Drought is an important global hazard, challenging the sustainable agriculture and food security of nations. Measuring agricultural drought vulnerability is a prerequisite for targeting interventions to improve and sustain the agricultural performance of both irrigated and rain-fed agriculture. In this study, crop-generic agricultural drought vulnerability status is empirically measured through a composite index approach. The study area is Haryana state, India, a prime agriculture state of the country, characterised with low rainfall, high irrigation support and stable cropping pattern. By analysing the multiyear rainfall and crop condition data of kharif crop season (June-October) derived from satellite data and soil water holding capacity and groundwater quality, nine contributing indicators were generated for 120 blocks (sub-district administrative units). Composite indices for exposure, sensitivity and adaptive capacity components were generated after assigning variance-based weightages to the respective input indicators. Agricultural Drought Vulnerability Index (ADVI) was developed through a linear combination of the three component indices. ADVI-based vulnerability categorisation revealed that 51 blocks are with vulnerable to very highly vulnerable status. These blocks are located in the southern and western parts of the state, where groundwater quality is saline and water holding capacity of soils is less. The ADVI map has effectively captured the spatial pattern of agricultural drought vulnerability in the state. Districts with large number of vulnerable blocks showed considerably larger variability of de-trended crop yields. Correlation analysis reveals that crop condition variability, groundwater quality and soil factors are closely associated with ADVI. The vulnerability index is useful to prioritise the blocks for implementation of long-term drought management plans. There is scope for improving the methodology by adding/fine-tuning the indicators and by optimising the weights.

Abstract-In the present study evaluation of L-band SAR data at different polarization combinations in linear, circular as well as hybrid polarimetric imaging modes for crop and other landuse classifications has been carried out. Full-polarimetric radar data contains all the scattering information for any arbitrary polarization state, hence data of any combination of transmit and receive polarizations can be synthesized, mathematically from full-polarimetric data. Circular and various modes of hybrid polarimetric data (where the transmitter polarization is either circular or orientated at 45 • , called π/4 and the receivers are at horizontal and vertical polarizations with respect to the radar line of sight) were synthesized (simulated) from ALOS-PALSAR fullpolarimetric data of 14th December 2008 over central state farm central latitude and longitude 29 • 15 N/75 • 43 E and bounds for northwest corner is 29 • 24 N/75 • 37 E and southeast corner is 29 • 07 N/75 • 48 E in Hisar, Haryana (India) Supervised classification was conducted for crops and few other landuse classes based on ground truth measurements using maximum-likelihood distance measures derived from the complex Wishart distribution of SAR data at various polarization combinations. It has been observed that linear fullpolarimetric data showed maximum classification accuracy (92%) followed by circular-full (89%) and circular-dual polarimetric data (87%), which was followed by hybrid polarimetric data (73-75%) and then linear dual polarimetric data (63-71%). Among the linear dual polarimetric data, co-polarization complex data showed better classification accuracy than the cross-polarization data. Also multidate single polarization SAR data over central state farm during rabi (winter) season was analyzed and it was observed that single date fullpolarimetric SAR data produced equally good classification result as the multi-date single polarization SAR data.

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