Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwater
ecosystems and to humans who use these resources for drinking and recreation. Remote sensing is being
used increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This study
uses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophic
freshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria.
The lake's small size, highly turbid water, and covariant water constituents present a challenging case for
both algorithm development and atmospheric correction. The objectives of the study are to assess the
optical properties of the lake, to evaluate various atmospheric correction procedures, and to compare the
performance of empirical and semi-analytical algorithms in hypertrophic water. In situ water quality
parameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwelling
radiance measurements at depth 0.66 m were corrected for instrument self-shading and processed to
water-leaving reflectance using downwelling irradiance measurements and estimates of the vertical
attenuation coefficient for upward radiance, Ku, generated from a simple bio-optical model estimating the
total absorption, a(λ), and backscattering coefficients, bb(λ). The normalised water-leaving reflectance was
used for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code
atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a
(Chl a), Total Suspended Solids (TSS), Secchi Disk depth (zSD) and absorption by CDOM (aCDOM) were
derived from simultaneously collected in situ andMERISmeasurements. The empirical algorithms gave high
correlation coefficient values, although they have a limited ability to separate between signals from
covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2
waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations.
However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of
atmospheric correction and divergence between the optical properties and ranges used to train the
algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the
spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results
it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland
waters. This study also demonstrates the considerable potential value for simple TOA algorithms for
hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern
Africa and that remote sensing be integrated into future operational water quality monitoring systems.
Eutrophication and cyanobacterial algal blooms present an increasing threat to the health of freshwaterecosystems and to humans who use these resources for drinking and recreation. Remote sensing is beingused increasingly as a tool for monitoring these phenomena in inland and near-coastal waters. This studyuses the Medium Resolution Imaging Spectrometer (MERIS) to view Zeekoevlei, a small hypertrophicfreshwater lake situated on the Cape Flats in Cape Town, South Africa, dominated by Microcystis cyanobacteria.The lake's small size, highly turbid water, and covariant water constituents present a challenging case forboth algorithm development and atmospheric correction. The objectives of the study are to assess theoptical properties of the lake, to evaluate various atmospheric correction procedures, and to compare theperformance of empirical and semi-analytical algorithms in hypertrophic water. In situ water qualityparameter and radiometric measurements were made simultaneous to MERIS overpasses. Upwellingradiance measurements at depth 0.66 m were corrected for instrument self-shading and processed towater-leaving reflectance using downwelling irradiance measurements and estimates of the verticalattenuation coefficient for upward radiance, Ku, generated from a simple bio-optical model estimating thetotal absorption, a(λ), and backscattering coefficients, bb(λ). The normalised water-leaving reflectance wasused for assessing the accuracy of image-based Dark Object Subtraction and 6S Radiative Transfer Code
atmospheric correction procedures applied to MERIS. Empirical algorithms for estimating chlorophyll a
(Chl a), Total Suspended Solids (TSS), Secchi Disk depth (zSD) and absorption by CDOM (aCDOM) were
derived from simultaneously collected in situ andMERISmeasurements. The empirical algorithms gave high
correlation coefficient values, although they have a limited ability to separate between signals from
covariant water constituents. The MERIS Neural Network algorithms utilised in the standard Level 2 Case 2
waters product and Eutrophic Lakes processor were also used to derive water constituent concentrations.
However, these failed to produce reasonable comparisons with in situ measurements owing to the failure of
atmospheric correction and divergence between the optical properties and ranges used to train the
algorithms and those of Zeekoevlei. Maps produced using the empirical algorithms effectively show the
spatial and temporal variability of the water quality parameters during April 2008. On the basis of the results
it is argued that MERIS is the current optimal sensor for frequent change detection applications in inland
waters. This study also demonstrates the considerable potential value for simple TOA algorithms for
hypertrophic systems. It is recommended that regional algorithm development be prioritized in southern
Africa and that remote sensing be integrated into future operational water quality monitoring systems.
การแปล กรุณารอสักครู่..
