Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

Theologou, I.; Patelaki, M.; Καράντζαλος, Κωνσταντίνος

doi:10.5194/isprsarchives-xl-7-w3-1511-2015

Cited by 15 publications

(10 citation statements)

References 18 publications

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“…The variation of EC in a water body is related to changes in the concentration of total dissolved solids in the water. The increase in the salinity of the water causes changes in the amount of radiation reflected in the visible and infrared spectrum bands (Khattab & Merkel, 2013;Theologou et al, 2015). Further, pH was related to bands b3, b4, b5, and b6 with R 2 of 0.8153. pH is the negative logarithm of the concentration of H + ions and has also been linked with the band b6 (Theologou et al, 2015).…”

Section: Statistical Models Of Water Quality and Depthmentioning

confidence: 98%

Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

González-Márquez

Torres-Bejarano

Torregroza-Espinosa

et al. 2018

Journal of South American Earth Sciences

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The aim of this study was to evaluate the viability of using Landsat 8 spectral images to estimate water quality parameters and depth in El Guájaro Reservoir. On February and March 2015, two samplings were carried out in the reservoir, coinciding with the Landsat 8 images. Turbidity, dissolved oxygen, electrical conductivity, pH and depth were evaluated. Through multiple regression analysis between measured water quality parameters and the reflectance of the pixels corresponding to the sampling stations, statistical models with determination coefficients between 0.6249 and 0.9300 were generated. Results indicate that from a small number of measured parameters we can generate reliable models to estimate the spatial variation of turbidity, dissolved oxygen, pH and depth, as well the temporal variation of electrical conductivity, so models generated from Landsat 8 can be used as a tool to facilitate the environmental, economic and social management of the reservoir.

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Section: Statistical Models Of Water Quality and Depthmentioning

confidence: 98%

Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

González-Márquez

Torres-Bejarano

Torregroza-Espinosa

et al. 2018

Journal of South American Earth Sciences

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“…Shen et al [63] and Ruddick et al [64] also successfully applied the spectral reflectance at Red-NIR region to detect and monitor harmful algal bloom at inland and coastal waters. Theologou et al [65] also found a high correlation of Chl-a with red and NIR regions of Landsat 7 and 8 satellite for predicting water quality in inland shallow lakes.…”

Section: Development Of the Prediction Modelmentioning

confidence: 91%

Estimation of Chlorophyll‐<em>a</em> Concentration and Trophic States for an Inland Lake from Landsat 8 OLI Data: A Case of Nalban Lake of East Kolkata Wetland, India

Patra¹,

Dubey²,

Trivedi³

et al. 2016

Preprint

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1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R2 value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.

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“…Similarly other studies like Dogliotti et al (2015) tested wavelength bands (645 and 859 nm) to extract turbidity parameter. A ratio between the blue (Band 2) and red bands (Band 4) was proposed by Theologou et al (2015) to retrieve dissolved oxygen with Landsat 8 images of a lake located in Greece and achieved an accuracy of 0.80 R 2 . In 2015 (Bonansea et al 2015), Landsat TM and Landsat ETMþ data were compared with field data for a lake in Argentina.…”

Section: Related Workmentioning

confidence: 99%

“…Dissolved Oxygen (DO) was calculated with the estimation algorithm proposed by Theologou et al (2015), and Khalil et al (2016). They used a combination of TOA reflectance of Bands 2 and 4, which can be seen in Equation (3).…”

Section: Dissolved Oxygenmentioning

confidence: 99%

Assessment of correlation amongst physico-chemical, topographical, geological, lithological and soil type parameters for measuring water quality of Rawal watershed using remote sensing

et al. 2022

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The quality of water is traditionally assessed by the collection of physico-chemical parameters, i.e., pH, turbidity, dissolved oxygen of the water bodies. However, the variations in environmental factors may have an impact on the quality of water, as changes in these attributes may affect the water bodies. These factors include the topographical, geological, lithological and soil type parameters of the watershed. In this study, the relationship amongst the physico-chemical, topographical, geological, lithological and soil type parameters of Rawal watershed was evaluated. The parameters included in the present study could be classified as follows: (a) water quality parameters (b) topographical parameters, (c) geological parameters, (d) lithological parameters, and (e) soil type parameters. Water quality parameters consisted of dissolved oxygen, pH, turbidity and temperature. The topographical parameters include the slope and aspect of the watershed while the lithological, geological and soil type parameters include the lithology, geology and soil type of the watershed. Pearson's correlation was used to determine the relationship amongst these different parameters. The results have revealed that the correlations of the topographical, lithological, geological parameters with the water quality parameters in the Rawal watershed for the monsoon seasons of June to August mostly have the same trend. Throughout the four year time period, turbidity and temperature parameters had positive correlations with soil type (ranging 0.03–0.24), however had weak correlation with geological and lithological parameters. Dissolved oxygen did not show any relationship with topographical and lithological parameters. The results for pH show that it has weak to fair positive correlations with topographical parameters. However, this analysis is based on the Landsat 8 images extracted for the monsoon seasons of the years of 2017–2020, and to examine a more prominent impact of geographical or environmental factors on the physico-chemical features, a large dataset should be considered.

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Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

Cited by 15 publications

References 18 publications

Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

Use of LANDSAT 8 images for depth and water quality assessment of El Guájaro reservoir, Colombia

Estimation of Chlorophyll‐<em>a</em> Concentration and Trophic States for an Inland Lake from Landsat 8 OLI Data: A Case of Nalban Lake of East Kolkata Wetland, India

Assessment of correlation amongst physico-chemical, topographical, geological, lithological and soil type parameters for measuring water quality of Rawal watershed using remote sensing

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