Project Title: Water Productivity Mapping for Irrigated Crops in California Using Farm-Level Assessments and Hyper-Spatial and Spectral Remote Sensing
Mendenhall Fellow: Michael Marshall, email@example.com
Duty Station: Flagstaff, Arizona
Start Date: February 28, 2011
Education: Ph.D. Geography, University of California–Santa Barbara, 2010
Research Advisor: Prasad Thenkabail, firstname.lastname@example.org
Project Description: In California, the water supply deficit is expected to increase to 2 million acre-feet by the end of this year, further straining a critical resource. Agricultural production is primarily watered through irrigation in California and comprises approximately 75 to 80 percent of the State’s annual water budget. Improving the water productivity of irrigated crops (“more crop per drop”) in the State could therefore significantly reduce water consumption in California. Crop water productivity (WP) is the ratio of crop biomass (or yield) to evapotranspiration (ET). Water productive fields have high yields and lose less moisture to the atmosphere via ET.
This project uses a suite of hyper-spatial and spectral remote sensing data, geographic information system (GIS) layers, and groundtruth data to estimate WP for four key irrigated crops in California (alfalfa, corn, cotton, and rice). Groundtruth data include information on crop spectral properties and physiology, topography, soils, irrigation practices and hydrology, and meteorology. Sampling is conducted near sites measuring ET using eddy covariance, lysimeters, or surface renewal systems.Following the field campaign at the end of summer 2011, the project will meet four objectives:
Choudhury, B.J.,Ahmed, N.U., Idso, S.B., Reginato, R.J., and Daughtry, C.S.T., 1994, Relations between evaporation coefficients and vegetation indices studied by model simulations: Remote Sensing of Environment, v. 50, p. 1–17.
Thenkabail, P.S., Enclonab, E.A., Ashtonb, M.S., and Van Der Meer, B., 2004, Accuracy assessments of hyperspectral waveband performance for vegetation analysis application: Remote Sensing of Environment,v. 91, p. 354–376.
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Last modified: 16:08:30 Thu 13 Dec 2012