Your search found 12 records
1 Thenkabail, Prasad; Biradar, Chandrashekhar; Turral, Hugh; Noojipady, Praveen; Li, Yuanjie; Vithanage, Jagath; Dheeravath, Venkateswarlu; Velpuri, Manohar; Schull, M.; Cai, Xueliang; Dutta, Rishiraj. 2006. An irrigated area map of the world (1999) derived from remote sensing. Colombo, Sri Lanka: International Water Management Institute (IWMI). 65p. (IWMI Research Report 105) [doi: https://doi.org/10.3910/2009.105]
(Location: IWMI-HQ Call no: IWMI 631.7.1 G000 THE Record No: H039270)
(1.57MB)
This document summarizes the materials and methods used to create a series of maps of irrigated areas of the world using remote sensing approaches. These maps are complementary to existing statistics (FAO-Aquastat) and the GISderived maps (FAO/University of Frankfurt Global irrigated area map). The document also provides details of how the estimates of global irrigated areas in one main season (net) and more than one season (intensity or annualized) were derived.
2 Kulawardhana, Wasantha; Thenkabail, Prasad; Masiyandima, Mutsa; Biradar, Chandrashekhar; Vithanage, Jagath; Finlayson, Max; Gunasinghe, Sarath; Alankara, Ranjith. 2006. Evaluation of different methods for delineation of wetlands in Limpopo River Basin using Landsat ETM + and SRTM data. In Proceedings, GlobWetland: Looking at Wetlands from Space, Frascati, Italy, 19-20 October 2006. 4p.
Wetlands ; Mapping ; Remote sensing / South Africa / Mozambique / Zimbabwe / Botswana / Limpopo River Basin
(Location: IWMI-HQ Call no: IWMI 333.918 G000 REB Record No: H039731)
3 Thenkabail, Prasad; Biradar, Chandrashekhar; Noojipady, Praveen; Islam, Aminul; Vithanage, Jagath; Velpuri, Manohar; Dheeravath, Venkateswarlu; Kulawardhana, Wasantha; Jie, Li Yuan; Gunasinghe, Sarath; Alankara, Ranjith. 2006. International Water Management Institute’s Data Storehouse Pathway (IWMIDSP): A unique data and knowledge gateway of spatial data with emphasis on river basins. In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6418, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 6418(64181R):1-6.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 THE Record No: H039738)
4 Biradar, Chandrashekhar; Thenkabail, Prasad; Turral, Hugh; Noojipady, Praveen; Jie, Li Yuan; Velpuri, Manohar; Dheeravath, Venkateswarlu; Vithanage, Jagath; Schull, M.; Cai, X. L.; Gumma, Murali Krishna; Rishiraj, D. 2006. A global map of rainfed cropland areas at the end of last millennium using remote sensing and geospatial techniques. In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6418, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 64181Q. 5p.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 BIR Record No: H039737)
5 Thenkabail, Prasad; Biradar, Chandrashekhar; Noojipady, Praveen; Islam, Aminul; Velpuri, Manohar; Vithanage, Jagath; Kulawardhana, Wasantha; Jie, Li Yuan; Venkateswarlu, Dheeravath; Gunasinghe, Sarath; Alankara, Ranjith. 2006. The spatial data and knowledge gateways at the International Water Management Institute (IWMI) In Li, D.; Xia, L. (Eds.). Geoinformatics 2006: GNSS and Integrated Geospatial applications. Proceedings of SPIE Vol.6421, Wuhan, China, 28-29 October 2006. Bellingham, Washington, USA: SPIE – The International Society for Optical Engineering. 64211Z. 10p.
(Location: IWMI-HQ Call no: IWMI 621.3678 G000 THE Record No: H039739)
6 Kulawardhana, Wasantha; Thenkabail, Prasad; Vithanage, Jagath; Biradar, Chandrashekhar; Islam, Aminul; Gunasinghe, Sarath; Alankara, Ranjith. 2007. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. Journal of Spatial Hydrology, 2. 47p.
Wetlands ; Remote sensing ; Mapping ; River basins / Botswana / Zimbabwe / South Africa / Mozambique / Limpopo River Basin
(Location: IWMI HQ Call no: IWMI 333.918 G178 KUL Record No: H040222)
7 Biradar, Chandrashekhar; Thenkabail, Prasad; Islam, Aminul; Anputhas, Markandu; Tharme, Rebecca; Vithanage, Jagath; Alankara, Ranjith; Gunasinghe, Sarath. 2007. Establishing the best spectral bands and timing of imagery for land use-land cover (LULC) class separability using Landsat ETM+ and Terra MODIS data. Canadian Journal of Remote Sensing, 33(5):431-444.
Remote sensing ; Land use ; Land cover mapping ; Irrigated farming ; Irrigation programs / Sri Lanka / Uda Walawe River Basin
(Location: IWMI HQ Call no: IWMI 333.918 G744 BIR Record No: H040453)
8 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.
9 Biradar, C. M.; Thenkabail, Prasad S.; Platonov, Alexander; Xiao, X.; Geerken, R.; Noojipady, P.; Turral, H.; Vithanage, Jagath. 2008. Water productivity mapping methods using remote sensing. Journal of Applied Remote Sensing, 2(1):023544. 22p. (Published online only)
Water productivity ; Mapping ; Remote sensing ; Vegetation index ; Evapotranspiration ; Wheat ; Rice ; Cotton ; Irrigated farming / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041669)
The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM)(m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with nly about 10% area in high WP.
10 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)
11 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]
Water productivity ; Crops ; Water use ; Evapotranspiration ; Mapping ; Remote sensing ; Models / Central Asia / Kyrgyzstan / Tajikistan / Uzbekistan / Kazakhstan / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H042408)
(4.07 MB)
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.
12 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Li, Yuan Jie; Velpuri, N. M.; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Turral, H.; Cai, Xueliang; Vithanage, Jagath; Schull, M. A.; Dutta, R. 2009. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium. International Journal of Remote Sensing, 30(14):3679-3733. [doi: https://doi.org/10.1080/01431160802698919]
(Location: IWMI HQ Call no: e-copy only Record No: H042409)
(18.23 MB)
A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997–1999, (b) Syste`me pour l’Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961–2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST-SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) ‘zoom-in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAMwas produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252Mha from season one, (b) 174Mha from season two and (c) 41Mha from continuous yearround crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).
Powered by DB/Text WebPublisher, from
