Figure 1. Processing chain for the Global irrigated area map (GIAM).

Table 1. Characteristics of the Satellite sensor and secondary datasets used in mapping Global irrigated areas. These datasets

 were Compiled into a 159-band layer stack^1,2.

Band number3 Or primary source (#) Wavelength range (µm) Duration4 (years) Number of bands and radiometry (#; one per month)1 Data final format Z-scale (percent: for reflectance) Range (percent) Satellite sensor data         AVHRR 10-km         Band 1 (B1) 0.58 - 0.68 1997-1999 36 reflectance @ ground, 8-bit 0-100 Band 2 (B2) 0.73-1.1 1997-1999 36 reflectance @ ground, 8-bit 0-100 Band 4 (B4) 10.3-11.3 1997-1999 36 Brightness temperature 160-340 (top-of-atmosphere)     NDVI (B2-B1)/(B2+B1) 1982-2000 36 unitless, 8-bit scaled NDVI -1 to +1       Secondary data     GTOPO30 1-km     one-band DCW, DTM, and others 1 time 1 meters, 16-bit -1 to + 1 Rainfall 1-km     one-band Mean of monthly 40-years 1961-01 1 mm, 16-bit 0-65536 Forest cover 1-km     one-band None 1992-93 1 class names, 8-bit 0-256       Table 2. Other data used in conjunction with the megafile   1. Band 1, 2, NDVI same as above 1981-2001 2391   2. SPOT 1-km2     NDVI (B3-B2)/(B3+B2) 1999 12 unitless, 8-bit scaled NDVI -1 to +1 3. JERS SAR 100-m     one-band L-band;24.5 cm Jan.-Mar 1996 1 unitless, 8-bit 0-256     Oct-Nov 1996 1 unitless, 8-bit 0-256

Note:
1 = animations of the irrigated area classes were run for the entire AVHRR time-series data to help understand the change history of the class. There was data for 239 months in 19 years (July 1981- September 2001). September-December 1994 data was not acquired due to failure of the satellite.

2 = all data were calibrated and normalized by data provider (see section 4.1.1).

Figure 2. Mega-file used in GIAM. The mega-file of 159 layers of data and consist of 144 AVHRR 10-km monthly layers from 3 years,
12 SPOT monthly layers from1999 year, single layer of DEM, mean annual rainfall from 40-years, and forest cover.

Figure 3. Primary and secondary data sets used in the Mega-file.

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4.1.2 SPOT data characteristics

The SPOT Végétation (SPOT VGT) 1-km data has 4 wavebands: blue (0.43-0.47 µm); green (0.61-0.68 µm); near-infrared (NIR) (0.78-0.89 µm); and short wave infrared (SWIR) (1.58-1.75 µm). There is 10-day synthesis of SPOT VGT data that can be downloaded free of cost for the entire world (http://free.vgt.vito.be/). A single year monthly SPOT VGT NDVI data for 1999 was used in this study.

Similar corrections have been made to the SPOT VGT data by the SPOT Image production team, including scattering and moisture absorption using radiative transfer models. Cloud and snow were detected using a multivariate thresholding technique and neural networks using data from all 4 VGT wavebands (Lissens et al., 2000). Cloud shadows were detected using geometrical model as described by Lissens et al. (2000). The 10-day synthesis is performed using daily data using maximum value compositing (MVC).

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4.1.3 Mask data

Secondary data sets in the mega-file are used to segment the world into characteristic regions based on rainfall, elevation, temperature and known forest cover. For example, in areas where temperatures are less than 280K, it is unlikely that there is any vegetation and little chance of there being any irrigation.

