Skip to main content

Remote sensing of the water quality of shallow lakes: A mixture modelling approach to quantifying phytoplankton in water characterized by high‐suspended sediment

Buy Article:

$55.00 plus tax (Refund Policy)

Remote sensing has the potential to provide truly synoptic views of water quality, in particular, the spatial distributions of phytoplankton. Whilst the spectral capabilities of satellites used in ocean colour work have improved significantly over recent years, the application of satellite remote sensing to lake water is constrained by the need for high spatial resolution image data and thus remains limited by spectral resolution capabilities. This becomes a significant problem when attempting to quantify chlorophyll a (Chl a ) in waters characterized by high and heterogeneous suspended sediment concentrations (SSC). The SSC dominates the spectral reflectance, masking the spectral influence from other components in broad spectral band systems, making Chl a determination from remote sensing imagery difficult. This paper presents a linear mixture modelling approach to derive accurate estimates of Chl a from Landsat Thematic Mapper (TM) imagery. This approach was tested in Lake Balaton, Europe's largest shallow lake characterized by high suspended sediment and, until recently, frequent eutrophic and hypereutrophic episodes. The last significant bloom occurred in September of 2000 and a Landsat TM image was acquired for 11th September, during which ground reference data of water quality was collected. The modelled image‐derived results of Chl a demonstrate an excellent correspondence (r 2  = 0.95) between the ground‐based measurements of Chl a , and yield considerable detail of lake phytoplankton distributions. The September 2000 calibration was then successfully applied to a July 1994 Landsat TM image and validated with Chl a data collected coincidently within two days of the image. The comparability between water sample data and image results demonstrates that there is temporal stability and robustness in the approach and calibration described.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: School of Biological and Environmental Sciences, University of Stirling, Stirling, FK9 4LA, UK 2: Isotope Biochemistry Laboratory, Scottish Universities Environmental Research Centre, Rankine Avenue, Scottish Enterprise Technology Park, East Kilbride, G75 0QF, UK 3: Balaton Limnological Research Institute of the Hungarian Academy of Sciences, Tihany, POB 35, H‐8237, Hungary

Publication date: 2006-04-20

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more