Remote sensing moisture model could aid farmers

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Remote sensing moisture model could aid farmers

Global farmers could get better decision-making help as refinements are made to North Alabama soil moisture modeling research being done by an atmospheric science doctoral student at The University of Alabama in Huntsville (UAH).

The models indicate how much added moisture would be needed in a given area versus historical data to achieve various crop yields, and they could aid in making expensive infrastructure investments by helping to determine their economic viability.

"The important thing that I want to stress is that this is not a predictive model, it is a decision-support model. It helps farmers and officials make decisions based on historical weather patterns," says doctoral student Vikalp Mishra. In areas where water is in short supply, irrigation infrastructure can be expensive and the model could help to determine its economic cost effectiveness.

Mishra was the primary author of a paper with his advisor and UAH associate professor of atmospheric science Dr. John Mecikalski, UAH Earth System Science Center principle researcher James Cruise, and researchers from the University of Maryland-College Park and the U.S. Dept. of Agriculture's Hydrology and Remote Sensing Laboratory in Beltsville, Md. ("A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields,"Vikalp Mishra,James F. Cruise,John R. Mecikalski,Christopher R. Hain andMartha C. Anderson;Remote Sensing,2013,5(7), 3331-3356; doi:10.3390/rs5073331).

The model uses satellite data to determine the amount of soil moisture present and then estimates yields based on available moisture. Water is at the center of nearly all farming decisions. It affects the crop cultivar, the variety of seed planted, the amount and type of fertilizer required and the amount of irrigation needed to produce a given weight of grain.

Researchers begin by using satellite derived evapotranspiration estimates at thermal infrared bands to deduce the amount of moisture being transpired by plants. Moisture data are derived from the Geostationary Operational Environmental Satellites (GOES). GOES data are inputted into the Atmosphere-Land Exchange Inverse (ALEXI) model, previously developed by Dr. Mecikalski and others. The ALEXI model calculates the evapotranspiration rates. The soil moisture is directly related to the evapotranspiration and the percentage of canopy cover so that the amount of moisture in the soil up to the rooting depth can be deduced.

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