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4.1.4 GTOPO 30 1-km DEM

The GTOPO30 is derived from eight sources consisting of digital terrain elevation data or DTED (50% of global coverage), digital chart of the World or  DCW (29.9%), USGS 1-degree digital elevation models (6.7%), army map service maps (ASM maps) at 1:1,000,000 scale (1.1%), international map of the world (IMW maps) at 1:1,100,000 scale (4.7%), Peru map at 1:1,000,000 scale (0.1%), New Zealand DEM (0.2%), and Antarctic digital database (8.3%) (Tucker et al., 2004; USGS, 1993; Verdin and Greenlee, 1996; and Verdin and Jenson, 1996). The vertical accuracy of the component DEM data varies significantly from source to source. Accuracies were 30-m for DTED, 160-m for DCW, 30-m for USGS DEM, 250-m for ASM maps, 50-m for IMW maps,  500-m for Peru map, 15-m for New Zealand map, and highly variable for Antarctic digital database. In this paper, GTOPO30 data were used to segment the image based on elevation gradients.

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4.1.5 CRU precipitation and temperature data

The 40-year (1961-2000) monthly, 0.5 degree, interpolated rainfall and temperature data were obtained from Dr. Tim Mitchell of the Climate Research Unit (CRU), University of East Anglia, UK (Mitchell, et al., 2003) (http://www.cru.uea.ac.uk/~timm/index.html).The data have been converted to ESRI GRID format at IWMI and mean monthly precipitation and temperature for 40-years were computed for each pixel and added to the mega-file.

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4.1.6Forest cover data

Forest cover was derived from the 1992 AVHRR 1-km data by university of Maryland that used a continuous fields approach (rather than discrete number of classes) using a linear mixture model approach (see DeFries et al. 2000a and DeFries et al. 2000b). This dataset was used to mask areas of very high forest cover, which implies the land is not available for cultivation or irrigation.

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4.2 Secondary data sets

4.2.1 JERS-1 SAR derived forest cover

Rainforests may contain fragmented irrigation along with shifting cultivation and clearance for livestock production. Mapping irrigated areas in rainforests is more complex than in other parts of the world as a result of forest fragmentation and significant cloud cover. Even the monthly AVHRR and SPOT VGT MVCs contain some cloud cover over rainforests and irrigated fragments are difficult to discern at 1 to 10-km. We obtained 100-m resolution JERS-1 SAR tiles ( http://southport.jpl.nasa.gov/GRFM/) for South America and Africa to assist in mapping major rainforest areas at higher resolution. Unfortunately, well processed JERS SAR images are not readily available for Asia and hence could not be used.

Figure 4. JERS-1 SAR 100-m image tile mosaicks for the Central Africa.

The rainforests of the Africa and the Central America were studied using JERS-1 SAR

100-m data for two periods in 1995-1996.

The Japanese Earth Resources Satellite-1 (JERS-1) Synthetic Aperture Radar is a L-band (24.5 cm wavelength) imaging radar with initial full-resolution of 18-m, that is processed to 100-m, mosaicked and made available for the entire contiguous rainforests of Amazonia and Central Africa. The JERS SAR antenna has a median look angle of 35 degrees.

The Amazon basin was imaged by JERS-1 during a low flood period from September-December 1995 and in a high flood time from May-August 1996. The Central and West Africa rainforests images were obtained for January-March 1996 and October-November 1996. Over 20 million square kilometers of the rainforests are covered by these images (Saatchi et al., 2000, Saatchi et al., 2001). The JERS SAR image tiles were mosaicked into single files for Central Africa (Figure 4) and Amazon using ERMapper 6.5, at IWMI.

The 8-bit JERS SAR data of rainforests were analyzed separately and fused with the overall classification results from other areas.

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4.2.2 ESRI Landsat 150-m GeoCover

ESRI re-sampled the 8,500 ortho-rectified Landsat ETM+ ?GeoCover? tiles that had been produced by the EarthSat Corporation (http://www.earthsat.com), funded by  NASA (Tucker et al., 2004). The original images are free from the USGS EROS data center and the University of Maryland (http://glcf.umiacs.umd.edu/index.shtml). The re-sampled images have a pixel resolution of 150 m compared with the original pan-sharpened size of 15m. GeoCover is the most positionally accurate image set covering the entire globe and shows maximum greenness.  They offer a detailed ?zoom-in? view of any part of the world (Figure 5) and are used to provide contextual information and pseudo ?ground-truth? by geo-linking to the class maps to identify and label classes.

