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Water productivity mapping (WPM) using landsat ETM+ data for the irrigated croplands of the Syrdarya river basin in Central Asia
Year:
2008
Source of publication :
sensors (source)
Authors :
Alchanatis, Victor
;
.
Cohen, Yafit
;
.
Volume :
8
Co-Authors:
Platonov, A., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka, International Water Management Institute (IWMI), Apt. 123, House 6, Murtazaeva Street, Tashkent 700000, Uzbekistan
Thenkabail, P.S., U.S. Geological Survey, 2255 N. Gemini Drive, Flagstaff, AZ 86001, United States
Biradar, C.M., University of Oklahoma, 101 David L. Boren Blvd., Norman, OK 73019, United States
Cai, X., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Gumma, M., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Dheeravath, V., United Nations Joint Logistics Center, Juba, Sudan
Cohen, Y., Institute of Agricultural Engineering, ARO, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO, Israel
Goldshlager, N., University of Soil Sciences, ARO, Volcani Center, Bet Dagan 50250, Israel
Ben-Dor, E., Department of Geography, Tel-Aviv University, P.O. B. 39040, 69989, Israel
Vithanage, J., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Manthrithilake, H., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Kendjabaev, S., Central Asian Scientific Research Institute of Irrigation, Block 11, Karasu-4, Tashkent, 700187, Uzbekistan
Isaev, S., Scientific Research Institute for Growing Cotton, Tashkent, Uzbekistan
Facilitators :
From page:
8156
To page:
8180
(
Total pages:
25
)
Abstract:
The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing "more crop per drop" (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m 3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the "hot" pixels (zero ET) and "cold" pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.
Note:
Related Files :
Crops
food supply
meteorology
remote sensing
Surface energy balance modeling
Show More
Related Content
More details
DOI :
10.3390/s8128156
Article number:
Affiliations:
Database:
Scopus
Publication Type:
Review
;
.
Language:
English
Editors' remarks:
ID:
30764
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:57
Scientific Publication
Water productivity mapping (WPM) using landsat ETM+ data for the irrigated croplands of the Syrdarya river basin in Central Asia
8
Platonov, A., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka, International Water Management Institute (IWMI), Apt. 123, House 6, Murtazaeva Street, Tashkent 700000, Uzbekistan
Thenkabail, P.S., U.S. Geological Survey, 2255 N. Gemini Drive, Flagstaff, AZ 86001, United States
Biradar, C.M., University of Oklahoma, 101 David L. Boren Blvd., Norman, OK 73019, United States
Cai, X., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Gumma, M., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Dheeravath, V., United Nations Joint Logistics Center, Juba, Sudan
Cohen, Y., Institute of Agricultural Engineering, ARO, Israel
Alchanatis, V., Institute of Agricultural Engineering, ARO, Israel
Goldshlager, N., University of Soil Sciences, ARO, Volcani Center, Bet Dagan 50250, Israel
Ben-Dor, E., Department of Geography, Tel-Aviv University, P.O. B. 39040, 69989, Israel
Vithanage, J., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Manthrithilake, H., International Water Management Institute (IWMI), 127, Sunil Mawatha, Colombo, Sri Lanka
Kendjabaev, S., Central Asian Scientific Research Institute of Irrigation, Block 11, Karasu-4, Tashkent, 700187, Uzbekistan
Isaev, S., Scientific Research Institute for Growing Cotton, Tashkent, Uzbekistan
Water productivity mapping (WPM) using landsat ETM+ data for the irrigated croplands of the Syrdarya river basin in Central Asia
The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing "more crop per drop" (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m 3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the "hot" pixels (zero ET) and "cold" pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.
Scientific Publication
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