APPLICATION OF REMOTE SENSING FOR ADJUSTMENT AND CALIBRATION OF WATER QUALITY MODEL - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 183

state of chlorophyll distributions on the 10 th of July, 2006. Before using the extracted SPOT
concentrations as a reference for the model mathematical model calibration, in the next step, the
SPOT-based chlorophyll values were upscaled to the MERIS image (Medium Resolution Imaging
Spectrometer) captured at the same exact date of the SPOT (10 th of july, 2006). The main objective of
this step was to compare and validate the used algorithm for CHL-a extraction from SPOT image.
8.6.3. Validation of SPOT-5 Extracted CHL-a Concentrations: Using (MERIS Satellite
Image)
MERIS sensor is one of the European Space Agency (ESA) sensors onboard the Envisat
environmental satellite. MERIS sensor measures the solar radiation reflected by the Earth in 15
spectral bands, ranging from the visible to the near-infrared spectral regions (390 nm to 1040 nm) at a
maximum ground spatial resolution of 300 m, and provides the most radiometrically accurate data on
Earth surface that is currently acquired from space (Curran and Steele 2005). MERIS data is known
for its relatively high spatial resolution and spectral bands optimized for estimating water quality
parameters. See Appendix (A-3) for MERIS images characteristics.
A full-resolution geo-located Level 1b MERIS FR image was acquired on July 10 th 2006, the same
date as the SPOT image described in 8.6.2. A subset from the image including Lake Edko area was
selected for analysis Figure (8-32) . The image was analyzed using BEAM-4.9 software (Brockmann
Consult, Geesthacht, Germany). BEAM is an open-source toolbox and development platform for
viewing, analysing and processing of remote sensing raster data such as MERIS. The BEAM software
includes a plug-in algorithm the MERIS case 2 water processor (MERIS C2R), which is a global
processor for retrieval of water quality parameters concentrations including TSM and CHL-a. This
plug-in algorithm is developed by Doerffer and Shiller (2007) especially for analysis of Case 2 Coastal
Waters and it is based on the inversion of the radiative transfer model using artificial neural networks,
so it is using a physically based observation model. The algorithms relate the radiances observed by
MERIS to first atmospherically corrected reflectances and then to water quality constituents.
Figure (8-32): MERIS full resolution scene , July 10 th 2006 and the subset scene of Lake Edko
The plug in processor MERIS C2R of the BEAM software is applied on the subset image scene. The
processing includes several steps, the first step is the atmospheric correction of the image and the
second one is the water algorithm calculating the TSM and the CHL-a concentrations. Figure (8-33)
shows the resulting CHL-a concentration map from the MERIS image for all water bodies including
the fisheries area. A masked subset of the CHL-a map is shown in Figure (8-34) . The pixel values of
the subset showed an average concentration 19 μg/l with a value range of 0.4-35μg/l. The eastern lake
CHL-a concentrations are around 33μg/l. The comparison proves that the CHL-a map derived from
the SPOT image with an empirical model is in good correspondence with the CHL-a map derived
from the MERIS image with a physically based observation model. It can be concluded that the
SPOT-based map is suitable for being used for the calibration of the CHL-a modelling parameters in
the Delft-3D model.