An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

Li, Lan; Gu, Meng; Gong, Chongshui; Hu, Yong; Wang, Xinhui; Yang, Zhe; He, Zhiyuan

doi:10.1016/j.scitotenv.2023.163389

Cited by 21 publications

(8 citation statements)

References 50 publications

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“…Compared to the traditional on-site monitoring of total phosphorus and ammonia nitrogen, remote sensing technology provides a fast and wide-ranging means of obtaining the spatial and temporal distribution characteristics of water quality parameters. Numerous researchers have utilized different sensor data, such as MODIS and GOCI, to conduct remote sensing inversion studies on water quality parameters, including total phosphorus, total nitrogen, and ammonia nitrogen [6][7][8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Shi,

Qiu,

et al. 2024

Water

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Remote sensing technology plays a crucial role in the rapid and wide-scale monitoring of water quality, which is of great significance for water pollution prevention and control. In this study, the downstream and nearshore areas of the Huaihe River Basin were selected as the study area. By utilizing spectral information from standard solution measurements in the laboratory and in situ water quality data matched with satellite spatiotemporal data, inversion models for total phosphorus (TP) and ammonia nitrogen (NH3-N) water quality parameters were developed. The validation results using field measurements demonstrated that the inversion models performed well, with coefficients of determination (R2) of 0.7302 and 0.8024 and root mean square errors of 0.02614 mg/L and 0.0368 mg/L for total phosphorus and ammonia nitrogen, respectively. By applying the models to Sentinel-2 satellite images from 2022, the temporal and spatial distribution characteristics of total phosphorus and ammonia nitrogen concentrations in the study area were obtained. The ammonia nitrogen concentration ranged from 0.05 to 0.30 mg/L, while the total phosphorus concentration ranged from 0.10 to 0.40 mg/L. Overall, the distribution appeared to be stable. The southern region of the Guan River estuary showed slightly higher water quality parameter concentrations compared to the northern region, while the North Jiangsu Irrigation Main Canal estuary was affected by the dilution of river water, resulting in lower concentrations in the estuarine area.

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Section: Introductionmentioning

confidence: 99%

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Shi,

Qiu,

et al. 2024

Water

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“…However, determining the link between these parameters and the recorded reflectance is challenging, especially for non-optically active parameters, such as DO and EC. These parameters only minimally influence the reflectance measured by using remote sensing sensors [32][33][34]. To address this, machine learning algorithms like support vector regression (SVR) [2,15,19,35], random forests [15,35], and artificial neural networks [19,35] have been proposed to model both optically and non-optically active water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Water Quality Parameters in Small Rivers Using SuperDove Imagery

Vatitsi,

Siachalou,

Latinopoulos

et al. 2024

Water

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Freshwater ecosystems provide an array of provisioning, regulating/maintenance, and cultural ecosystem services. Despite their crucial role, freshwater ecosystems are exceptionally vulnerable due to changes driven by both natural and human factors. Water quality is essential for assessing the condition and ecological health of freshwater ecosystems, and its evaluation involves various water quality parameters. Remote sensing has become an efficient approach for retrieving and mapping these parameters, even in optically complex waters such as small rivers. This study specifically focuses on modelling two non-optically active water quality parameters, dissolved oxygen (DO) and electrical conductivity (EC), by integrating 3 m PlanetScope satellite imagery with data from real-time in situ remote monitoring sensors across two small rivers in Thrace, Northeast Greece. We employed three different experimental setups using a support vector regression (SVR) algorithm: ‘Multi-seasonal by Individual Sensor’ (M-I-S) for individual sensor analysis across two seasons, ‘Multi-seasonal—All Sensors’ (M-A-S) integrating data across all seasons and sensors, and ‘Seasonal—All Sensors’ (S-A-S) focusing on per-season sensor data. The models incorporating multiple seasons and all in situ sensors resulted in R2 values of 0.549 and 0.657 for DO and EC, respectively. A multi-seasonal approach per in situ sensor resulted in R2 values of 0.885 for DO and 0.849 for EC. Meanwhile, the seasonal approach, using all in situ sensors, achieved R2 values of 0.805 for DO and 0.911 for EC. These results underscore the significant potential of combining PlanetScope data and machine learning to model these parameters and monitor the condition of ecosystems over small river surfaces.

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“…Water temperature, color, transparency, and other physical properties are mainly measured in situ, and most of the chemical and biological properties such as the concentration of water components need to be analyzed in the laboratory. Such kind of method is inefficient and lacks details in time and space [4]. Although the development of water quality monitoring stations can provide almost continuous measurement data, the point-based data still fail to fully describe the spatial characteristics [5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

Gao,

Shangguan,

Sun

et al. 2024

Remote Sensing

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Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

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An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

Cited by 21 publications

References 50 publications

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Monitoring the Water Quality Distribution Characteristics in the Huaihe River Basin Based on the Sentinel-2 Satellite

Monitoring Water Quality Parameters in Small Rivers Using SuperDove Imagery

Estimation of Non-Optically Active Water Quality Parameters in Zhejiang Province Based on Machine Learning

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