Water productivity mapping using remote sensing data of various resolutions to support more crop per drop
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 plu...
| Autores principales: | , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
International Society for Optical Engineering
2009
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/40587 |
| _version_ | 1855519228048703488 |
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| author | Xueliang Cai Thenkabail, Prasad S. Biradar, Chandrashekhar M. Platonov, Alexander Gumma, Murali K. Dheeravath, Venkateswarlu Cohen, Y. Goldlshleger, F. Ben-Dor, E. Alchanatis, V. Vithanage, Jagath Anputhas, Markandu |
| author_browse | Alchanatis, V. Anputhas, Markandu Ben-Dor, E. Biradar, Chandrashekhar M. Cohen, Y. Dheeravath, Venkateswarlu Goldlshleger, F. Gumma, Murali K. Platonov, Alexander Thenkabail, Prasad S. Vithanage, Jagath Xueliang Cai |
| author_facet | Xueliang Cai Thenkabail, Prasad S. Biradar, Chandrashekhar M. Platonov, Alexander Gumma, Murali K. Dheeravath, Venkateswarlu Cohen, Y. Goldlshleger, F. Ben-Dor, E. Alchanatis, V. Vithanage, Jagath Anputhas, Markandu |
| author_sort | Xueliang Cai |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace40587 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| publisher | International Society for Optical Engineering |
| publisherStr | International Society for Optical Engineering |
| record_format | dspace |
| spelling | CGSpace405872025-12-08T10:29:22Z Water productivity mapping using remote sensing data of various resolutions to support more crop per drop Xueliang Cai Thenkabail, Prasad S. Biradar, Chandrashekhar M. Platonov, Alexander Gumma, Murali K. Dheeravath, Venkateswarlu Cohen, Y. Goldlshleger, F. Ben-Dor, E. Alchanatis, V. Vithanage, Jagath Anputhas, Markandu water productivity crops water use evapotranspiration mapping remote sensing models 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. 2009-10-01 2014-06-13T14:47:58Z 2014-06-13T14:47:58Z Journal Article https://hdl.handle.net/10568/40587 en Limited Access International Society for Optical Engineering Cai, Xueliang; Thenkabail, P. S.; Biradar, C. M.; Platonov, Alexander; Gumma, Murali Krishna; Dheeravath, V.; Cohen, Y.; Goldlshleger, F.; Ben-Dor, E.; Alchanatis, V.; Vithanage, Jagath; Anputhas, Markandu. 2009. Water productivity mapping using remote sensing data of various resolutions to support more crop per drop. Journal of Applied Remote Sensing, 3(033557). 23p. doi: https://doi.org/10.1117/1.3257643 |
| spellingShingle | water productivity crops water use evapotranspiration mapping remote sensing models Xueliang Cai Thenkabail, Prasad S. Biradar, Chandrashekhar M. Platonov, Alexander Gumma, Murali K. Dheeravath, Venkateswarlu Cohen, Y. Goldlshleger, F. Ben-Dor, E. Alchanatis, V. Vithanage, Jagath Anputhas, Markandu Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title | Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title_full | Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title_fullStr | Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title_full_unstemmed | Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title_short | Water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| title_sort | water productivity mapping using remote sensing data of various resolutions to support more crop per drop |
| topic | water productivity crops water use evapotranspiration mapping remote sensing models |
| url | https://hdl.handle.net/10568/40587 |
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