Your search found 12 records
1 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.
2 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).
3 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)
4 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)
5 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)
6 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)
7 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).
8 Thenkabail, P. S.; Hanjra, M. A.; Dheeravath, V.; Gumma, Murali Krishna. 2010. A holistic view of global croplands and their water use for ensuring global food security in the 21st century through advanced remote sensing and non-remote sensing approaches. Remote Sensing, 2(1):211-261. [doi: https://doi.org/10.3390/rs2010211]
Farmland ; Irrigated land ; Remote sensing ; Mapping ; Water use ; Virtual water ; Water productivity ; Food security
(Location: IWMI HQ Call no: e-copy only Record No: H043444)
(4.41 MB)
This paper presents an exhaustive review of global croplands and their water use, for the end of last millennium, mapped using remote sensing and non-remote sensing approaches by world’s leading researchers on the subject. A comparison at country scale of global cropland area estimated by these studies had a high R2-value of 0.89–0.94. The global cropland area estimates amongst different studies are quite close and range between 1.47–1.53 billion hectares. However, significant uncertainties exist in determining irrigated areas which, globally, consume nearly 80% of all human water use. The estimates show that the total water use by global croplands varies between 6,685 to 7,500 km3 yr-1 and of this around 4,586 km3 yr-1 is by rainfed croplands (green water use) and the rest by irrigated croplands (blue water use). Irrigated areas use about 2,099 km3 yr-1 (1,180 km3 yr-1 of blue water and the rest from rain that falls over irrigated croplands). However, 1.6 to 2.5 times the blue water required by irrigated croplands is actually withdrawn from reservoirs or pumping of ground water, suggesting an irrigation efficiency of only between 40–62 percent. The weaknesses, trends, and future directions to precisely estimate the global croplands are examined. Finally, the paper links global croplands and their water use to a paradigm for ensuring future food security.
9 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.
10 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.
11 Gumma, M. K.; Thenkabail, P. S.; Hideto, F.; Nelson, A.; Dheeravath, V.; Busia, D.; Rala, A. 2011. Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data. Remote Sensing, 3(4):816-835. [doi: https://doi.org/10.3390/rs3040816]
Remote sensing ; Methodology ; Mapping ; Irrigated land ; Irrigated farming ; Land use ; Land cover ; Satellite imagery ; Statistics / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H044267)
(1.69MB)
Maps of irrigated areas are essential for Ghana’s agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20–57% higher than irrigated areas reported by Ghana’s Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.
12 Thenkabail, P. S.; Hanjra, M. A.; Dheeravath, V.; Gumma, M. 2011. Global croplands and their water use from remote sensing and nonremote sensing perspectives. In Weng, Q. (Ed.). Advances in environmental remote sensing: sensors, algorithms, and applications. Boca Raton, FL, USA: CRC Press. pp.383-416. [doi: https://doi.org/10.1201/b10599-20]
Farmland ; Mapping ; Water use ; Remote sensing ; Irrigated farming ; Rainfed farming ; Water use ; Food production ; Satellite surveys ; Data ; Policy
(Location: IWMI HQ Call no: e-copy only Record No: H046020)
(1.86 MB)
Powered by DB/Text WebPublisher, from
