ◽

Peters Steef ◽

Annelies Hommersom ◽

De Reus Nils ◽

Oana Culcea ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Black Sea ◽

Coastal Water ◽

Hyperspectral Remote Sensing ◽

Coastal Water Quality

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Hyperspectral Remote Sensing of Nearshore Water Quality

10.1007/978-3-319-46949-2 ◽

2017 ◽

Author(s):

Sima Bagheri

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Hyperspectral Remote Sensing ◽

Nearshore Water

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Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

Remote Sensing ◽

10.3390/rs12020336 ◽

2020 ◽

Vol 12 (2) ◽

pp. 336 ◽

Cited By ~ 3

Author(s):

Yishan Zhang ◽

Lun Wu ◽

Huazhong Ren ◽

Yu Liu ◽

Yongqian Zheng ◽

...

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Water Quality ◽

Artificial Neural Networks ◽

Oxygen Demand ◽

Quality Parameters ◽

Hyperspectral Remote Sensing ◽

Water Quality Parameters ◽

Proposed Model ◽

Selection Of

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

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