In-situ measurements of the bio-optical properties of the seawater are important to develop algorithms for seawater
constituent estimation using satellite remote sensing. A data collection campaign was conducted for bio-optical
characterization of the open and coastal waters of the Arabian Sea during April 15-29, 2006. Bio-optical measurements
were made using the Satlantic hyper-spectral underwater radiometer (Hyperpro-II) for 13 sampling stations include
oligotrophic, Trichodesmium bloom dominated and coastal waters in 400-800 nm spectral range.
For open oceans stations 1% light was available at 50 to 70 meter depth, whereas, for coastal waters it varied from 18 to
35 meter. The deep chlorophyll maxima (DCM) was observed at 30 to 42 meter depth during the bloom conditions with
surface chlorophyll-a concentration ranging between 0.1 to 0.85 mg m-3 whereas, for open ocean and non-bloom
conditions the DCM depth varied from 35 to 60 m with surface chlorophyll ranging between 0.05 to 0.12 mgm-3.
Particulate back scattering coefficient at 700-nm vary from 0.0011 to 0.0031 for bloom waters and 0.00046 to 0.0012 for
open ocean waters. The normalized water leaving radiance computed from these spectra in the spectral bands of IRS-P4,
OCM bands were examined. The global ocean chlorophyll-2 (OC2), and 4 (OC4) algorithms performed reasonably well
for open ocean waters, however both the algorithms overestimated chlorophyll concentration for bloom dominated
waters.
An artificial neural network (ANN) based procedure is developed to estimate concentrations of Chlorophyll-a, Suspended Particulate Matter (SPM) and absorption due to chromophoric dissolved organic matter (CDOM) in the seawater from satellite detected normalized water-leaving radiance (nLw) of the IRS-P4 Ocean Colour Monitor (OCM) satellite data. An ocean colour reflectance model was used to generate surface spectral reflectance corresponding to first five bands of IRS-P4 OCM sensor, using three optically active oceanic water constituents as inputs. ANN model making use of reflectance in five visible bands was trained, tested and validated to invert the spectral reflectance for the simultaneous estimation of three optically active constituents. The retrieved chlorophyll-a concentrations from ANN based algorithm were compared with the corresponding chlorophyll-a distribution obtained by global empirical algorithms e.g. Ocean Chlorophyll-4 (OC4) algorithm. ANN derived chlorophyll-a estimates were found to have reduced the over estimation in coastal waters often observed with the use of band ratio algorithms.
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