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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 pro...

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Autores principales: Platonov, Alexander, Thenkabail, Prasad S., Biradar, Chandrashekhar M., Cai, Xueliang, Gumma, Muralikrishna, Dheeravath, Venkateswarlu, Cohen, Yafit, Alchanatis, Victor, Goldshlager, Naftali, Ben-Dor, Eyal, Vithanage, Jagath, Manthrithilake, Herath, Kendjabaev, Shavkat, Isaev, Sabirjan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791012/
https://www.ncbi.nlm.nih.gov/pubmed/27873981
http://dx.doi.org/10.3390/s8128156
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author Platonov, Alexander
Thenkabail, Prasad S.
Biradar, Chandrashekhar M.
Cai, Xueliang
Gumma, Muralikrishna
Dheeravath, Venkateswarlu
Cohen, Yafit
Alchanatis, Victor
Goldshlager, Naftali
Ben-Dor, Eyal
Vithanage, Jagath
Manthrithilake, Herath
Kendjabaev, Shavkat
Isaev, Sabirjan
author_facet Platonov, Alexander
Thenkabail, Prasad S.
Biradar, Chandrashekhar M.
Cai, Xueliang
Gumma, Muralikrishna
Dheeravath, Venkateswarlu
Cohen, Yafit
Alchanatis, Victor
Goldshlager, Naftali
Ben-Dor, Eyal
Vithanage, Jagath
Manthrithilake, Herath
Kendjabaev, Shavkat
Isaev, Sabirjan
author_sort Platonov, Alexander
collection PubMed
description 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/m(3)) 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/m(3)) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m(3), 11% of the area having WP in range of 0.30-0.36 kg/m(3), and only 2% of the area with WP greater than 0.36 kg/m(3). 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.
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spelling pubmed-37910122013-10-18 Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia Platonov, Alexander Thenkabail, Prasad S. Biradar, Chandrashekhar M. Cai, Xueliang Gumma, Muralikrishna Dheeravath, Venkateswarlu Cohen, Yafit Alchanatis, Victor Goldshlager, Naftali Ben-Dor, Eyal Vithanage, Jagath Manthrithilake, Herath Kendjabaev, Shavkat Isaev, Sabirjan Sensors (Basel) Review 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/m(3)) 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/m(3)) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m(3), 11% of the area having WP in range of 0.30-0.36 kg/m(3), and only 2% of the area with WP greater than 0.36 kg/m(3). 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. Molecular Diversity Preservation International (MDPI) 2008-12-10 /pmc/articles/PMC3791012/ /pubmed/27873981 http://dx.doi.org/10.3390/s8128156 Text en © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Review
Platonov, Alexander
Thenkabail, Prasad S.
Biradar, Chandrashekhar M.
Cai, Xueliang
Gumma, Muralikrishna
Dheeravath, Venkateswarlu
Cohen, Yafit
Alchanatis, Victor
Goldshlager, Naftali
Ben-Dor, Eyal
Vithanage, Jagath
Manthrithilake, Herath
Kendjabaev, Shavkat
Isaev, Sabirjan
Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title_full Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title_fullStr Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title_full_unstemmed Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title_short Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia
title_sort water productivity mapping (wpm) using landsat etm+ data for the irrigated croplands of the syrdarya river basin in central asia
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3791012/
https://www.ncbi.nlm.nih.gov/pubmed/27873981
http://dx.doi.org/10.3390/s8128156
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