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This study aims at discriminating eight mangrove species ofRhizophoraceaefamily of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (chl-a) retrieved using the MERIS CoastColour (CC) algorithm was evaluated for the coastal waters of the west coast of India, against in situ observations made as part of the Satellite Coastal and Oceanographic Research (SATCORE) programme. These observations include profiles of surface solar irradiance (Es) along with those of upwelling radiance and downwelling irradiance, measured using hyperspectral radiometry. Chl-a was also estimated from water samples. Furthermore, remote-sensing reflectance (Rrs) and chl-a were retrieved from MODIS-Aqua using the OC3M algorithm, and from MERIS using the OC4E algorithm. In addition, to understand the long-term seasonal variability, chl-a retrieved from the MERIS-CC algorithm was overlaid on monthly mean chl-a time series data from MODIS. Comparison of chl-a using MERIS-CC to that measured in situ showed wide scatter around the linear trend line. We observed that chl-a from MERIS-CC was underestimated for two-thirds of the observations, whereas with MODIS and MERIS it was 51% and 44%, respectively. Statistical analysis showed an improved performance in chl-a retrieval using the operational OC4E algorithm as compared to that of MERIS-CC. The time series analysis showed a good match between in situ chl-a and that derived from MODIS using the OC3M algorithm, whereas the MERIS-CC algorithm showed inconsistency in match-up with regard to both magnitude and trend. This inconsistency was more prominent during the low-chl-a scenario during the northern winter. We infer that algorithms such as OC4E and OC3M that use bands from the blue and green regions of the spectrum offer better chlorophyll retrieval in high-TSM or -CDOM concentration waters in comparison with CoastColour, which uses all bands across the spectrum. [ABSTRACT FROM AUTHOR]

Development of a spectral library is a prerequisite for the higher order classification of satellite data and hyperspectral image analysis to map any ecosystem with rich diversity. In this study, sampling methodology, collection of field and laboratory spectral signatures and post-processing methodologies were investigated for developing an exclusive spectral library of mangrove species using hyperspectral spectroscopic techniques. Canopy level field spectra and leaf level laboratory spectra were collected for 34 species (25 true and 9 associated mangroves) from two different mangrove ecosystems of the Indian east coast. Post-processing steps such as removal of water vapour absorption bands, correction of drifts which occur due to the thermal properties of the instrument during data collection and smoothing of spectra for its further utilisation were applied on collected spectra. The processed spectra were then compiled as spectral library. [ABSTRACT FROM AUTHOR]