Biological and Planetary science and Applications Group

Ahmadābād, India

Biological and Planetary science and Applications Group

Ahmadābād, India
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Jain N.,Biological and Planetary science and Applications Group | Chauhan P.,Biological and Planetary science and Applications Group
Icarus | Year: 2015

Spectral reflectance data from the MRO-CRISM (Mars Reconnaissance Orbiter-Compact Reconnaissance Imaging Spectrometer for Mars) of Capri Chasma, a large canyon within Valles Marineris on Mars, have been studied. Results of this analysis reveal the presence of minerals, such as, phyllosilicates (illite, smectite (montmorillonite)) and carbonates (ankerite and manganocalcite). These minerals hint of the aqueous history of Noachian time on Mars. Phyllosilicates are products of chemical weathering of igneous rocks, whereas carbonates could have formed from local aqueous alteration of olivine and other igneous minerals. Four different locations within the Capri Chasma region were studied for spectral reflectance based mineral detection. The study area also shows the spectral signatures of iron-bearing minerals, e.g. olivine with carbonate, indicating partial weathering of parent rocks primarily rich in ferrous mineral. The present study shows that the minerals of Capri Chasma are characterized by the presence of prominent spectral absorption features at 2.31. μm, 2.33. μm, 2.22. μm, 2.48. μm and 2.52. μm wavelength regions, indicating the existence of hydrous minerals, i.e., carbonates and phyllosilicates. The occurrence of carbonates and phyllosilicates in the study area suggests the presence of alkaline environment during the period of their formation. Results of the study are important to understand the formation processes of these mineral assemblages on Mars, which may help in understanding the evolutionary history of the planet. © 2014 Published by Elsevier Inc.

Singh R.B.,Biological and Planetary science and Applications Group | Chauhan P.,Biological and Planetary science and Applications Group
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2014

Coastal waters, in particular, are the regions of high productivity and biodiversity. Detailed investigations of the variability within them can aid in understanding many biogeochemical processes. With the advent of hyperspectral remote sensing having large number of closely spaced channels and highly improved signal-to-noise ratio (SNR), the coastal applications are expected to increase and improve. In India, very less work is done in the field of coastal studies, let alone using hyperspectral remote sensing. HICO, onboard ISS, is the most recent addition to this family of instruments. So, a pilot study was conducted to assess HICO data for coastal studies especially in deriving the shallow water bathymetry estimates. The methodology for deriving bathymetry estimates is based on the different responses of shallow-water reflectance on depth and substrate type because with decreasing water depth in case 2 waters, the spectral contributions arriving from pure water reduce while from other OCAs increase. This variability is typically higher in the wavelength range 480 to 610nm. Using this wavelength range, bathymetric estimates were made at pixel level. Bathymetry estimates were found to vary from 1m to >12m. Spectral variability is clearly observed in the continuum removed spectral plots from waters of different depths and is reported in this paper.

Nigam R.,Biological and Planetary science and Applications Group | Vyas S.S.,Biological and Planetary science and Applications Group | Bhattacharya B.K.,Biological and Planetary science and Applications Group | Oza M.P.,Atmospheric and Oceanic science Group | And 4 more authors.
GIScience and Remote Sensing | Year: 2015

In-season agricultural area tracking at regular interval from geostationary satellite.Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring. © 2015 Taylor & Francis

Durairaj P.,Annamalai University | Sarangi R.K.,Biological and Planetary science and Applications Group | Ramalingam S.,Annamalai University | Thirunavukarassu T.,Annamalai University | Chauhan P.,Biological and Planetary science and Applications Group
Environmental Monitoring and Assessment | Year: 2015

In situ datasets of nitrate, sea surface temperature (SST), and chlorophyll a (chl a) collected during the monthly coastal samplings and organized cruises along the Tamilnadu and Andhra Pradesh coast between 2009 and 2013 were used to develop seasonal nitrate algorithms. The nitrate algorithms have been built up based on the three-dimensional regressions between SST, chl a, and nitrate in situ data using linear, Gaussian, Lorentzian, and paraboloid function fittings. Among these four functions, paraboloid was found to be better with the highest co-efficient of determination (postmonsoon: R2 = 0.711, n = 357; summer: R2 = 0.635, n = 302; premonsoon: R2 = 0.829, n = 249; and monsoon: R2 = 0.692, n = 272) for all seasons. Based on these fittings, seasonal nitrate images were generated using the concurrent satellite data of SST from Moderate Resolution Imaging Spectroradiometer (MODIS) and chlorophyll (chl) from Ocean Color Monitor (OCM-2) and MODIS. The best retrieval of modeled nitrate (R2 = 0.527, root mean square error (RMSE) = 3.72, and mean normalized bias (MNB) = 0.821) was observed for the postmonsoon season due to the better retrieval of both SST MODIS (28 February 2012, R2 = 0.651, RMSE = 2.037, and MNB = 0.068) and chl OCM-2 (R2 = 0.534, RMSE = 0.317, and MNB = 0.27). Present results confirm that the chl OCM-2 and SST MODIS retrieve nitrate well than the MODIS-derived chl and SST largely due to the better retrieval of chl by OCM-2 than MODIS. © 2015, Springer International Publishing Switzerland.

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