Your search found 11 records
1 Thenkabail, Prasad S.; Biradar, C. M.; Noojipady, P.; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Y. J.; Velpuri, M.; Gumma, Murali Krishna; Pandey, Suraj. 2007. Sub-pixel area calculation methods for estimating irrigated areas. Sensors, 7: 2519-2538.
(Location: IWMI HQ Call no: IWMI 631.7.1 G000 THE Record No: H040450)
2 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.
3 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)
4 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).
5 Li, Y. J.; Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Gangalakunta, O. R. P.; Cai, Xueliang. 2009. A history of irrigated areas of the world. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.13-37. (Taylor & Francis Series in Remote Sensing Applications)
Irrigated land ; History ; Irrigation programs ; Statistics / China / India / Egypt / Peru / Indus River Basin / Tigris River Basin / Euphrates River Basin
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042418)
(1.04 MB)
6 Thenkabail, P. S.; Biradar, C. M.; Noojipady, P.; Dheeravath, V.; Gumma, Murali Krishna; Li, Y. J.; Velpuri, M.; Gangalakunta, O. R. P. 2009. Global irrigated area maps (GIAM) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.41-117. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042419)
(2.98 MB)
7 Gangalakunta, O. R. P.; Dheeravath, V.; Thenkabail, P. S.; Chandrakantha, G.; Biradar, C. M.; Noojipady, P.; Velpuri, M.; Kumar, M. A. 2009. Irrigated areas of India derived from satellite sensors and national statistics: a way forward from GIAM experience. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.139-176. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042421)
8 Biradar, C. M.; Thenkabail, P. S.; Noojipady, P.; Dheeravath, V.; Velpuri, M.; Turral, H.; Cai, Xueliang; Gumma, Murali Krishna; Gangalakunta, O. R. P.; Schull, M. A.; Alankara, Ranjith; Gunasinghe, Sarath; Xiao, X. 2009. Global map of rainfed cropland areas (GMRCA) and statistics using remote sensing. In Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M. (Eds.). Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. pp.357-389. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042430)
(1.40 MB)
9 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%.
10 Velpuri, N. M.; Thenkabail, P. S.; Gumma, Murali Krishna; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L. 2009. Influence of resolution in irrigated area mapping and area estimation. Photogrammetric Engineering and Remote Sensing, 75(12):1383-1395.
Remote sensing ; Satellite surveys ; Mapping ; Irrigated sites ; Estimation ; River basins ; Surface irrigation ; Groundwater irrigation ; Conjunctive use / India / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H043443)
(3.31 MB)
The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that “the finer the spatial resolution of the sensor used, greater was the irrigated area derived,” since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent).
11 Dheeravath, V.; Thenkabail, P. S.; Chandrakantha, G.; Noojipady, P.; Reddy, G. P. O.; Biradar, C. M.; Gumma, Murali Krishna; Velpuri, M. 2010. Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1):42-59. [doi: https://doi.org/10.1016/j.isprsjprs.2009.08.004]
(Location: IWMI HQ Call no: e-copy only Record No: H043479)
(5.30 MB)
The overarching goal of this research was to develop methods and protocols for mapping irrigated areas using a Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series, to generate irrigated area statistics, and to compare these with ground- and census-based statistics. The primary mega-file data-cube (MFDC), comparable to a hyper-spectral data cube, used in this study consisted of 952 bands of data in a single file that were derived from MODIS 500 m, 7-band reflectance data acquired every 8-days during 2001–2003. The methods consisted of (a) segmenting the 952-band MFDC based not only on elevation-precipitation-temperature zones but on major and minor irrigated command area boundaries obtained from India’s Central Board of Irrigation and Power (CBIP), (b) developing a large ideal spectral data bank (ISDB) of irrigated areas for India, (c) adopting quantitative spectral matching techniques (SMTs) such as the spectral correlation similarity (SCS) R2-value, (d) establishing a comprehensive set of protocols for class identification and labeling, and (e) comparing the results with the National Census data of India and field-plot data gathered during this project for determining accuracies, uncertainties and errors. The study produced irrigated area maps and statistics of India at the national and the subnational (e.g., state, district) levels based on MODIS data from 2001–2003. The Total Area Available for Irrigation (TAAI) and Annualized Irrigated Areas (AIAs) were 113 and 147 million hectares (MHa), respectively. The TAAI does not consider the intensity of irrigation, and its nearest equivalent is the net irrigated areas in the Indian National Statistics. The AIA considers intensity of irrigation and is the equivalent of “irrigated potential utilized (IPU)” reported by India’s Ministry of Water Resources (MoWR). The field-plot data collected during this project showed that the accuracy of TAAI classes was 88% with a 12% error of omission and 32% of error of commission. Comparisons between the AIA and IPU produced an R2-value of 0.84. However, AIA was consistently higher than IPU. The causes for differences were both in traditional approaches and remote sensing. The causes of uncertainties unique to traditional approaches were (a) inadequate accounting of minor irrigation (groundwater, small reservoirs and tanks), (b) unwillingness to share irrigated area statistics by the individual Indian states because of their stakes, (c) absence of comprehensive statistical analyses of reported data, and (d) subjectivity involved in observation-based data collection process. The causes of uncertainties unique to remote sensing approaches were (a) irrigated area fraction estimate and related sub-pixel area computations and (b) resolution of the imagery. The causes of uncertainties common in both traditional and remote sensing approaches were definitions and methodological issues.
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