Your search found 3 records
1 Platonov, Alexander; Thenkabail, Prasad; Biradar, Chandrashekhar M.; Cai, Xueliang; Gumma, Murali Krishna; Dheeravath, Venkateswarlu; Cohen, Y.; Alchanatis, V.; Goldshlager, N.; Ben-Dor, E.; Vithanage, Jagath; Manthrithilake, Herath; Kendjabaev, S.; Isaev, S. 2008. Water productivity mapping (WPM) using Landsat ETM+ data for the irrigated croplands of the Syrdarya River Basin in Central Asia. Sensors, 8:8156-8180.
Water productivity ; Mapping ; Remote sensing ; Water use ; Crops ; Productivity ; Crop yield ; Models ; Evapotranspiration ; Irrigated farming ; River basins / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041566)
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 (m3/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.
2 Thenkabail, Prasad S.; Biradar, Chandrashekhar M.; Noojipady, P.; Dheeravath, Venkateswarlu; Li, Yuan Jie; Velpuri, M.; Reddy, G. P. O.; Cai, Xueliang; Gumma, Murali Krishna; Turral, Hugh; Vithanage, Jagath; Schull, M.; Dutta, R. 2008. A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium. Colombo, Sri Lanka: International Water Management Institute (IWMI) 62p. [doi: https://doi.org/10.5337/2011.0024]
(Location: IWMI HQ Call no: e-copy only Record No: H042115)
(3.00 MB) (3MB)
3 Biradar, Chandrashekhar M.; Thenkabail, Prasad S.; Noojipady, P.; Li, Yuan Jie; Dheeravath, Venkateswarlu; Turral, Hugh; Velpuri, Manohar; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Cai, X. L.; Xiao, X.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Mohideen, Sadir. 2009. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. International Journal of Applied Earth Observation and Geoinformation, 11(2):114-129. [doi: https://doi.org/10.1016/j.jag.2008.11.002]
(Location: IWMI HQ Call no: e-copy only Record No: H042769)
The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2–8%, and the errors of commission of 19–36%.
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