Abstract
The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period.
Highlights
Hydrological and agricultural applications of soil moisture data are numerous
1.5.1 Objectives: 1. To build the database of soil moisture time series generated under Essential Climate Variable (ECV) production system of ESA and to generate soil moisture index for spatio-temporal comparisons
The analysis has shown that soil moisture variability is not persistent over the three months of crop sowing period in majority pixels
Summary
Hydrological and agricultural applications of soil moisture data are numerous. Agricultural drought is the most important phenomena to be directly influenced by soil moisture. Soil moisture is an important physical data set playing vital role in environmental and climate system related research It plays a key role in hydrology and land surface processes. Microwave remote sensing measurements of bare soil surfaces are very sensitive to the water content in the surface layer due to the pronounced increase in the soil dielectric constant with increasing water content. This is the fundamental reason why any microwave technique, in the low-frequency microwave region from 1 to 10 GHz, offers the opportunity to measure soil moisture in a relatively direct manner. The generation of the long-term (30+ years) soil moisture data set involves three step blending of active and passive soil moisture datasets
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