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Abstract:
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In situ methods used for water quality assessment have both physical
and time constraints. Just a limited number of sampling points can be
performed due to this, making it difficult to capture the range and variability
of coastal processes and constituents. In addition, the mixing between fresh
and oceanic water creates complex physical, chemical and biological
environment that are difficult to understand, causing the existing measurement
methodologies to have significant logistical, technical, and economic
challenges and constraints.
Remote sensing of ocean colour makes it possible to acquire
information on the distribution of chlorophyll and other constituents over large
areas of the oceans in short periods. There are many potential applications of
ocean colour data. Satellite-derived products are a key data source to study the
distribution pattern of organisms and nutrients (Guillaud et al. 2008) and
fishery research (Pillai and Nair 2010; Solanki et al. 2001. Also, the study of
spatial and temporal variability of phytoplankton blooms, red tide
identification or harmful algal blooms monitoring (Sarangi et al. 2001; Sarangi
et al. 2004; Sarangi et al. 2005; Bhagirathan et al., 2014), river plume or
upwelling assessments (Doxaran et al. 2002; Sravanthi et al. 2013), global
productivity analyses (Platt et al. 1988; Sathyendranath et al. 1995;
IOCCG2006) and oil spill detection (Maianti et al. 2014). For remote sensing
to be accurate in the complex coastal waters, it has to be validated with the in situ measured values. In this thesis an attempt to study, measure and validate
the complex waters with the help of satellite data has been done.
Monitoring of coastal ecosystem health of Arabian Sea in a synoptic way
requires an intense, extensive and continuous monitoring of the water quality
indicators. Phytoplankton determined from chl-a concentration, is considered as
an indicator of the state of the coastal ecosystems. Currently, satellite sensors
provide the most effective means for frequent, synoptic, water-quality
observations over large areas and represent a potential tool to effectively assess
chl-a concentration over coastal and oceanic waters; however, algorithms
designed to estimate chl-a at global scales have been shown to be less accurate
in Case 2 waters, due to the presence of water constituents other than
phytoplankton which do not co-vary with the phytoplankton. The constituents of
Arabian Sea coastal waters are region-specific because of the inherent
variability of these optically-active substances affected by factors such as
riverine input (e.g. suspended matter type and grain size, CDOM) and
phytoplankton composition associated with seasonal changes. |