Among rivers, small- and medium-sized rivers are often ignored in daily water quality monitoring. However, these rivers are places where water pollution often occurs [1
], especially in the cities of developing countries [2
]. The most common pollutants are nitrogen (N) and phosphorus (P), which may lead to algal blooms [4
]. In addition, for a single river, water quality is usually different in different watersheds and it constantly changes over time. Therefore, a comprehensive, fine-scale and high-frequency monitoring method is needed to improve urban river water quality monitoring programs.
Remote sensing technology can rapidly monitor a large area. Recently, there have been a large number of studies on remote sensing water quality monitoring [7
]. The purpose of remote sensing water quality inversion is to establish an accurate relationship between image features and water quality. A large quantity of these studies focused on Case 1 waters and Case 2 waters, such as seas, large rivers, and lakes [8
]. Moreover, these studies were mainly based on images of specific bands [11
]. However, the water quality and optical properties of different urban rivers may vary greatly [12
]. Thus, considering the complexity of urban water systems, it is necessary to extract suitable features and establish a universal inversion model for key water quality parameters.
Spectral features are the most important features in water quality remote sensing. Different constituents in water have different optical properties. Spectral feature analysis aims to extract the key bands that have a high correlation with the water quality parameters. Dimensionality reduction [13
], clustering [14
], and ranking [15
] are the most commonly used methods for extracting the feature bands. In addition, regarding the water quality parameters, the correlation between the spectral features and water quality parameters also needs to be analyzed [16
]. Bandwidth is another important spectral feature that needs to be determined for water quality inversion and sensor production [17
]. The sensitive bands of water quality parameters have specific wavelength ranges. The information from other bands will interfere with the inversion [18
]. Therefore, considering the many aspects of spectral analysis, a new process needs to be proposed.
Remote sensing imagery has the advantage of capturing spatial properties at fine scales [19
]. However, the spatial features around rivers and lakes have been ignored by many studies. In fact, water quality in the city is always affected by the surrounding environment [20
]. The water functional zone is one of the spatial factors that can affect the water quality [21
]. For example, use in industry, use in agriculture, and use in the landscape can determine sewage discharge into the river. The hydrodynamic properties of a river or stream affect its capacity to self-clean [22
]. Streamflow and discharge rates are two of the important factors. The channel morphology also affects this capacity because it is affected by streamflow and discharge patterns [23
]. Thus, in addition to the spectral features of the water, water quality remote sensing should also include the spatial features of the stream and the surrounding environment.
Inversion model construction is the key step in establishing mapping relationships between spectral features and water quality parameters. The most frequently used methods are empirical estimation methods [24
] and bio-optical estimation methods [28
]. The empirical estimation method is based on sampling data, identifying relationships between water quality parameters and spectral reflectance values by means of regression-related efforts [30
]. One limitation of empirical methods is geographic transferability, which means that the established models are usually quite accurate within the sampling areas but are not suitable in other areas. Due to the high complexity of water bodies, researchers developed bio-optical modeling techniques to alleviate or even overcome the problem of regional transferability in empirical methods [31
]. However, based on the principle of light–water interaction, these models require detailed spectral information regarding optically relevant water components [9
]. Moreover, for non-optically relevant water quality constituents, such as nitrogen (N) and phosphorus (P), the bio-optical estimation method is more difficult to establish. Therefore, both estimation methods have advantages and disadvantages.
Machine-learning inversion models have been widely used in recent years and are strictly classified as empirical estimation methods [33
]. In previous studies, machine-learning methods, such as support vector regression (SVR) [34
] and neural networks (NN) [37
], were often used due to their capacity to capture complex statistical trends between water quality parameters and spectral reflectance values. Researchers have shown that machine-learning methods provide the best overall accuracy for almost all water quality parameters compared to other methods [9
]. The ensemble learning method can aggregate multiple machine-learning methods to improve classification accuracy and robustness [40
]. For water quality inversion, ensemble learning has shown great improvement with regard to the inversion results [41
]. Given these findings, in urban areas, using the ensemble learning method may also improve the inversion results for complex urban river networks.
