Daily gridded (0.5°× 0.5°) rainfall data between 1971 and 2005 were used to detect spatial patterns of trend in rainfall and rainy days during the Indian Summer Monsoon (June to September). A non-parametric (Mann-Kendall test) method was used to test for monotonic trend at each grid level. The magnitude of trend is estimated using Sen's method. Further, a field significance test was applied to assess significant trend at an aggregated level over each meteorological subdivision. A statistically significant (α = 0.1) increasing trend of both rainfall and rainy days during the monsoon season was found over the east coast and Deccan Plateau region of India. Meteorological subdivisions over the west coast, western arid region and northeastern humid region showed significantly decreasing trends in both rainfall and rainy days. The northern hilly parts of the Himalaya were found to have a significantly increasing trend of rainfall but decreasing trend of rainy days. The north and central plains of India showed a decreasing trend of rainy days and the eastern plain was found to have a decreasing trend of rainfall during the summer monsoon period.

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The reflectance spectrum of the species in a hyperspectral data can be modelled as an ndimensional vector. The spectral angle mapper computes the angle between the vectors which is used to discriminate the species. The spectral information divergence models the data as a probability distribution so that the spectral variability between the bands can be extracted using the stochastic measures. The hybrid approach of spectral angle mapper and spectral information divergence is found to be better discriminator than spectral angle mapper or spectral information divergence alone. The spectral correlation angle is computed as a cosine of the angle of the Pearsonian correlation coefficient between the vectors. The spectral correlation angle is a better measure than the spectral angle mapper as it considers only standardized values of the vectors rather than the absolute values of the vector. In the present paper a new hybrid measure is proposed which is based on the spectral correlation angle and the spectral information divergence. The proposed method has been compared with the hybrid approach of spectral information divergence and spectral angle mapper for discrimination of crops belonging to Vigna species using measures like relative spectral discriminatory power, relative discriminatory probability and relative discriminatory entropy in different spectral regions. Experimental results using the laboratory spectra show that the proposed method gives higher relative discriminatory power in 400nm700nm spectral region.

Early season or crop-planting-period (ES/CPP) drought conditions have become a recurrent phenomenon in tropical countries like India, due to fluctuations in the time of onset and progression of monsoon rains. ES/CPP agricultural drought assessment is a major challenge because of the difficulties in the generation of operational products on soil moisture at larger scales. The present study analyzed the Shortwave Angle Slope Index (SASI) derived from Near Infrared and Shortwave Infrared data of Moderate Resolution Imaging Spectroradiometer, for tracking surface moisture changes and assessing the agricultural drought conditions during ES/CPP, over Andhra Pradesh state, India. It was found that in-season progression of SASI was well correlated with rainfall and crop planting patterns in different districts of the study area state in both drought and normal years. Rainfall occurrence, increase in crop planted area, and decrease in SASI were in chronological synchronization in the season. Change in SASI from positive to negative values is a unique indication of dryness to wetness shift in the season. Duration of positive SASI values indicated the persistence of agricultural drought in the crop planting period. Mean SASI values were able to discriminate an area which was planted in normal year and unplanted in drought year. SASI thresholds provide an approximate and rapid estimate of the crop planting favorable area in a region which is useful to assess the impact of drought. Thus, SASI is a potential index to strengthen the existing operational drought monitoring systems. Further work needs to be on the integration of multiple parameters-SASI, soil texture, soil depth, rainfall and cropping pattern, to evolve a geospatial product on crop planting favorable areas. Such products pave the way for quantification of drought impact on agriculture in the early part of the season, which is a major inadequacy in the current drought monitoring system.