Comparisons between
in situ measurements of surface chlorophyll-
a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic.
In situ values were based on fluorometry, above-water radiometry
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Comparisons between
in situ measurements of surface chlorophyll-
a concentration (CHL) and ocean color remote sensing estimates were conducted during an oceanographic cruise on the Brazilian Southeastern continental shelf and slope, Southwestern South Atlantic.
In situ values were based on fluorometry, above-water radiometry and lidar fluorosensor. Three empirical algorithms were used to estimate CHL from radiometric measurements: Ocean Chlorophyll 3 bands (OC3M
RAD), Ocean Chlorophyll 4 bands (OC4v4
RAD), and Ocean Chlorophyll 2 bands (OC2v4
RAD). The satellite estimates of CHL were derived from data collected by the MODerate-resolution Imaging Spectroradiometer (MODIS) with a nominal 1.1 km resolution at nadir. Three algorithms were used to estimate chlorophyll concentrations from MODIS data: one empirical - OC3M
SAT, and two semi-analytical - Garver, Siegel, Maritorena version 01 (GSM01
SAT), and Carder
SAT. In the present work, MODIS, lidar and
in situ above-water radiometry and fluorometry are briefly described and the estimated values of chlorophyll retrieved by these techniques are compared. The chlorophyll concentration in the study area was in the range 0.01 to 0.2 mg·m
-3. In general, the empirical algorithms applied to the
in situ radiometric and satellite data showed a tendency to overestimate CHL with a mean difference between estimated and measured values of as much as 0.17 mg/m
3 (OC2v4
RAD). The semi-analytical GSM01 algorithm applied to MODIS data performed better (
rmse 0.28,
rmse-L 0.08,
mean diff. -0.01 mg/m
3) than the Carder and the empirical OC3M algorithms (
rmse 1.14 and 0.36,
rmse-L 0.34 and 0.11,
mean diff. 0.17 and 0.02 mg/m
3, respectively). We find that
rmsd values between MODIS relative to the
in situ radiometric measurements are < 26%,
i.e., there is a trend towards overestimation of
RRS by MODIS for the stations considered in this work. Other authors have already reported over and under estimation of MODIS remotely sensed reflectance due to several errors in the bio-optical algorithm performance, in the satellite sensor calibration, and in the atmospheric-correction algorithm.
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