Figure 5. Landsat ETM+ 150-m images of the World as ?ground-truth?.
The Landsat ETM+ (GeoCover 2000) orthorectified images for the nominal year 2000 at 150-m resolution were used as a ?ground-truth?.

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4.2.3 Google Earth Dataset

Google Earth (http://earth.google.com/) contains an increasingly comprehensive image coverage of the globe at very high resolution 0.61-4m, allowing the user to zoom into specific areas in great detail, from a base of 30m resolution data, based on GeoCover 2000. This assists:

• Identification and labeling the GIAM classes;
• Area calculations (section 7);  and
• Accuracy assessment of the classes (see section 8).

For every identified class, 20-50 sample locations were cross-checked using Google Earth.. The indicative GIAM class name can be updated according to the dominant class identified at high resolution within the sample area. The process also helps to identify mixed classes. Google Earth data is used as a substitute for groundtruth, although images may in fact be snapshots of cropping systems taken at different times. The very high resolution data has some advantage over real groundtruth in that it provides information on a much larger areas, and therefore more representative area than is normally sampled directly on the ground.

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4.2.4 Ground-truth (GT) data

Precise knowledge of the real situation on the ground is essential to the interpretation of all remote sensing products for: training; class identification and naming; and for accuracy assessment. Clearly, it is hard to obtain sufficient detailed groundtruth data to cover the whole world, but there is a growing amount of publicly available data thanks to the internet. IWMI actively collects groundtruth data for more localized projects, and  undertook two major groundtruth campaigns in India in 2003. Four more were completed in 2005, one in Southern Africa, one in Central Asia and two in India. The project team is working to continually expand the geographical scope, range and detail of groundtruth data available and all ground truth data is archived in the IWMI?s data storehouse pathway or IWMIDSP (http://www.iwmidsp.org). There are two global archives of GT data, one collected by IWMI and its staff and one using public domain data from the degree confluence project (http://www.confluence.org/).

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4.2.5 Groundtruth at IWMI: data collected in field campaigns

Detailed ground-truth (GT) data were collected by IWMI specifically for irrigated area mapping (see for example, http://www.iwmidsp.org and also in Thenkabail et al. 2005a, Thenkabail et al. 2005b, Biggs et al. 2005). The India GT mission covered about 12,000 kilometers and collected data from 393 specific points in September-October 2005, which is the peak of the monsoon crop season (Kharif, July-October). The Ganges basin field campaign was conducted from October 1 to 22, 2003 to coincide with the peak crop growth stages in Kharif. The Krishna River Basin data were collected from October 13 to 26, 2003. Data was gathered for 144 sample sites in Krishna and 196 sites in the Ganges region, covering all available land types. The precise locations of the samples were recorded by GPS in the Universal Transverse Mercator (UTM) and the latitude/longitude coordinate system with a common datum of WGS84 (see Figure 6).

At each location the following data were recorded (Thenkabail et al. 2005a):

• LULC classes: levels I, II and III of the Anderson approach.
• Land cover types (percentage): trees, shrubs, grasses, built-up area, water, fallow lands, weeds, different crops, sand, snow, rock, and fallow farms.
• Crop types, cropping pattern and cropping calendar for Kharif, Rabi (winter or dry season cropping period from November to March) and interim seasons.
• Water source: rain-fed, full or supplemental irrigation; surface or groundwater.
• Digital photos hot linked to each ground truth location.
  
  

Figure 6. Ground-truth (GT) data collected by IWMI.
Groundtruth data assembled from multiple locations and times by IWMI projects and staff.

Data accumulated by IWMI and its staff

A significant amount of groundtruth data has been collected on research projects conducted by the primary author over the last decade and was added to the archive of other data already held by IMWI. The data is sourced from India, Sri Lanka, Syria, West and Central Africa, South Africa, and Central Asia (see Figure 6).

