Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

Sagan, Vasit; Peterson, Kyle T.; Maimaitijiang, Maitiniyazi; Sidike, Paheding; Sloan, John J.; Greeling, Benjamin A.; Maalouf, Samar; Adams, Craig D.

doi:10.1016/j.earscirev.2020.103187

Cited by 371 publications

(235 citation statements)

References 133 publications

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“…In the process of constructing the water quality parameter inversion model, each pixel of the satellite image reflects the spectral reflectance of a certain range, while the measured data is the precise value of a certain point. In order to establish the correspondence between them, it is necessary to match the two kinds of data [43]. We obtained the measured weekly data of four water quality parameters and water quality evaluation grade from 2013 to 2018 from six monitoring stations in Taihu Lake.…”

Section: Data Acquisitionmentioning

confidence: 99%

Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN

Huang

Wang

2020

IEEE Access

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Long-term dynamic monitoring of the water quality of freshwater resources is of great significance to the stable and orderly operation of human society. Most studies only use one of the measured data from the monitoring station and the remote sensing satellite data as the data source. However, a single data source will cause inaccuracy and incompatibility of the water quality monitoring results. Few studies start from practical applications to generate digital images of water quality changes. Furthermore, the performance of shallow neural networks in water quality monitoring is not often ideal. Considering the above problems, we proposed a long short-term memory network model (LSTM) to invert four key water parameters including pondus hydrogenii (PH), dissolved oxygen (DO), chemical oxygen demand (CODMn) and ammonia-nitrogen (NH3-H). Moreover, the model was applied to the satellite images of various periods to generate the inverted image of each water quality parameter. The proposed model has exhibited excellen t performance in the water quality ass essment of the project, with the coefficient of determination (R 2), the relative root-mean-square error (rRMSE), and the mean relative error (MRE) values of 0.83, 0.16, and 0.18, respectively. And the inverted images are also consistent with the official information. INDEX TERMS Remote sensing, water quality parameters, water quality monitoring, LSTM network.

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Section: Data Acquisitionmentioning

confidence: 99%

Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN

Huang

Wang

2020

IEEE Access

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“…Although the measurement is accurate, only discrete sample point data was acquired, and the change rule of large-scale water monitoring data cannot be obtained. This status will be improved by remote sensing technology (Sagan et al, 2020) and large-scale water monitoring data (Chawla et al, 2020;. For the evaluation of water areas with special pollutant types, the single-factor index evaluation method has the problem that the single-factor influence is too large, and the objectivity of the evaluation results cannot be guaranteed (Zhu, 2019).…”

Section: Water Heritagementioning

confidence: 99%

Systematic Method for Monitoring and Early-Warning of Garden Heritage Ontology Used in the Suzhou Classical Garden Heritage

Zhao

Wen

2020

Journal of Environmental Engineering and Landscape Management

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Taking garden heritage ontologies as the object, this paper explores monitoring and early-warning methods of heritage based on fuzzy cluster analysis. A monitoring and early-warning system for garden heritage ontologies is designed and consists of monitoring indexes, a monitoring program, monitoring data collection, application of an early-warning grading evaluation model and conclusion of early-warning grading. Taking the Suzhou classical garden heritage as an example, it can be concluded that the systematic method can integrate various qualitative and quantitative index values and collectively reflect the overall state of garden heritage ontologies as well as match a heritage monitoring ontology with an early warning grade by calculating the data similarity matrix, membership matrix, fuzzy similarity matrix, fuzzy equivalent matrix and cut matrix. Five kinds of heritage ontologies with a total of twenty-seven heritage monitoring indicators are applied in the model and then be matched with MATLAB software to obtain accurate early-warning results. When types of heritage ontology need to be expanded, the heritage is further refined, or the heritage is more comprehensive, this method is applicable.

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“…The physical, biological, and chemical characteristics that water must have to meet the needs of various types of use, including human consumption, irrigation, and recreation, define its quality (Sagan et al, 2020). This quality is related to the health of society and ecosystems, so its deterioration represents a risk to human health, food productivity and biodiversity (Ramadas;Samantaray, 2018).…”

Section: Remote Sensing and Monitoring And Water Quality Studiesmentioning

confidence: 99%

“…This quality is related to the health of society and ecosystems, so its deterioration represents a risk to human health, food productivity and biodiversity (Ramadas;Samantaray, 2018). However, access to clean water has become an increasingly critical issue worldwide, in view of population growth, changes in land use and occupation and the increase in the load of pollutants, both in rural and urban areas (Sagan et al, 2020).…”

Section: Remote Sensing and Monitoring And Water Quality Studiesmentioning

confidence: 99%

“…Some of these algae can cause damage to human life, from limiting the recreational use of lakes and coastal waters, due to changes in taste and smell, to some types of cancers and tumors. Water pollution and eutrophication are estimated to cause an economic loss of 6 to 16 billion dollars, in addition to causing more than 3 million deaths per year worldwide (Sagan et al, 2020).…”

Section: Remote Sensing and Monitoring And Water Quality Studiesmentioning

confidence: 99%

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Remote Sensing and complex urban systems: Contributions to the state of the art on the Heat Island and the monitoring of water quality

Quaresma

Oliveira

2020

Sustentabilidade

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The article starts from the premise that cities are constituted in complex systems, so that its study, through the systemic approach, encounters three difficulties. Among these, the availability of technological instrumentation applied to research and which favors the obtaining and analysis of reality data stands out. In this context, remote sensing techniques are inserted, which have been increasingly applied in urban studies. Thus, the present article aims to present the state of the art of the use of remote sensing in urban studies, having the phenomenon of the Heat Island and the monitoring of water quality as thematic focus. In methodological terms, this article is a narrative, non-systematic and qualitative review, carried out based on the analysis of the literature, from books and articles in international journals. It is expected that the present material will contribute to the updating of knowledge, as well as to highlight the importance of the topic addressed in the academic environment.

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Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

Cited by 371 publications

References 133 publications

Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN

Monitoring Water Quality Parameters of Taihu Lake Based on Remote Sensing Images and LSTM-RNN

Systematic Method for Monitoring and Early-Warning of Garden Heritage Ontology Used in the Suzhou Classical Garden Heritage

Remote Sensing and complex urban systems: Contributions to the state of the art on the Heat Island and the monitoring of water quality

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