Your search found 15 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 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.
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 Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M.. (Eds.) 2009. Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. 476p. (Taylor & Francis Series in Remote Sensing Applications)
Remote sensing ; Mapping ; Irrigated land ; Irrigated farming ; Rainfed farming ; Evapotranspiration ; Cropping systems ; Food security / Asia / Central Asia / China / India / Pakistan / USA / UK / Africa / Middle East
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042416)
(2.65 MB)
Provides a comprehensive knowledge base in the use of satellite sensor-based maps and statisics for irrigated and rainfed croplands and available water that will ensure food security.
6 Turral, H.; Thenkabail, P. S.; Lyon, J. G.; Biradar, C. M.. 2009. Context, needed: the need and scope for mapping global irrigated and rain-fed areas. 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.3-11. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042417)
7 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)
8 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)
9 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)
10 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)
11 Thenkabail, P. S.; Lyon, J. G.; Turral, H.; Biradar, C. M.. (Eds.) 2009. Remote sensing of global croplands for food security. Boca Raton, FL, USA: CRC Press. 476p. (Taylor & Francis Series in Remote Sensing Applications)
Remote sensing ; Mapping ; Irrigated land ; Irrigated farming ; Rainfed farming ; Evapotranspiration ; Cropping systems ; Food security / Asia / Central Asia / China / India / Pakistan / USA / UK / Africa / Middle East
(Location: IWMI HQ Call no: 631.7.1 G000 THE c2 Record No: H042543)
Provides a comprehensive knowledge base in the use of satellite sensor-based maps and statisics for irrigated and rainfed croplands and available water that will ensure food security.
12 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.
13 Gumma, M. K.; Thenkabail, P. S.; Muralikrishna. I. V.; Velpuri, M. N.; Gangadhara Rao, Parthasaradhi; Dheeravath, V.; Biradar, C. M.; Acharya, N. Sreedhar; Gaur, A. 2011. Changes in agricultural cropland areas between a water-surplus year and a water-deficit year impacting food security, determined using MODIS 250 m time-series data and spectral matching techniques, in the Krishna River basin (India). International Journal of Remote Sensing, 32(12):3495-3520. [doi: https://doi.org/10.1080/01431161003749485]
Agricultural land ; Farmland ; Water availability ; Water use ; Water deficit ; Food security ; Models ; River basins ; Rain ; Rainfed farming ; Irrigated land ; Land use ; Land cover ; Climate change ; Satellite imagery ; Mapping ; Time series analysis ; Spectral analysis / India / Krishna River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H043968)
(1.46 MB)
The objective of this study was to investigate the changes in cropland areas as a result of water availability using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series data and spectral matching techniques (SMTs). The study was conducted in the Krishna River basin in India, a very large river basin with an area of 265 752 km2 (26 575 200 ha), comparing a water-surplus year (2000–2001) and a water-deficit year (2002–2003). The MODIS 250 m time-series data and SMTs were found ideal for agricultural cropland change detection over large areas and provided fuzzy classification accuracies of 61–100% for various land-use classes and 61–81% for the rain-fed and irrigated classes. The most mixing change occurred between rain-fed cropland areas and informally irrigated (e.g. groundwater and small reservoir) areas. Hence separation of these two classes was the most difficult. The MODIS 250 m-derived irrigated cropland areas for the districts were highly correlated with the Indian Bureau of Statistics data, with R2-values between 0.82 and 0.86. The change in the net area irrigated was modest, with an irrigated area of 8 669 881 ha during the water-surplus year, as compared with 7 718 900 ha during the water-deficit year. However, this is quite misleading as most of the major changes occurred in cropping intensity, such as changing from higher intensity to lower intensity (e.g. from double crop to single crop). The changes in cropping intensity of the agricultural cropland areas that took place in the water-deficit year (2002–2003) when compared with the water-surplus year (2000–2001) in the Krishna basin were: (a) 1 078 564 ha changed from double crop to single crop, (b) 1 461 177 ha changed from continuous crop to single crop, (c) 704 172 ha changed from irrigated single crop to fallow and (d) 1 314 522 ha changed from minor irrigation (e.g. tanks, small reservoirs) to rain-fed. These are highly significant changes that will have strong impact on food security. Such changes may be expected all over the world in a changing climate.
14 Gangodagamage, C.; Biradar, C. M.; Islam, A.; Thenkabail, P. S. 2004. Shuttle Radar Topography Mission (SRTM) data for Sri Lanka: potential contributions in river basin research. In De Silva, R. P. (Ed.). Geo-informatics research and applications: proceedings of the First Symposium on Geo-informatics, Peradeniya, Sri Lanka, 30 July 2004. Peradeniya, Sri Lanka: Geo-Informatics Society of Sri Lanka (GISSL). pp.19-30.
River basins ; Catchment areas ; Radar ; Models ; Global Positioning Systems) ; Hydrology / Sri Lanka
(Location: IWMI HQ Call no: 621.3678 G000 DES Record No: H045954)
(1.11 MB)
15 Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, A.; Singh, S.; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, S.; Gupta, S.; Pujar, G.; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, P.; Singh, J. S.; Chitale, V.; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, D.; Karnatak, H.; Saran, S.; Amarnath, Giriraj; Padalia, H.; Kale, M.; Nandy, S.; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, C.; Singh, D. K.; Devagiri, G. M.; Talukdar, G.; Panigrahy, R. K.; Singh, H.; Sharma, J. R.; Haridasan, K.; Trivedi, S.; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, M.; Nagabhatla, N.; Prasad, N.; Tripathi, O. P.; Prasad, P. R. C.; Dash, P.; Qureshi, Q.; Tripathi, S. K.; Ramesh, B. R.; Gowda, B.; Tomar, S.; Romshoo, S.; Giriraj, S.; Ravan, S. A.; Behera, S. K.; Paul, S.; Das, A. K.; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, U.; Menon, A. R. R.; Srivastava, G.; Sharma, N. S.; Mohapatra, U. B.; Peddi, A.; Rashid, H.; Salroo, I.; Krishna, P. H.; Hajra, P. K.; Vergheese, A. O.; Matin, S.; Chaudhary, S. A.; Ghosh, S.; Lakshmi, U.; Rawat, D.; Ambastha, K.; Malik, A. H.; Devi, B. S. S.; Gowda, B.; Sharma, K. C.; Mukharjee, P.; Sharma, A.; Davidar, P.; Raju, R. R. V.; Katewa, S. S.; Kant, S.; Raju, V. S.; Uniyal, B. P.; Debnath, B.; Rout, D. K.; Thapa, R.; Joseph, S.; Chhetri, P.; Ramachandran, R. M. 2015. New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation, 39:142-159. [doi: https://doi.org/10.1016/j.jag.2015.03.003]
Satellite imagery ; Remote sensing ; Vegetation ; Climate change ; Temperature ; Precipitation ; Scrublands ; Grasslands ; Ecology ; Global positioning systems ; Land cover ; Assessment ; Cultivation / India
(Location: IWMI HQ Call no: e-copy only Record No: H047008)
(2.48 MB)
A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. Weused 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).
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