Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina)

Bohn, Vanesa Yael; Carmona, Facundo; Rivas, Raúl Eduardo; Lagomarsino, Leonardo; Diovisalvi, Nadia; Zagarese, Horacio

doi:10.1016/j.ejrs.2017.04.005

Cited by 21 publications

(18 citation statements)

References 29 publications

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“…According to our study findings, the logchl-a predictive model based on spectral indices incorporated OLI bands 2 (blue), 3 (green), 4 (red), 5 (NIR) and 7 (swir2) with R equal to 0.58 [13,16,62,69] used similar bands for the estimation of chlorophyll-a in lakes and reservoirs and more particular vegetation indices and bands TM and ETM 1 (blue), 2 (green) and 4 (NIR). Reference [62] used the NDVI in Laguna Chascomús in relation to Chl-a estimation for its optical characteristics and because it is sensitive to the pigment absorption. NDVI has been found to be very sensitive to changes in the environment [62,63].…”

Section: Discussionmentioning

confidence: 74%

“…Reference [62] used the NDVI in Laguna Chascomús in relation to Chl-a estimation for its optical characteristics and because it is sensitive to the pigment absorption. NDVI has been found to be very sensitive to changes in the environment [62,63]. Moreover, its use is more successful in zones with moderate wind speeds without developing waves, which is not the case in Trichonis Lake [62].…”

Section: Discussionmentioning

confidence: 99%

“…NDVI has been found to be very sensitive to changes in the environment [62,63]. Moreover, its use is more successful in zones with moderate wind speeds without developing waves, which is not the case in Trichonis Lake [62]. Furthermore, water indices include SWIR band and according to Reference [70] all significant band combinations for chlorophyll included at least one of the short wave infrared bands (SWIR), although most water quality studies to this point have not included SWIR bands.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the above, several vegetation and water indices (Table 3) were calculated and added to the analysis ( Figure 2). The usefulness of water indices has been demonstrated in different studies for drought monitoring and early warning assessment [59][60][61][62]. However, the vegetation indices and reflectance values (individuals bands and band ratios) application are highly encouraged for the estimation of water quality parameters (i.e., chlorophyll-a, transparency) in lakes [63][64][65][66] in [62].…”

Section: Developing Relationships Between Landsat 8 and Water Qualitymentioning

confidence: 99%

“…The usefulness of water indices has been demonstrated in different studies for drought monitoring and early warning assessment [59][60][61][62]. However, the vegetation indices and reflectance values (individuals bands and band ratios) application are highly encouraged for the estimation of water quality parameters (i.e., chlorophyll-a, transparency) in lakes [63][64][65][66] in [62]. As a last step of the analysis, the most accurate regression models (developed by satellite and in-situ data of August 2014) were selected on the basis of the established statistical criteria described earlier on, while then they were validated based on the satellite and in-situ data of 2013. to a subset of optimal models in order to further compare them and select the most straightforward models instead of complicated ones with higher accuracy (higher R).…”

Section: Developing Relationships Between Landsat 8 and Water Qualitymentioning

confidence: 99%

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An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

et al. 2018

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In-situ monitoring of lake water quality in synergy with satellite remote sensing represents the latest scientific trend in many water quality monitoring programs worldwide. This study investigated the suitability of the Operational Land Imager (OLI) instrument onboard the Landsat 8 satellite platform in accurately estimating key water quality parameters such as chlorophyll-a and nutrient concentrations. As a case study the largest freshwater body of Greece (Trichonis Lake) was used. Two Landsat 8 images covering the study site were acquired on 30 October 2013 and 30 August 2014 respectively. Near concurrent in-situ observations from two water sampling campaigns were also acquired from 22 stations across the lake under study. In-situ measurements (nutrients and chlorophyll-a concentrations) were statistically correlated with various spectral band combinations derived from the Landsat imagery of year 2014. Subsequently, the most statistically promising predictive models were applied to the satellite image of 2013 and validation was conducted using in-situ data of 2013 as reference. Results showed a relatively variable statistical relationship between the in-situ and reflectances (R logchl-a: 0.58, R NH 4 + : 0.26, R chl-a: 0.44). Correlation coefficient (R) values reported of up to 0.7 for ammonium concentrations and also up to 0.5 and up to 0.4 for chl-a concentration and chl-a concentrations respectively. These results represent a higher accuracy of Landsat 8 in comparison to its predecessors in the Landsat satellites series, as evidenced in the literature. Our findings suggest that Landsat 8 has a promising capability in estimating water quality components in an oligotrophic freshwater body characterized by a complete absence of any quantitative, temporal and spatial variance, as is the case of Trichonis lake. Yet, even with the presence of a lot of ground information as was the case in our study, a quantitatively accurate estimation of water quality constituents in coastal/inland waters remains a great challenge. The launch of sophisticated spaceborne sensing systems, such as that of Landsat 8, can assist in improving our ability to estimate freshwater lake properties from space.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Developing Relationships Between Landsat 8 and Water Qualitymentioning

confidence: 99%

Section: Developing Relationships Between Landsat 8 and Water Qualitymentioning

confidence: 99%

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An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

et al. 2018

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Temporal and spatial variability of turbidity in a highly productive and turbid shallow lake (Chascomús, Argentina) using a long time-series of Landsat and Sentinel-2 data

Gayol,

Dogliotti,

Lagomarsino

et al. 2024

Hydrobiologia

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Quality of Natural Waters Surrounding Campo Mourão, State of Paraná, Southern Brazil: Water Resources Under the Influences from Urban and Agricultural Activities

Mendes

Gonçalves

Frâncica

et al. 2020

Water Air Soil Pollut

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Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina)

Cited by 21 publications

References 29 publications

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

An Appraisal of the Potential of Landsat 8 in Estimating Chlorophyll-a, Ammonium Concentrations and Other Water Quality Indicators

Temporal and spatial variability of turbidity in a highly productive and turbid shallow lake (Chascomús, Argentina) using a long time-series of Landsat and Sentinel-2 data

Quality of Natural Waters Surrounding Campo Mourão, State of Paraná, Southern Brazil: Water Resources Under the Influences from Urban and Agricultural Activities

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