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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A b s t r a c t. Soil temperature is an important factor in biogeochemical processes. On-site monitoring of soil temperature is limited in spatio-temporal scale as compared to air temperature data inventories due to various management difficulties. Therefore, empirical models were developed by taking 30-year long-term (1985-2014) air and soil temperature data for prediction of soil temperatures at three depths (5, 15, 30 cm) in morning (0636 Indian standard time) and afternoon (1336 Indian standard time) for alluvial soils in lower Indo-Gangetic plain. At 5 cm depth, power and exponential regression models were best fitted for daily data in morning and afternoon, respectively, but it was reverse at 15 cm. However, at 30 cm, exponential models were best fitted for both the times. Regression analysis revealed that in morning for all three depths and in afternoon for 30 cm depth, soil temperatures (daily, weekly, and monthly) could be predicted more efficiently with the help of corresponding mean air temperature than that of maximum and minimum. However, in afternoon, prediction of soil temperature at 5 and 15 cm depths were more precised for all the time intervals when maximum air temperature was used, except for weekly soil temperature at 15 cm, where the use of mean air temperature gave better prediction.

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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.

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Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product was used to assess early season drought condition over Maharashtra State of India in summer monsoon (kharif). AMSR-E Soil Moisture Index (AMSR-E SMI) was calculated using AMSR-E soil moisture and soil textural information. The index had normalization to soil matrix and could be composited over a time scale. The index had statistically significant correlation (R 2 value 0.6 to 0.7) with rainfall during early kharif season (June to August). Spatiotemporal dynamics of AMSR-E SMI and its inter annual variability (2007 to 2010) were analysed at meteorological subdivision level. Persistent dryness was found in 2008 (drought year) and soil wetness was near normal in other years. Intermittent short dry spells were also monitored and quantified. Spatio-temporal variation of intensity and persistence of soil wetness as depicted by AMSR-E SMI had good correspondence with progression of sown area over Maharashtra. Geospatial information of sowing period soil wetness condition derived from AMSR-E SMI could be vital supplementary information in assessment of early season drought.