Our study demonstrates the potential of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral data and radiative transfer modeling to retrieve the atmospheric carbon dioxide (CO2) concentration from point source emission of one of India’s major coal-fed super thermal power plants at Kota, Rajasthan, India. AVIRIS-NG data with synchronous in situ measurements were collected as a part of the first ISRO-NASA joint airborne hyperspectral science campaign in India. The method utilized in our study includes theoretical simulations of at-sensor radiance in AVIRIS-NG spectral bands through the atmospheric radiative transfer model MODTRAN. Simulations were performed for the specific scene-sensor-atmospheric conditions pertaining to AVIRIS-NG overpass over Kota site. To eliminate the interfering effect of atmospheric water vapor with CO2 concentrations, simulations pertaining to variable water vapor values varying from 0.25 to 4.5 g / cm2 were also carried out, which in turn were utilized to normalize the water vapor effects. Based upon the simulation results pertaining to the specific absorption bands of CO2 in the shortwave infrared spectral range of AVIRIS-NG radiance data, a theoretical model was established between continuum interpolated band ratio (CIBR) and CO2 concentration. The CIBR − CO2 model coefficients for each of the water vapor subranges were then applied to AVIRIS-NG L1 radiance data to obtain the CO2 concentration map of the study area. A distinct CO2 plume could be detected from the coal-fed Kota Super Thermal Power Station with the CO2 concentration (XCO2) of the order of more than 500 ppmv near the power plant stacks. A range of XCO2 from 400 to 550 ppmv was observed within the scene. Our study has provided promising results in mapping the atmospheric CO2 content from the point source using the high-resolution airborne hyperspectral AVIRIS-NG data.
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.
Chlorophyll-a maps derived from IRS-P4 Ocean Color Monitor (OCM) was used to study the distribution pattern of phytoplankton biomass in the eastern Arabian sea off Karnataka-Goa coast, southwest coast of India. The data was compared with in-situ measurements of chlorophyll-a concentration estimated for 100 stations covering an area of more than 4000 km^2 in the above region. The presence of dense algal blooms spread over an area of almost 100 km^2 representing Trichodesmium sp. 18km off Kumta-Gokarna in the eastern Arabian Sea depicts high value (30 to 40 mg/m^3) of chlorophyll concentration. Similarly, around the Nethrani Island, off Bhatkal, the surface concentration ranged from 5 to 10 mg/m^3. The secchi depth varied from 4 to 8 m near the island. The sea surface area enveloping this high bloom (around the island) depicts a normal distribution of chlorophyll-a ranging from of 0.1 to 5 mg/m^3. It is suggested here, that the low salinity value (35 to 35.2 %) around the Nethrani Island enhances the algal bloom due to enrichment of nutrients in the shallow marine environment through probable inputs of nutrient charged fresh water from the island aquifers. Near river mouths, the values are marginally high in the range of 3 to 5 mg/m^3, probably enforced by riverine nutrient inputs well depicted by the Tadri River. The satellite (IRS-P4 OCM) derived images of chlorophyll during summer also shows high values as a band parallel to the coast. During the occurrence of algal blooms this band, parallel to the coast, widens offshore and this phenomenon of widening is typically absent during non-bloom summer scenarios, as identified for summer 2005.
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