The quality of the acquired remote sensing data restricts the accuracy of water quality inversion. To date, multispectral images from satellites and aircraft are the main data sources [8
]. These platforms usually observe the ground at a high altitude. Thus, these platforms can observe large-scale areas in a short timeframe. However, for urban river networks, the width of small- and medium-sized rivers usually ranges from less than 10 m to approximately 30 m. Considering the resolution and the atmospheric error of satellite and aircraft images, they may not be suitable choices to perform the accurate observation of these rivers. Furthermore, satellite bands are mainly fixed, which means that they cannot be adjusted according to the water quality situation.
Unmanned aerial vehicle (UAV) remote sensing has the characteristics of flexibility, adjustability, efficiency, and high resolution [42
] and is an effective solution for the remote sensing of small- and medium-sized rivers. In the field of water quality monitoring, UAV remote sensing can fill the gap between ground monitoring and high-altitude monitoring. As a newly developing technology, some research has been performed so far. UAV remote sensing was used to infer the water quality in a single urban river, coastal regions, or reservoirs [43
]. Furthermore, UAV remote sensing was also used to detect black-odor river basins in medium-sized rivers [46
]. Although these studies have achieved satisfactory results, they are still preliminary studies before large-scale application, which means that the application framework of UAV-borne water quality remote sensing has not been established. Aiming to make UAV remote sensing a routine monitoring method to support traditional water quality monitoring methods in cities, many improvements need to be achieved.
We propose a spectral- and spatial-integrated ensemble learning method for urban river network water quality grading to meet the demand of comprehensive domain and high-frequency fine-scale monitoring. Based on a representative in situ sampling dataset, the spectral bands that are suitable for analyzing urban river quality in the study area of this paper are extracted. We select the water functional zone and stream order as the spatial features and prove that they can improve the grading accuracy. The ensemble learning model is established and evaluated using the selected features as input. Finally, we present a process for UAV-borne water quality remote sensing applications.
Multispectral remote sensing technology has been applied to monitor water quality for several decades. Researchers have proposed many effective methods to infer the water quality from spectral reflectance values or multispectral images. However, for complex urban river networks, it is still a new application field. In fact, a high-efficiency, high-frequency, and whole-basin water quality monitoring method is an urgent need for urban rivers because of the complexity, diversity, and variability of urban rivers. Aiming to address the practical need, our research proposed a spectral- and spatial-integrated ensemble learning method for urban river network water quality grading. The experiment in this paper proved that our method is an improvement for urban river water quality remote sensing.
4.1. Dataset Construction
In situ data collection is a key step before water quality remote sensing. To construct the water quality parameter inversion models, researchers collected their datasets. Usually, studies have focused on one or several rivers or lakes [46
]. Therefore, the data collection areas were usually among these rivers or lakes. Additionally, water and spectrum sampling are works that require considerable labor power and time. This means that the samples are limited. Furthermore, these datasets mainly include spectrum and water quality data. However, water pollution is affected by many factors, such as the surrounding environment and climate. Remote sensing is a technology that can analyze spatial properties accurately. Thus, it is meaningful and feasible to add spatial property analysis to remote sensing water quality monitoring.
For river networks in large cities, it is almost impossible to cover all rivers and all environmental situations. Thus, collecting representative data is significant to extract the main features for urban water quality remote sensing. In this paper, the water functional zone, stream order, water quality diversity, and seasons were taken into consideration. This is a requirement we proposed for urban water quality remote sensing dataset collection that can be a reference for sampling. In the future, more samples will be added to our dataset. Furthermore, other environmental features will be considered. For example, the recent precipitation affects the discharge, which can directly worsen the water quality. Other features, such as the distribution of discharge outlets and isolation fences in the river, also affect the water quality. Thus, the dataset can be expanded and more reasonable.
4.2. Feature Analysis
Spectral feature analysis is an important step for remote sensing that aims to establish a connection between spectrum and observation targets. To avoid band redundancy, we chose to select feature bands in advance and use a multispectral camera. The band-selection methods usually only consider the spectrum feature. For environmental observations, it is also important to analyze the correlation between bands and the target. For multispectral cameras, bandwidth is another problem that needs to be considered, because bandwidth can affect the observation accuracy and image quality. Based on the aims and problems above, a feature analysis process was proposed. The features selected in this paper are proven effective according to the modeling and application experiment results. This analysis method also has some areas to improve in the future. Other methods, such as dimensionality reduction [13
], clustering [14
], and ranking [15
], can be used to select the center wavelength, which may extract more potential bands. Other indexes can also be used to calculate the best bandwidth range. For example, camera performance is another aspect that can be considered.