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4.2.6 Public domain groundtruth ? the Degree Confluence Project

The Degree Confluence Project (DCP) (http://www.confluence.org/) is an organized sampling of the entire World at every 1 degree latitude and longitude intersection. It is a voluntary effort and close to 4000 confluence locations have already been contributed. The confluence points include precise latitude, longitude and a digital photo of land cover. These were converted to proprietary GIS formats and added to the DSP in a separate archive to preserve their identity.

We used DCP data to interpret land use at each location, based on the digital photo and added the complete set to a GIS (see Figure 7). These photos were used as part of the accuracy assessment and for illustration.

Figure 7. Ground-truth data of the World from the Degree Confluence Project (DCP).

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4.3 Other data sets for comparison purposes

A number of existing global land use/land cover (LULC) products were used in preliminary class identification and labeling process. These included USGS LULC (Loveland, et al., 2000), USGS seasonal LULC (Loveland, et al., 2000), GLC2000 (Bartholome´ and Belward, 2005), IGBP (IGBP, 1990), and Olson eco-regions of the world (Olson 1994a, 1994b). These data supplemented/complemented the ground-truth data during the preliminary class identification and labeling processes. The characteristics of these LULC classes are briefly mentioned here and for further detail the reader is referred to  peer-reviewed publications.

The Global Land Cover 2000 (GLC2000) data set was derived using data from SPOT 1-km resolution Végétation Instrument (Bartholome´ and Belward, 2005, Agrawal et al., 2003). The 10 day synthesis data from November 1, 1999 through December 31, 2000 were used for the classification (http://www.gvm.sai.jrc.it/glc2000/Products/). The Global Land Cover characteristics database was developed on a continent-by-continent basis using 1-km, 10-day AVHRR data spanning April 1992 through March 1993 (Loveland et al., 2000). The same primary data was used in the Global USGS LULC, seasonal USGS LULC, and IGBP LULC (http://edcdaac.usgs.gov/glcc/globe_int.html).

Olson data provide global 94 unique ecosystem classes for the Globe (Olson 1994a, 1994b) (http://edcdaac.usgs.gov/glcc/globe_int.html). This approach was developed in the mid-1980s and did not use any remote sensing information. For easy convenience, all these land cover products are made available in standard image processing formats (e.g., ERDAS Imagine) in IWMIDSP (http://www.iwmidsp.org).

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5 Methods

An overall summary of the methods and analytical techniques is shown in Figure 8. The basic process involves segmenting the world into characteristic regions that are easier to analyze and then performing an unsupervised classification on each segment, containing all the 159-band information from the AVHRR time-series and the single year of SPOT VGT data. Identification of the resulting classes is performed using a suite of new techniques to interpret vegetation dynamics in multitemporal series, which are explained in more detail below. A number of classes could not be clearly identified, and so were subdivided and classified using simple decision trees and ?ground-truth? data sourced from GeoCover 150-m and other secondary information (Tucker, et al. 2005). This resulted in generic class map of 951 ?unique? classes. As far as possible, class naming was harmonized with earlier Global Land Cover classifications. Irrigation classes were then derived by aggregation of similar irrigated land use in the generic map, resulting in a 28 irrigation class map (GIAM10km-28 Classes). This map is used to estimate irrigated crop areas in each of three reference seasons (see section 8). A further aggregation of this map into 8 broad irrigated area classes of the World (GIAM10km-8 Classes) gives a more visually friendly presentation, with class names that are more familiar to irrigation professionals.

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5.1 Image segmentation

5.1.1 Mega-file of segments

The original 159 band mega-file was converted into a megafile of segments, each with its own set of 159 bands (see Figure 1). The seven global masks created are listed below and then brief examples of each one are presented in Figure 9 and 10. The Global masks are:

• Precipitation less than 360 mm per year  (PLT360);
• Precipitation greater than 2400 mm per year (PGT2400);
• Temperature less than 280 degree Kelvin per year (TLT280);
• Forest cover greater than 75 percent canopy cover (FGT75);
• Special forest SAR (FSAR);
• Elevation greater than 1500 meter (EGT1500); and
• All other areas of the World (AOAW).

