Your search found 167 records
1 Rao, B. R. M.; Dwivedi, R. S.; Sreenivas, K.; Khan, Q. I.; Ramana, K. V.; Thammappa, S. S,; Fyzee, M. A. 1998. An inventory of salt-affected soils and waterlogged areas in the Nagarjunsagar Canal Command Area of southern India, Using space-borne multispectral data. Land Degradation and Development, 9(4):357-367.
Soil degradation ; Irrigated land ; Soil surveys ; Waterlogging ; Remote sensing ; Sodic soils ; Land degradation ; Salinity / India / Nagarjunsagar
(Location: IWMI-HQ Call no: P 5862 Record No: H028909)
2 Biggs, Trent; Thenkabail, Prasad; Gumma, Murali; Scott, Christopher; Parthasaradhi, G. R.; Turral, Hugh. 2006. Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India. International Journal of Remote Sensing, 27(19):4245-4266.
Irrigated land ; Groundwater irrigation ; Surface irrigation ; Mapping ; River basins ; Time series analysis / India / Krishna Basin
(Location: IWMI-HQ Call no: IWMI 631.7.1 G635 BIGG Record No: H039379)
3 Thenkabail, Prasad; Gangadhara Rao, P.; Biggs, Trent; Krishna, M.; Turral, Hugh. 2007. Spectral matching techniques to determine historical land use/Land cover (LULC) and irrigated areas using time-series 0.1 degree AVHRR Pathfinder Datasets. Photogrammetric Engineering & Remote Sensing, 73(9):1029-1040.
Land use ; Land cover ; Irrigated land ; Time series analysis ; Remote sensing ; Mapping / India / Krishna River Basin
(Location: IWMI-HQ Call no: IWMI 631.7.1.1 G635 THE Record No: H039380)
4 Vyshpolsky, F. F.; Mukhamedjanov, K.; Bekbaev, U.; Qadir, Manzoor; Karimov, Akmal. 2006. Application of phosphogypsum for the amelioration of sealed soils of Southern Kazakhstan. Bulletin of Agricultural Sciences of Kazakhstan, 3:37-40.
(Location: IWMI-HQ Call no: IWMI 631.4 G772 VYS Record No: H039758)
5 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)
6 Worbes, M.; Botman, E.; Khamzina, A.; Tupitsa, A.; Martius, C.; Lamers, J. P. A. 2006. Scope and constraints for tree planting in the irrigated landscapes of the Aral Sea Basin: case studies in Khorezm Region, Uzbekistan. Bonn, Germany: Center for Development Research. 49p. (ZEF Discussion Papers on Development Policy 112)
Lakes ; Agroforestry ; Trees ; Windbreak trees ; Forests ; Legal aspects ; Irrigated land ; Farmers attitudes ; Legal aspects / Central Asia / Uzbekistan / Aral Sea Basin / Khorezm
(Location: IWMI HQ Call no: e-copy only Record No: H041351)
http://www.zef.de/fileadmin/webfiles/downloads/zef_dp/zefdp_112.pdf
https://vlibrary.iwmi.org/pdf/H041351.pdf
https://vlibrary.iwmi.org/pdf/H041351.pdf
7 Gumma, Murali Krishna; Thenkabail, Prasad S.; Gautam, N. C.; Gangadhara Rao, Parthasaradhi; Manohar, Velpuri. 2008. Irrigated area mapping using AVHRR, MODIS and LANDSAT ETM+ data for the Krishna River Basin, India. Technology Spectrum, 2(1): 1-11.
River basins ; Water scarcity ; Irrigation programs ; Irrigated land ; Remote sensing ; Mapping ; Time series analysis / India / Krishna River Basin
(Location: IWMI HQ Call no: IWMI 631.7.1 G635 GUM Record No: H041432)
Net irrigated area in the Krishna river basin is varying quiet frequently due to water scarcity. Accurate area and extent of irrigated area in the Krishna River Basin is not available. State Irrigation Department projects large area under irrigation in the Krishna River Basin, which is attributed to its prestigious irrigation projects. However, the irrigation projects do not fulfill the demand in the basin consequently the tail Enders grow dry crops. Remote sensing replaces costly and tedious data collection on the ground, which is non-destructive. The aim of the present study is to prepare a comprehensive land use/land cover (LU/LC) map using continuous time-series data of multiple resolutions. A methodology is developed to map irrigated area categories using LANDSAT ETM+ along with coarse resolution time series imagery from AVHRR and MODIS, SRTM elevation, and other secondary data. Major stress was towards discrimination of ground-water irrigated area from surface-water irrigated area, determining of cropping patterns in irrigated area using MODIS NDVI time- series, and use of non-traditional methods of accuracy assessment using, ancillary datasets like SRTM-DEM, precipitation and state census statistics. A regression of the 9 class areas against agricultural census data explained 73% and 74% of the variance in groundwater and surface water irrigated area, respectively.
