Research Papers

Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"

[+] Author Affiliations
Xueliang Cai

International Water Management Institute, PO Box 2075, Colombo, West Province 2075 Sri Lanka

Prasad S. Thenkabail

Southwest Geographic Science Center, U.S. Geological Survey, Flagstaff, Arizona 86001

Chandrashekhar M. Biradar

University of Oklahoma, 101 David L. Boren Blvd., Norman, Oklahomoa 73109

Alexander Platonov, Murali Gumma

International Water Management Institute, PO Box 2075, Colombo, Sri Lanka

Venkateswarlu Dheeravath

United Nations Joint Logistic Center, Juba, South Sudan Sudan

Yafit Cohen

Institute of Agricultural Engineering, Bet-Dagan, 50250 Israel

Naftali Goldlshleger

Soil Erosion Research Station, Ministry of Agriculture, Israel

Eyal Ben Dor

The Remote Sensing and GIS Laboratory, Tel Aviv University, Tel-Aviv, 69778 Israel

Victor Alchanatis

Institute of Agricultural Engineering, Bet Dagan, 50250 Israel

Jagath Vithanage, Anputhas Markandu

International Water Management Institute, PO Box 2075, Colombo, Sri Lanka

J. Appl. Remote Sens. 3(1), 033557 (October 12, 2009). doi:10.1117/1.3257643
History: Received July 23, 2009; Revised October 5, 2009; Accepted October 8, 2009; October 12, 2009; Online October 12, 2009
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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.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Xueliang Cai ; Prasad S. Thenkabail ; Chandrashekhar M. Biradar ; Alexander Platonov ; Murali Gumma, et al.
"Water productivity mapping using remote sensing data of various resolutions to support "more crop per drop"", J. Appl. Remote Sens. 3(1), 033557 (October 12, 2009). ; http://dx.doi.org/10.1117/1.3257643


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