Application and recent progress of inland water monitoring using remote sensing techniques

Cao, Qi; Yu, Gongliang

doi:10.1007/s10661-022-10690-9

Cited by 38 publications

(13 citation statements)

References 87 publications

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“…In this context, the demand for near-real-time analysis of water quality has intensified, driven by the need for timely and actionable data. Optical remote sensing technologies have emerged as powerful tools in this endeavour, facilitating the transition from traditional laboratory-based analyses to dynamic, spatially explicit monitoring approaches [ 3 ]. This evolution has been enabled by a diverse array of platforms, including satellites [ 3 ], aircraft [ 4 ], drones [ 5 ], and increasingly, smartphones [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Optical remote sensing technologies have emerged as powerful tools in this endeavour, facilitating the transition from traditional laboratory-based analyses to dynamic, spatially explicit monitoring approaches [ 3 ]. This evolution has been enabled by a diverse array of platforms, including satellites [ 3 ], aircraft [ 4 ], drones [ 5 ], and increasingly, smartphones [ 6 ]. The ubiquity of smartphones and their sophisticated imaging capabilities make them particularly promising for democratizing environmental monitoring efforts, empowering citizens to actively engage in data collection and analysis.…”

Section: Introductionmentioning

confidence: 99%

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Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Mahama,

Salama

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Mahama,

Salama

2024

Heliyon

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“…The complex interactions between physical, chemical and biological processes in surface waters lead to significant challenges for in situ monitoring and often limit the ability to adequately capture the dynamics of aquatic systems and to understand their status, functioning and response to pressures. In this context, the use of remote sensing allows for wide spatial coverage and regular monitoring frequency, providing information on water conditions, bottom properties and the presence and abundance of aquatic plants, distinguishing them into different association types; this complements traditional in situ measurements [17][18][19][20][21][22]. The reviews reported in [23,24] highlighted the significant increase in remote sensing studies of inland water quality, due both to improved access to Earth Observation (EO) data and increasing computational capabilities.…”

Section: Introductionmentioning

confidence: 99%

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

Fabbretto,

Bresciani,

Pellegrino

et al. 2024

Remote Sensing

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This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

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“…Most applications of remote sensing monitoring technology and spectral imaging in the field of water environment monitoring can be summarized in three steps: remote sensing data acquisition, data processing and inversion model construction, and model analysis and application. The available data sources for remote sensing of water quality retrieval are usually multispectral and hyperspectral remote sensors [5] categorized by the spectral resolution of the sensors, carried by the spaceborne, airborne, and portable and groundbased load platforms [6]. Multispectral data available for remote sensing water quality retrieval typically have 3-10 bands.…”

Section: Introductionmentioning

confidence: 99%

“…Higher spectral resolution data have a large number of bands that can be precisely and optimally chosen for developing inversion models of water quality parameters to differentiate the spectral differences in multispectral data, greatly enhancing the accuracy of inversion algorithms [12][13][14]. Among the four spectral sensor platforms for the water quality monitoring, the portable and ground-based spectrometer [4] is less flexible and more labor-intensive; the airborne spectrometer [5,6] is flexible and has high spatial resolution, but the observation area is small; and the satellitebased spectrometer [8] has low imagery cost and is suitable for large-scale monitoring, but it has the disadvantages of low spatial resolution, poor timeliness, and long revisit cycle.…”

Section: Introductionmentioning

confidence: 99%

Research on the Characteristic Spectral Band Determination for Water Quality Parameters Retrieval Based on Satellite Hyperspectral Data

Xia,

Lu,

et al. 2023

Remote Sensing

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Hyperspectral remote sensing technology has been widely used in water quality monitoring. However, while it provides more detailed spectral information for water quality monitoring, it also gives rise to issues such as data redundancy, complex data processing, and low spatial resolution. In this study, a novel approach was proposed to determine the characteristic spectral band of water quality parameters based on satellite hyperspectral data, aiming to improve data utilization of hyperspectral data and to achieve the same precision monitoring of multispectral data. This paper first introduces the data matching method of satellite hyperspectral data and water quality based on space–time information for guidance in collecting research data. Secondly, the customizable and fixed spectral bands of the existing multispectral camera products were studied and used for the preprocessing of hyperspectral data. Then, the determination approach of characteristic spectral bands of water quality parameters is proposed based on the correlation between the reflectance of different bands and regression modeling. Next, the model performance for retrieval of various water quality parameters was compared between the typical empirical method and artificial neural network (ANN) method of different spectral band sets with different band numbers. Finally, taking the adjusted determination coefficient R2¯ as an evaluation index for the models, the results show that the ANN method has obvious advantages over the empirical method, and band set providing more band options improves the model performance. There is an optimal band number for the characteristic spectral bands of water quality parameters. For permanganate index (CODMn), dissolved oxygen (DO), and conductivity (EC), the R2¯ of the optimal ANN model with three bands can reach about 0.68, 0.43, and 0.49, respectively, whose mean absolute percentage error (MAPE) values are 14.02%, 16.26%, and 17.52%, respectively. This paper provides technical guidance for efficient utilization of hyperspectral data by determination of characteristic spectral bands, the theoretical basis for customization of multispectral cameras, and the subsequent water quality monitoring through remote sensing using a multispectral drone.

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Application and recent progress of inland water monitoring using remote sensing techniques

Cited by 38 publications

References 87 publications

Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

Research on the Characteristic Spectral Band Determination for Water Quality Parameters Retrieval Based on Satellite Hyperspectral Data

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