8 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)
9 Gumma, Murali Krishna; Thenkabail, P. S.; Velpuri, N. M. 2009. Vegetation phenology to partition groundwater- from surface water-irrigated areas using MODIS 250-m time-series data for the Krishna River basin. In Bloschl, G.; van de Giesen, N.; Muralidharan, D.; Ren, L.; Seyler, F.; Sharma, U.; Vrba, J. (Eds.). Improving integrated surface and groundwater resources management in a vulnerable and changing world: proceedings of Symposium JS.3 at the Joint Convention of the International Association of Hydrological Sciences (IAHS) and the International Association of Hydrogeologists (IAH), Hyderabad, India, 6-12 September 2009. Wallingford, UK: International Association of Hydrological Sciences (IAHS) pp.271-281. (IAHS Publication 330)
Vegetation ; Phenology ; River basins ; Vegetation ; Maps ; Land cover ; Land use ; Groundwater irrigation ; Surface irrigation ; Canals ; Reservoirs ; Irrigated land ; Time series analysis ; Remote sensing / India / Krishna River basin
(Location: IWMI HQ Call no: e-copy only Record No: H042217)
(1.15 MB)
This paper describes a remote sensing based vegetation-phenology approach to accurately separate out and quantify groundwater irrigated areas from surface-water irrigated areas in the Krishna River basin (265 752 km2), India, using MODIS 250-m every 8-day near continuous time series for 2000–2001. Temporal variations in the Normalized Difference Vegetation Index (NDVI) pattern, depicting phenology, obtained for the irrigated classes enabled demarcation between: (a) irrigated surface-water double crop, (b) irrigated surface-water continuous crop, and (c) irrigated groundwater mixed crops. The NDVI patterns were found to be more consistent in areas irrigated with groundwater due to the continuity of water supply. Surface water availability, however, was dependent on canal water release that affected time of crop sowing and growth stages, which was in turn reflected in the NDVI pattern. Double-cropped (IDBL) and light irrigation (IL) have relatively late onset of greenness, because they use canal water from reservoirs that drain large catchments and take weeks to fill. Minor irrigation and groundwater-irrigated areas have early onset of greenness because they drain smaller catchments where aquifers and reservoirs fill more quickly. Vegetation phonologies of nine distinct classes consisting of irrigated, rainfed, and other land-use classes were derived using MODIS 250-m near continuous time-series data that were tested and verified using groundtruth data, Google Earth very high resolution (sub-metre to 4 m) imagery, and state-level census data. Fuzzy classification accuracies for most classes were around 80% with class mixing mainly between various irrigated classes. The areas estimated from MODIS were highly correlated with census data (R-squared value of 0.86).
10 Hoanh, Chu Thai; Facon, T.; Thuon, T.; Bastakoti, R. C.; Molle, Francois; Phengphaengsy, F. 2009. Irrigation in the Lower Mekong Basin countries: the beginning of a new era? In Molle, Francois; Foran, T.; Kakonen, M. (Eds.). Contested waterscapes in the Mekong region: hydropower, livelihoods and governance. London, UK: Earthscan. pp.143-171.
River basins ; Irrigation management ; Farming systems ; Rice ; Irrigated land ; Irrigation schemes ; Rehabilitation ; Modernization ; Participatory management ; Farmers ; Cost recovery ; Water policy / Laos / Cambodia / Thailand / Vietnam / Lower Mekong Basin / Stung Chinit Irrigation Scheme
(Location: IWMI HQ Call no: 333.91 G800 MOL Record No: H042241)
(0.96 MB)
11 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).
12 Cai, Xueliang; Cui, Y. 2009. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. In Chinese. Transactions of the Chinese Society of Agricultural Engineering, 25(8):124-130.
Remote sensing ; Irrigated land ; Crop management ; Rice ; Wheat / China / Zhanghe Irrigation System
(Location: IWMI HQ Call no: e-copy only Record No: H042411)
(1.13 MB)
Crop planting structure extraction in irrigated areas includes a range of dynamic parameters which require proper spatial and temporal resolution remotely sensed data. The paper seeks to extract crop planting structure by employing multi-temporal images from multi-sensors. Landsat enhanced thematic mapper plus (ETM+) images and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) monthly data were res-merged to produce a mega data tube, which was then classified using ISO cluster algorithm. Spectral signature of each class was extracted and identified using spectral matching technique taking crop coefficient curve as reference. In the way Zhanghe Irrigation system in southern China was classified into four classes: rice-rapeseed rotation, rice-wheat rotation, single summer crops, and double economic crops. Accuracy assessment suggests good agreement with statistical data and 91% classification accuracy when using IKONOS high resolution images as Ground Truth data. The application demonstrates the method a cost-efficient and robust approach to extract crop planting structure at irrigation system scale.
13 Cai, Xueliang; Cui, Yuanlai. 2009. A simplified ET mapping algorithm and its application in irrigation district. In Chinese. Journal of Irrigation and Drainage, 28(2):51-54.
Remote sensing ; Mapping ; Evapotranspiration ; Irrigated land / China / Zhanghe irrigation district
(Location: IWMI HQ Call no: PER Record No: H041477)
(0.50 MB)
14 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.
15 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)
16 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)
17 You, S.; Liao, S.; Suchuang, D.; Yuan, Y. 2009. Uncertainty of estimating irrigated areas in China. 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.121-137. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042420)
18 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)
19 Brown, J. F.; Maxwell, S.; Pervez, S. 2009. Mapping irrigated lands across the United States using MODIS satellite imagery. 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.177-198. (Taylor & Francis Series in Remote Sensing Applications)
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042422)
20 De Pauw, E. 2009. Irrigated area mapping in the CWANA region and its use in spatial applications for land use planning, poverty mapping, and water resources management. 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.251-279. (Taylor & Francis Series in Remote Sensing Applications)
Remote sensing ; Mapping ; Irrigated land ; Water poverty ; Indicators ; Farm income ; Case studies ; Supplemental irrigation / Central Asia / West Asia / North Africa / Syria
(Location: IWMI HQ Call no: 631.7.1 G000 THE Record No: H042425)
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