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Aquaculture is one of the fastest growing primary food production sectors in India and ranks second behind China. Due to its growing economic value and global demand, India's aquaculture industry experienced exponential growth for more than one decade. In this study, we extract land-based aquaculture at the pond level for the entire coastal zone of India using large-volume time series Sentinel-1 synthetic-aperture radar (SAR) data at 10-m spatial resolution. Elevation and slope from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) data were used for masking inappropriate areas, whereas a coastline dataset was used to create a land/ocean mask. The pixel-wise temporal median was calculated from all available Sentinel-1 data to significantly reduce the amount of noise in the SAR data and to reduce confusions with temporary inundated rice fields. More than 3000 aquaculture pond vector samples were collected from high-resolution Google Earth imagery and used in an object-based image classification approach to exploit the characteristic shape information of aquaculture ponds. An open-source connected component segmentation algorithm was used for the extraction of the ponds based on the difference in backscatter intensity of inundated surfaces and shape metrics calculated from aquaculture samples as input parameters. This study, for the first time, provides spatial explicit information on aquaculture distribution at the pond level for the entire coastal zone of India. Quantitative spatial analyses were performed to identify the provincial dominance in aquaculture production, such as that revealed in Andhra Pradesh and Gujarat provinces. For accuracy assessment, 2000 random samples were generated based on a stratified random sampling method. The study demonstrates, with an overall accuracy of 0.89, the spatio-temporal transferability of the methodological framework and the high potential for a global-scale application.

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 utilization were applied on collected spectra. The processed spectra were then compiled as spectral library.

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