Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters.

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8 km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40 % of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30 % of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

Early-season agricultural drought: detection, assessment and monitoring using Shortwave Angle and Slope Index (SASI) data.

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.