© 2014 Peter Free
Citation — to study
Vikalp Mishra, James F. Cruise, John R. Mecikalski, Christopher R. Hain, and Martha C. Anderson, A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields, Remote Sensing 5(7): 3331-3356, DOI:10.3390/rs5073331 (July 2013)
Citation — to press release
University of Alabama in Huntsville, Remote sensing moisture model could aid farmers, UAH.edu (05 March 2014)
Not the most timely of university press releases
The study to which the press release refers was published in July 2013.
I can’t help but envision the political dynamics that led to yesterday’s press release, 8 months late.
How the remote sensing idea works
Geostationary Operational Environmental Satellites deliver land surface moisture data to the team. The Atmosphere-Land Exchange Inverse computer model uses the GOES data to estimate vegetative evapotranspiration, which obviously varies according to the percentage of ground cover.
If most of the ground is plant-covered, most of the sensed moisture is assumed to be coming from evapotranspiration. Which, in turn, means that the plants’ root zone is being depleted of the water necessary to keep the plant cover transpiring.
However, if vegetation constitutes less than 30 percent ground coverage, the computer model apportions more of the sensed air moisture to evaporation from the top soil layer, which one can presume to mean that there is comparatively more soil moisture still contained in the root zone.
A computer model breaks down the probabilities for which water is coming from where. That estimate is run through the Decision Support System for Agrotechnology Transfer computer program. The 20 year old DSSAT application simulates input-outcomes for 28 crops by using “weather, plant spacing, cultivar, fertilizer, soil type and fertility” as variables:
“What we found was that our soil moisture measurements and estimated crop yields were significantly comparable with county averaged National Agricultural Statistics Service yield data and ground-based precipitation-induced DSSAT results,” [graduate student and researcher Vikalp] Mishra says.
© 2014 University of Alabama in Huntsville, Remote sensing moisture model could aid farmers, UAH.edu (05 March 2014)
In other words, satellite moisture sensing and crop input-output modeling appear to properly estimate real world data regarding how much to irrigate.
The moral? — A rewarding practical way to harvest value from satellite data
Professor and lead researcher James Cruise adds that the research platform “could result in an online database that farmers in Alabama and elsewhere could use to help them make decisions to cope with variability in annual rainfall.”
That would be really cool.