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Journal of Applied Remote Sensing

Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"
Author(s): Xueliang Cai; Prasad S. Thenkabail; Chandrashekhar M. Biradar; Alexander Platonov; Muralikrishna Gumma; Venkateswarlu Dheeravath; Yafit Cohen; Naftali Goldshleger; Eyal Ben-Dor; Victor Alchanatis; Jagath Vithanage; Anputhas Markandu
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Paper Abstract

The overarching goal of this research was to map crop water productivity using satellite sensor data at various spectral, spatial, radiometric, and temporal resolutions involving: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) 500m, (b) MODIS 250m, (c) Landsat enhanced thematic mapper plus (ETM+) 60m thermal, (d) Indian Remote Sensing Satellite (IRS) 23.5 m, and (e) Quickbird 2.44 m data. The spectro-biophysical models were developed using IRS and Quickbird satellite data for wet biomass, dry biomass, leaf area index, and grain yield for 5 crops: (a) cotton, (b) maize, (c) winter wheat, (d) rice, and (e) alfalfa in the Sry Darya basin, Central Asia. Crop-specific productivity maps were developed by applying the best spectro-biophysical models for the respective delineated crop types. Water use maps were produced using simplified surface energy balance (SSEB) model by multiplying evaporative fraction derived from Landsat ETM+ thermal data by potential ET. The water productivity (WP) maps were then derived by dividing the crop productivity maps by water use maps. The results of cotton crop, an overwhelmingly predominant crop in Central Asian Study area, showed that about 55% area had low WP of < 0.3 kg/m3, 34% had moderate WP of 0.3-0.4 kg/m3, and only 11% area had high WP > 0.4 kg/m3. The trends were similar for other crops. These results indicated that there is highly significant scope to increase WP (to grow "more crop per drop") through better water and cropland management practices in the low WP areas, which will substantially enhance food security of the ballooning populations without having to increase: (a) cropland areas, and\or (b) irrigation water allocations.

Paper Details

Date Published: 1 October 2009
PDF: 23 pages
J. Appl. Remote Sens. 3(1) 033557 doi: 10.1117/1.3257643
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Xueliang Cai, International Water Managemant Institute (Sri Lanka)
Prasad S. Thenkabail, U.S. Geological Survey (United States)
Chandrashekhar M. Biradar, Univ. of Oklahoma (United States)
Alexander Platonov, International Water Management Institute (Sri Lanka)
Muralikrishna Gumma, International Water Management Institute (Sri Lanka)
Venkateswarlu Dheeravath
Yafit Cohen, Agricultural Research Organization (Israel)
Naftali Goldshleger, Ministry of Agriculture and Rural Development (Israel)
Eyal Ben-Dor, Tel-Aviv Univ. (Israel)
Victor Alchanatis
Jagath Vithanage, International Water Management Institute (Sri Lanka)
Anputhas Markandu, International Water Management Institute (Sri Lanka)


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