Monthly gridded (0.5° × 0.5°) standardized precipitation–evapotranspiration index (SPEI) data of 1901–2008 were used to detect spatial pattern of temporal trend in meteorological drought during the Indian summer monsoon period. The SPEI data were converted into drought duration, magnitude and types of occurrence to capture overall drought scenario. A non‐parametric (Mann–Kendall test) method was used to test for monotonic trend at each grid level, whereas the magnitude was estimated using Sen's method. Further, a field significance test was performed at major meteorological regions level to assess whether the individual trends identified could occur by chance in an analysis of large number of time series analysed. Significant (α = 0.05) increasing trend of both drought duration and magnitude during the monsoon was found over parts of eastern, central and north‐eastern region of India, whereas the parts of west coast, arid western region and north India showed significantly decreasing trend. The trends in mid‐ as well as late‐season drought were contributing towards significant increase in drought condition, whereas trends in early‐season drought were responsible for decrease in drought events. The time series analysis of rainfall and potential evapotranspiration (PET) illustrated that over eastern part of India the increase in drought was attributed due to decrease in rainfall, whereas the decrease in drought occurrence over western arid region was mainly because of decrease in PET.

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. Copyright © 2013 Royal Meteorological Society

Bacterial leaf blight (BLB) is one of the most common diseases of rice in India, which may lead to partial or total crop loss based on the time of infestation. Hence, on-time identification of disease and its distribution over the affected region could provide useful information for minimizing the crop loss. In the present study, a comprehensive approach has been adopted to monitor the BLB-affected rice crop using hyperspectral data at filed scale and to upscale the observations at village-level using multispectral satellite data. The spectro-radiometer data at 350–2500 nm wavelength range was collected, along with other crop parameters, viz. chlorophyll and moisture content. The step-wise discriminate analysis (SDA) revealed that only four wavebands, i.e. 760, 990, 680 and 540 nm, could significantly discriminate diseased crop from healthy one. The selected wavebands were used to compute 12 narrowband vegetation indices, whereas according to SDA plant senescence index (PSRI), pigment-specific simple ratio (PSSR_b) and red-edge position were only found to be effective. PSRI and PSSR_b could be successfully re-computed using resolution simulation of hyperspectral data corresponding to linear imaging self-scanner (LISS) IV sensor. The equations were deployed on LISS IV satellite data to generate geospatial maps of PSRI and PPSR_b. The geospatial maps could differentiate different degrees of stressed crop very effectively. Hence, the proposed approach can be adopted for in-season monitoring and assessment of diseased crops at regional level for better agricultural planning and management.

The inflection point of spectral reflectance of crop in the red edge region (680–780 nm) is termed as the red edge position (REP), which is sensitive to crop biochemical and biophysical parameters. We propose a technique for automatic detection of four dynamic wavebands, i.e. two in the far-red and two in the near-infrared (NIR) region from hyperspectral data, for REP estimation using the linear extrapolation method. A field experiment was conducted at the SHIATS Farm, Allahabad, India, with four levels of nitrogen and irrigation treatments to assess the sensitivity of REP towards crop stress. A correlation analysis was carried out between REPs and different biophysical parameters, such as leaf area index (LAI) and chlorophyll content index (CCI), recorded in each plot at 50, 70, and 90 days after sowing of wheat crop under the field experiment. The inter-comparison among different REP extraction techniques revealed that the proposed technique, i.e. the modified linear extrapolation (MLE) method, has a better ability to distinguish different crop stress conditions. REPs extracted using the MLE technique showed high correlations with a wide range of LAI, CCI, and LAI × CCI, being comparable with results obtained using the traditional linear extrapolation and polynomial fitting techniques. The behaviour of the new techniques was found to be stable at both narrower and broader bandwidth, i.e. 2 and 10 nm. A new red-edge-based index, i.e. area under REP (A_REP), was used to detect the cumulative stress over wheat crop by utilizing the REP and its rate of change information at different crop growth stages. A high coefficient of determination (R² = 0.89) was found between A_REP and dry grain yield (Q ha⁻¹) up to 50 Q ha⁻¹ of wheat crop, whereas, beyond this range the relationship was found to be diminishing.