The segment with less than 360 mm per year identifies areas where any green vegetation has a very high likelihood of being irrigated, since average evaporation rates of 30mm per month, however distributed in reality, will be considerably less than evaporative demand. This segment will mainly identify arid, semi arid areas and deserts, as shown in

Figure 9. By contrast, the segment with rainfall over 2400mm per year mainly identifies the rainforest areas of the world, although there are considerable areas of irrigation within the SE Asian lands. Where temperature is less than 280K on average, it is too cold for agriculture, and irrigation is not likely to be found there. However, some northern hemisphere areas have low average temperature but short summer seasons in which supplemental irrigation is actually practiced.

Figure 8. Summary of analysis to determine irrigation and land use classes (Part 1).

 

Figure 8. Summary of analysis to determine irrigation and land use classes (Part 2).

Figure 9.  Precipitation less than 360 mm segment (PLT360-segment).
These arid or semi-arid areas provide distinct contrast between areas with and without vegetation.


Figure 10. Forest density greater than 75 percent (FGT75-segment). These areas have low probability of agriculture,
except in rare fragments of slash and burn.

If forest density is greater than 75%, it is also rare that there will be any irrigation, due to high rainfall and limited infrastructure. There is likely to be slash and burn agriculture in small fragments.  This mask in complemented by a special rainforest mask derived from the JERS-1 SAR imagery, in order to better identify other land use fragments at higher resolution within the rainforest areas, including where there might be irrigation.

There is a lower likelihood of irrigation above 1500m elevation, although there are certainly hill irrigation systems in the Andes, Himalayas and in the Philippines at higher elevations. Forest is a likely land cover, but should be separable from irrigation and agriculture due to its continuous vegetation signature. Finally, the segment ?all other areas of the world? focuses on where there are few bio-physical constraints to irrigation and shows where we are most likely to find it in various forms.

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5.2 Classification

Each segment is processed using unsupervised ISOCLASS K-means classification (Tou and Gonzalez, 1974, ERDAS, 2005). This calculates class means evenly distributed in the data space and then iteratively clusters the remaining pixels using minimum distance techniques. Each iteration recalculates the means and reclassifies pixels with respect to the new means. Iterative class splitting, merging and deleting is done based on input threshold parameters (see ERDAS, 2005). All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case, some pixels may be unclassified if they do not meet the selected criteria. This process continues until the number of pixels in each class changes by less than the selected threshold or the maximum number of iterations is reached.

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5.2.1 Preliminary Class identification and naming

On completion of an unsupervised classification, it is necessary to identify what the classes are and label them accordingly. In more localized applications, it is common to undertake ground-truth after a preliminary unsupervised classification, which identifies characteristic land units for investigation and this was done for the IWMI field campaigns in India. However, at global scale this is not possible, and a combination of techniques is employed to first group classes based on the similarity of their time series behaviour, then identify in more detail what they are through understanding the spatial-temporal variations in reflectance and cross referencing to higher resolution images (GeoCover 150; Tucker et al., 2005), existing GIS, maps and groundtruth data.

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5.2.2 Spectral Matching Techniques

Time series of NDVI or other metrics are analogous to spectra, where time is substituted for wavelength. Considerable research effort has been made into hyperspectral imagery analysis and this yields a number of promising avenues, developed here, for the analysis of time series. Spectral Matching Techniques (SMTs) have mostly been applied to hyperspectral data analysis of minerals (Homayouni S. and Roux M., 2003; Shippert, P. 2001, Bing et al. 1998; Farrand and Harsanyi, 1997; Granahan and Sweet, 2001; and Thenkabail et al. 2004c and 2004d).

The principle in spectral matching is to match the shape, or the magnitude or (preferably) both to an ideal or target spectrum (commonly know as a pure class or ?end-member?). The time series signatures of irrigated crops across the globe can match (tropics) or be out of phase (tropics and southern hemisphere). In this work we used a combination of qualitative and quantitative approaches to identify target spectra and match others to them. The selection of the target spectra is governed by groundtruth and GeoCover2000 to identify consistent LULC and irrigated areas classes. Example locations, where we know the signatures precisely, for ideal target spectra used in this work are shown in Table 3.

