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

Prasad S. Thenkabail
2009 Journal of Applied Remote Sensing  
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
more » ... ass, 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/m 3 , 34% had moderate WP of 0.3-0.4 kg/m 3 , and only 11% area had high WP > 0.4 kg/m 3 . 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. to 129.15.14.53. Terms of Use: http://spiedl.org/terms agriculture: thermal and hyper-spectral imagery for monitoring and mapping water status and nitrogen level in various crops and orchards; Spatio-temporal analysis of pests and diseases: Medfly, Olive fly, Pear Psylla, and soil-borne diseases in potato and nuts; Development of spatial knowledge-based decision support systems for pest control; Remote sensing for recognition and mapping of crop types and delineation of Green corridors. Naftali Goldlshleger is a Researcher at Eyal Ben-Dor is a full professor at the Tel Aviv University (TAU) and was the chair of the Geography and Human Environment Department at Tel-Aviv University from 2005-2009. Currently he is serving as the head of the Remote Sensing Laboratory (RSL) within this department. He has more than 16 years experience in remote sensing of the Earth, with special emphasis on the Imaging Spectroscopy technology (IS) and soil spectroscopy. He has a strong background in soil science, spectroscopy, and remote sensing processing and is the author of more than 200 papers, book chapters and technical reports.
doi:10.1117/1.3257643 fatcat:6mcv3a4ngve7beppj36q3zpxwe