Empirical relationships for monitoring water quality of lakes and reservoirs through multispectral images

Doña, Carolina; Sánchez, Juan Manuel; Caselles, Vicente; Domínguez, José Antonio; Camacho, Antonio

doi:10.1109/jstars.2014.2301295

Cited by 58 publications

(31 citation statements)

References 24 publications

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“…Within the two-band spectral indices for water, several variants of the NDWI were studied, including the Modification of the Normalized Difference Water Index (MNDWI) and the index proposed by Ángel-Martínez [37] (I_CEDEX) where CEDEX is the Centre for studies and experimentation on public works in Madrid, Spain. The approach proposed by Bustamante et al [38] based on the MIR band and the single-band threshold with the NIR used by [39] were tested. Both supervised and unsupervised classification methods using different spectral bands were also tested.…”

Section: Water Mapping Methodsmentioning

confidence: 99%

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

et al. 2016

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Abstract:The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.

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Section: Water Mapping Methodsmentioning

confidence: 99%

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

et al. 2016

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“…Retrieving properties such as water clarity; turbidity, and Total Suspended Solids (TSS) concentrations using earth observation imageries have been tackled in applied research studies worldwide (Kloiber et al 2002;Zhang, 2002;Bilge et al, 2003;He et al, 2008;Sravanthi et al, 2013;Dona et al, 2014; Dorji and Fearns 2016; Abayazid and El-Gamal 2017). In 2008, He et al presented water quality retrieval models with proven successful results for optical nitrogenous and phosphorous components.…”

Section: A Remote Sensing-based Water Quality Retrievalmentioning

confidence: 99%

“…Other parameters such as chlorophyll-a (chl-a) and Colored Dissolved Organic Matter (CDOM)) have also been covered in various studies (i.e. Brezonik et al, 2005;Thiemann and Kaufmann, 2000;Li et al, 2002;Dona et al, 2014).…”

Section: A Remote Sensing-based Water Quality Retrievalmentioning

confidence: 99%

Assessment of a Non-Optical Water Quality Property Using Space-based Imagery in Egyptian Coastal Lake

Abayazid

Eladawy

2019

International Journal of Advanced Network, Monitoring and Controls

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Progressive anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen-related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R2 0.79), then validated with winter and spring data (R2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.

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“…Other parameters such as chlorophyll-a (chl-a) and Colored Dissolved Organic Matter (CDOM)) have also been covered in various studies (e.g. [8] [11] [12] and [13]).…”

Section: Remote Sensing-based Water Quality Retrievalmentioning

confidence: 99%

Assessment of a Non-Optical Water Quality Property Using Space-Based Imagery in Egyptian Coastal Lake

Abayazid¹,

Eladawy²

2019

JWARP

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Progressively anthropogenic intrusion and increasing water demand necessitate frequent water quality monitoring for sustainability management. Unlike laborious, time consuming field-based measurements, remote sensing-based water quality retrieval proved promising to overcome difficulties with temporal and spatial coverage. However, remotely estimated water quality parameters are mostly related to visibility characteristic and optically active property of water. This study presents results of an investigated approach to derive oxygen-related water quality parameter, namely Dissolved Oxygen (DO), in a shallow inland water body from satellite imagery. The approach deduces DO levels based on interrelated optical properties that dictate oxygen consumption and release in waters. Comparative analysis of multiple regression algorithms was carried out, using various combinations of parameters; namely, Turbidity, Total Suspended Solids (TSS), Chlorophyll-a, and Temperature. To cover the wide range of conditions that is experienced by Edku coastal lake, ground truth measurements covering the four seasons were used with corresponding satellite imageries. While results show successful statistically significant correlation in certain combinations considered, yet optimal results were concluded with Turbidity and natural logarithm of temperature. The algorithm model was developed with summer and fall data (R 2 0.79), then validated with winter and spring data (R 2 0.67). Retrieved DO concentrations highlighted the variability in pollution degree and zonation nature within that coastal lake, as related to boundary interactions and irregularity in flow dynamics within. The approach presented in this study encourages expanded applications with space-based earth observation products for exploring non-detectable water quality parameters that are interlinked with optically active properties in water.

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Empirical relationships for monitoring water quality of lakes and reservoirs through multispectral images

Cited by 58 publications

References 24 publications

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Húmeda Biosphere Reserve in Central Spain

Assessment of a Non-Optical Water Quality Property Using Space-based Imagery in Egyptian Coastal Lake

Assessment of a Non-Optical Water Quality Property Using Space-Based Imagery in Egyptian Coastal Lake

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