Table 3. The locations of the ideal target spectra for 7 irrigated area classes.

Type of Irrigation	Latitude	Longitude
1. Delta irrigation:
1. Krishna Delta (India)	15 59 17.81 N	80 57 07.55 E
2. Bangladesh	22 10 22.31 N	90 44 14.44 E
4. Yellow River Delta (China)	37 00 15.93 N	118 12 16.56 E
5. Mekong River Delta (Vietnam)	10 30 29.09 N	105 12 02.16 E
5. Nile Delta (Egypt)	31 08 05.65 N	31 03 30.10 E
2. Large Scale irrigation;
1. Tungabhadra Reservoir (Krishna River Basin)	15 41 31.43 N	76 41 50.54 E
2. Nagarjuna Sagar (Krishna River Basin)	16 29 29.87 N	80 52 00.95 E
4. Ganges River Basin (Haryana)	30 43 39.20 N	76 27 41.24 E
5. Rechna Doab (Indus River Basin)	32 02 10.58 N	74 33 28.86 E
5. Indus RB (Lower part)	25 39 40.32 N	68 34 57.51 E
6. California Valley	39 27 35.96 N	121 48 31.89 W
7. South East Australia	29 39 27.00 S	116 01 57.34 E
4. Canal irrigation;
1. Ganges River Basin (Upper Uttar Pradesh)	27 06 22.85 N	78 40 08.24 E
5. Centre Pivot
1. Colorado valley	37 42 54.93 N	106 03 45.28 W
2. Montana	47 09 01.26 N	119 44 31.51 W
5. Large-Lake irrigation;
1. Aral sea	42 39 48.81 N	59 00 47.76 E
6. Flood irrigation (River flood plains);
1. Ganges River Basin (Patna)	25 32 32.88 N	85 08 25.13 E
2. Brahmaputra flood plains (Assam)	26 07 44.84 N	90 55 47.45 E
7. Supplemental irrigation;
1. Midwest (US)	43 09 06.11 N	97 49 40.99 W
2. Syria	35 53 06.71 N	37 03 28.33 E

We also attempted to use Modified Spectral Angle Similarity (MSAS) (Shippert, P. 2001, Homayouni S. and Roux M., 2003, Farrand and Harsanyi, 1997, Schwarz and Staenz, 2001, Thenkabail et al. 2005b) which measures hyperspectral angle between spectra of any 2 classes or between target and sample class spectra. However, the practical implementation of this was troublesome (see also Thenkabail et al., 2006), often providing uncertain results, and so is not discussed further.

Qualitative spectral matching

Qualitative spectral matching is often performed before quantitative approaches (e.g., Figure 11. It provides a preliminary indication of which classes group together and which stand apart. Indeed the classes that match up through: (a) shape only, and/or (b) magnitude only, and/or (c) both shape and magnitude, are identified visually. When two classes, such as continuous irrigation and forests, match and provide high quantitative correlations, it is essential to plot both classes with reference to their spatial location using ground truth or ancilliary data.

Figure 11. Time-series AVHRR 10-km profile of spectral classes is illustrated for AOAW-segment. The AOAW-segment initially had 350 classes. The plot of some of these classes highlights the spectral characteristics of each class. A quantitative approach to determine which of these classes match is performed through SCS R²-squared (e.g., Table 4).

Quantitative spectral matching

In this study, Spectral Correlation Similarity (SCS) has been applied to match the shape of any class to the selected target class. Spectral Similarity Value (SSV) has been used to determine the match of both shape and magnitude.  SCS is defined as follows and is based on Pearson?s correlation coefficient  applied to an NDVI time series (SAS, 2004, van der Meer and Bakker, 1997):

(5)

where: 

t_i          = NDVI time-series (i=1 to n) of the target class;

μ_t         = Mean of the NDVI time series of the target class;

h_i          = NDVI time-series (i=1 to n) of any other class;

μ_h         = mean of the NDVI time-series (i=1 to n) of any other class;

       = standard deviation of target class NDVI time-series (i=1 to n); and

       = standard deviation of NDVI time-series of other class.

