Literature DB >> 19067211

Assessment of temporal variations of water quality in inland water bodies using atmospheric corrected satellite remotely sensed image data.

Diofantos G Hadjimitsis1, Chris Clayton.   

Abstract

Although there have been many studies conducted on the use of satellite remote sensing for water quality monitoring and assessment in inland water bodies, relatively few studies have considered the problem of atmospheric intervention of the satellite signal. The problem is especially significant when using time series multi-spectral satellite data to monitor water quality surveillance in inland waters such as reservoirs, lakes, and dams because atmospheric effects constitute the majority of the at-satellite reflectance over water. For the assessment of temporal variations of water quality, the use of multi-date satellite images is required so atmospheric corrected image data must be determined. The aim of this study is to provide a simple way of monitoring and assessing temporal variations of water quality in a set of inland water bodies using an earth observation- based approach. The proposed methodology is based on the development of an image-based algorithm which consists of a selection of sampling area on the image (outlet), application of masking and convolution image processing filter, and application of the darkest pixel atmospheric correction. The proposed method has been applied in two different geographical areas, in UK and Cyprus. Mainly, the method has been applied to a series of eight archived Landsat-5 TM images acquired from March 1985 up to November 1985 of the Lower Thames Valley area in the West London (UK) consisting of large water treatment reservoirs. Finally, the method is further tested to the Kourris Dam in Cyprus. It has been found that atmospheric correction is essential in water quality assessment studies using satellite remotely sensed imagery since it improves significantly the water reflectance enabling effective water quality assessment to be made.

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Year:  2008        PMID: 19067211     DOI: 10.1007/s10661-008-0629-3

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  8 in total

1.  Comparison of remote sensing data, model results and in situ data for total suspended matter (TSM) in the southern Frisian lakes.

Authors:  A G Dekker; R J Vos; S W Peters
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

2.  Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland.

Authors:  P Härmä; J Vepsäläinen; T Hannonen; T Pyhälahti; J Kämäri; K Kallio; K Eloheimo; S Koponen
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

3.  Detecting chlorophyll, Secchi disk depth and surface temperature in a sub-alpine lake using Landsat imagery.

Authors:  C Giardino; M Pepe; P A Brivio; P Ghezzi; E Zilioli
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

4.  Application of Landsat imagery to regional-scale assessments of lake clarity.

Authors:  Steven M Kloiber; Patrick L Brezonik; Marvin E Bauer
Journal:  Water Res       Date:  2002-10       Impact factor: 11.236

5.  Determination of chlorophyll-a amount in Golden Horn, Istanbul, Turkey using IKONOS and in situ data.

Authors:  C Ormeci; E Sertel; O Sarikaya
Journal:  Environ Monit Assess       Date:  2008-06-24       Impact factor: 2.513

6.  Water quality monitoring using remote sensing in support of the EU water framework directive (WFD): a case study in the Gulf of Finland.

Authors:  Qiaoling Chen; Yuanzhi Zhang; Martti Hallikainen
Journal:  Environ Monit Assess       Date:  2006-08-05       Impact factor: 2.513

7.  A semi-operative approach to lake water quality retrieval from remote sensing data.

Authors:  J Pulliainen; K Kallio; K Eloheimo; S Koponen; H Servomaa; T Hannonen; S Tauriainen; M Hallikainen
Journal:  Sci Total Environ       Date:  2001-03-14       Impact factor: 7.963

8.  Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data.

Authors:  Yunpeng Wang; Hao Xia; Jiamo Fu; Guoying Sheng
Journal:  Sci Total Environ       Date:  2004-07-26       Impact factor: 7.963

  8 in total
  4 in total

1.  Monitoring water quality in a hypereutrophic reservoir using Landsat ETM+ and OLI sensors: how transferable are the water quality algorithms?

Authors:  Eliza S Deutsch; Ibrahim Alameddine; Mutasem El-Fadel
Journal:  Environ Monit Assess       Date:  2018-02-15       Impact factor: 2.513

Review 2.  A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.

Authors:  Mohammad Haji Gholizadeh; Assefa M Melesse; Lakshmi Reddi
Journal:  Sensors (Basel)       Date:  2016-08-16       Impact factor: 3.576

3.  Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil.

Authors:  I Ogashawara; L Li; M J Moreno-Madriñán
Journal:  Geohealth       Date:  2019-08-21

4.  Multispectral Remote Sensing Utilization for Monitoring Chlorophyll-a Levels in Inland Water Bodies in Jordan.

Authors:  Nidal M Hussein; Mohammed N Assaf
Journal:  ScientificWorldJournal       Date:  2020-08-07
  4 in total

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