NASA’s scientists developed SMAP satellite to combat Drought
NASA developed a new satellite to predict the severity of droughts worldwide and help farmers maximize the crop yield.
NASA scientists, including one of Indian-origin, developed Soil Moisture Active Passive (SMAP) satellite that can predict the severity of droughts worldwide and help farmers maximize the crop yield.
The mission is scheduled to launch in November 2014 and will collect the local data of agricultural and water managers that are needed worldwide.
About NASA’s Soil Moisture Active Passive (SMAP) satellite
• SMAP uses two microwave instruments to monitor the top 2 inches of soil on Earth’s surface.
• Together, the instruments create soil moisture estimates with a resolution of about 6 miles (9 kilometers), mapping the entire globe every two or three days.
Uses of SMAP
These measurements made by theSMAP will enable science and applications users to:
• To understand processes that link the terrestrial water, energy and carbon cycles
• To estimate global water and energy fluxes at the land surface
• To quantify net carbon flux in boreal landscapes
• To enhance weather and climate forecast skill
• To develop improved flood prediction and drought monitoring capability.
• SMAP can also assist in predicting how dramatic drought will be, and then its data can help farmers plan their recovery from drought.
Without enough water in the soil, seeds can’t sprout roots and leaves can’t perform photosynthesis and agriculture can’t be sustained. At present, there is no ground- or satellite-based global network to monitor the soil moisture at a local level. Farmers, scientists and resource managers can place sensors in the ground but these only provide spot measurements and are rare across some critical agricultural areas in Africa, Asia and Latin America.
The European Space Agency’s Soil Moisture and Ocean Salinity mission measures soil moisture at a resolution of 31 miles (50 kilometers), but because soil moisture can vary on a much smaller scale, its data are most useful in broad forecasts.
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