Thenkabail, Prasad: USGS (Project Lead)
Project Funding:
2011 - 2012
NRA: 2010 NASA: HyspIRI Preparatory Activities Using Existing Imagery
Funded by NASA
Abstract:
The overarching goal of this study is to assess water use and water productivity of key world crops using Hyperion-ASTER and a large collection of in-situ field biological and spectral data. The study will be based on existing datasets, collected during the 2006 and 2007 crop growing seasons, over large-scale irrigated areas of the arid Syr Darya river basin (444,000 km2) in Central Asia where recent studies show snowmelt water supplies from Himalayas are on swift decrease. The irrigated cropland data acquired include: (a) Hyperion narrow-band data (5 images) from Earth Observing-1, (b) spectroradiometer data in 400-2500 nanometer, (c) broad-band data from ASTER as well as ETM+, ALI, IKONOS, and Quickbird, and (d) field-plot biological data. The field-plot data of 5 crops (wheat, cotton, maize, rice and alfalfa) were collected, every 15-20 days, throughout the summer crop growing seasons (April-October) of 2006 and 2007 for a total of 1003 sample locations and consisted of: several thousand spectral measurements, crop variables (e.g. biomass, yield), soil salinity, water variables (e.g., inflow, outflow), and meteorological data (e.g., rainfall, ET).
The study of 5 crops using Hyperion-ASTER-field spectral and biological data will: (a) develop and test water productivity models (WPMs), (b) establish shifts in phenology depicting canopies’ integrated response to environmental change andor controlled-planted by humans, (c) highlight best performing hyperspectral water indices (HWIs); and (d) establish chief causes of water productivity variations and identify hyperspectral wavebands and indices that are most sensitive to them.
The outcome of the research will lead to: 1. Determining proportion of irrigated areas in low, medium, or high water productivity and their drivers (e.g., management practices, soil salinity); 2. Establishing water use of 5 irrigated crops, 3. Determining dynamics of water and nutrient stress; 4. Recommending optimal Hyperion wavebands required to best study irrigated cropland characteristics; and 5. Comparing the performances of narrow-band data with broad-band data.
More details may be found in the following project profile(s):