Spatial features are a new and significant type of feature used in the model for remote sensing water quality monitoring in this paper. The water’s functional zone [21
] and hydrodynamics [23
] are two key factors that affect the water’s quality. We proved that the spatial features improve the grading accuracy. Furthermore, despite these two factors, other spatial factors or even environmental factors may also affect water quality, such as climate. Thus, which spatial or environmental features are the main features that can affect the inversion model is a topic worth studying.
4.3. Water Quality Grading Model
Water quality inversion modeling is the most important and difficult step in water quality remote sensing. TP and NH3-N are two of the most significant water pollutants that need to be monitored. In future studies, the models for grading other water quality parameters will also be established. In this paper, to meet the actual needs of municipal water affairs, a model was established to grade TP and NH3-N. Three strategies were used to improve the grading accuracy: adding spatial features, using ensemble learning, and using hard-soft fused voting. From the five-fold cross validation results, it can be clearly seen that these three strategies can improve the accuracy and generalization ability. Compared to the traditional machine-learning models using only spectral features, the macro precision, macro recall, and macro F1 scores improved from approximately 0.30 to approximately 0.60 when using our model. However, the evaluation indexes of the model show that the model can still be improved. We hope the accuracy can be improved to 0.80. By introducing the concentration of optically relevant components and other features that affect the water quality, the model can have a higher interpretability and accuracy. The inversion model can be improved using better machine-learning methods and voting methods. There are also limitations to this model. The machine-learning method itself also has weaknesses. If water quality measurement errors occur before establishing the model, this can have big consequences. Therefore, improving the robustness of the model is significant. In addition, not all regions have a strictly divided water functional zone. The spatial features used in the model need further improvement.
4.4. Application Process
To date, many researchers have performed numerous experiments with images collected with UAVs [42
], aircraft [73
], or satellites [74
]. We hope to make remote sensing a routine monitoring method to support traditional water quality monitoring methods. Aiming to achieve this goal, we proposed a complete a feasible application process. The results prove that this process can be used for UAV-borne remote sensing water quality monitoring. Based on this process, some improvements can be made. For the data collection step, the flight route can be more targeted. For example, a long strip river may use a patrol route. The image correction method followed the calibration and correction method of the satellite multispectral camera. However, low-altitude remote sensing may require a more appropriate sensor-calibration and image-correction method. The water part image extraction step can use machine-learning to clip the image automatically. Furthermore, because of the object shadows on the shore, the grading results of some river parts appear to be a grade jump. How to deal with shadows is also a problem that needs to be considered. Thus, more work needs to be done in the future to achieve routine monitoring using remote sensing technology.
Whole-basin and high-frequency monitoring is an urgent need for urban river network water quality monitoring. Remote sensing is an appropriate technology to address this problem. Aiming to accurately invert the water quality situation, it is necessary to extract suitable features and establish a more universal model for key water quality parameters.
We proposed a spectral- and spatial-integrated ensemble learning method for urban river network water quality grading. The method includes three parts: an in situ sampling method and a practical application process, a feature analysis process, and spectral- and spatial-feature integrated water quality grading modeling. The sampling method aims to provide a reference to handle the problem of numerous rivers and complex environmental influences. Based on the sampling condition requirements, a representative dataset for urban water quality remote sensing can be collected. For multispectral cameras used for water quality remote sensing, we provide a process to select the bands, which also includes bandwidth analysis. Spatial features are the new features that were added in our study. The spatial features proved that they can improve the water quality grading accuracy. An ensemble learning model with a hard-soft fused voting method was proposed by using both spectral and spatial features. The new model can improve the TP and NH3-N grading macro precision, macro recall, and macro F1 scores from approximately 0.30 to approximately 0.60, compared to the traditional machine-learning models, using only spectral features. Finally, an application process was proposed that was proven feasible. The precision of both the TP and NH3-N grading results was 0.67.
The method proposed in this paper can extract suitable features and build a relationship model between the features and water quality. This model improves the accuracy of the water quality inversion model for complex urban river networks, which means it is more universal than other existing models. Based on UAV-borne multispectral remote sensing technology, our method can effectively deal with the high-efficiency, high-frequency, and whole-basin water quality monitoring problem. However, as also recommended in the discussion, further studies are needed to improve the grading result’s accuracy. In the future, we hope to make remote sensing a routine monitoring method to support traditional water quality monitoring methods.