Abstract

Satellite remote sensing is a valuable tool in obtaining information on the processes taking place in the surface of sea and coastal waters. With currently advanced satellite-based data (e.g., MODIS and MERIS), a large number of variables concerning water quality conditions such as chlorophyll-a (Chl-a), total suspended matter (TSM), yellow substance, turbidity, salinity and sea surface temperature (SST) could be observed on a regular basis, which are also the primary steps to monitor harmful algal blooms (HABs). Based on MERIS reduced resolution L_1B product, atmospheric correction and water components are implemented via neural network in Case2Regional processor that ESA provides. Water leaving reflectances are calculated and show reasonable distribution compared with that directly from L_2 data. Chl-a and TSM concentrations are also retrieved and compared with L_2 data products, which show that low Chl-a concentrations (<10 mg/m3) retrieval is very close to that of L_2 data while TSM concentration are a little higher than that from L_2 data. However, the distribution and values range of Chl-a and TSM concentrations are accordant with the water quality in the study area. Still more in-situ data are needed to verify the accuracy of the water components retrieval from MERIS data.

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