The range of Pearson?s Correlation Coefficient () normally lies between -1 and +1, but negative values have no meaning in this application. The higher the value, the greater is the similarity.

SSV is defined as follows (Homayouni S. and Roux M., 2003, Granahan and Sweet, 2001):

(6)

where:

E_d         = Euclidian (shortest) distance between two points

        = Pearson?s Correlation Coefficient as defined above.

The normal range of SSV is from 0 to 1.415 and the smaller the value, the greater is the similarity.

Table 4. The SCS R²-value matrix of spectral classes. The spectral correlation similarity (SCS) R² value matrix is illustrated for 2-spectral classes. Those highlighted in cyan are highly correlated (R²-value greater than 0.92) with 11 other spectral classes. Similarly spectral classes highlighted in yellow and orange are moderately correlated with thresholds of 0.9-0.92 and 0.84-0.90 respectively. SCS is the first step towards grouping classes, providing a strong indication of which classes have similar characteristics.

The process of spectral matching is illustrated beginning with a plot of multiple time series and two selected target series in Figure 11, which are characteristic of two irrigated crops per year in the Indian subcontinent. Figure 12 shows the results of grouping similar spectra for double crop irrigation, continuous forest cover, and bare or fallow soils. The process involves matching spectra of classes, grouping classes with similar spectra, and then identifying and labeling classes using Figure 8 protocol. The results of a cross correlation of 64 time series is shown as a subset in Table 4 and shows, for example, that Class 1 is highly correlated with five other classes (2,19, 20, 42 and 44). Similar matrices were calculated for each segment classification. Figure 13 shows where the spatial similarity of classes across the globe is determined with reference to known ground condition and high  values in the matrix.

Figure 12. Identifying similar irrigated classes using spectral matching. Spectral matching in combination with ground truthing and ideal spectra helped group similar irrigated (shown in dark green, for classes 25, 26, and 27). The same logic was used to group: forests (sown in light green; class numbers 1, 2, 3, 4 and 5), Savanna/Croplands mix (Orange; class 50, 59, 60, 67, 74), and Barren/Deserts (shown in blue; classes 10 to 15).

The extraction and geographical location of similar classes is shown in a more pictorial way in Figure 13. Figure 14 illustrates where there is good shape similarity but poor correlation in magnitude, which indicates that the classes should be separated. Such time series were compared with target spectra from ideal locations (Table 4) and those with low values of SSV were grouped together.

The use of SSV is illustrated taking three distinct types of irrigated areas-major irrigation, supplemental irrigation, and delta irrigation (Figure 16). The target time series is selected for a ?pure? location for the class depicting near ideal conditions. The unsupervised class spectra are sourced from a much larger number of pixels, and hence depict an average situation. The target classes in this example are: (a) major irrigation in Ganges basin (target in red, unsupervised grouped class series in magenta), (b) supplemental irrigation from mid-west USA (centre pivot sprinkler) and Syria (groundwater) (targets in light blue and actual unsupervised class series in dark blue), and (c) delta irrigation from Bangladesh (ideal series in light green, unsupervised class series in dark green).

Figure 13. The process of combining classes in spectral matching techniques (SMTs) is illustrated. First, the SCS R²-values are determined for a matrix of classes. The time-series spectra of classes with high SCS R²-values are then matched. Grouped classes are investigated further using all other types of information including groundtruth. This leads to distinct groups such as: boreal forests and tropical forests. Finally, the classes of similar types are color coded.

Figure 14. The process of spectral matching techniques (SMTs) is illustrated. The 17 classes considered in Figure 12 are further refined by quantitative and qualitative SMTs that lead to 3 distinct groups.

A relatively small SSV of 0.22 and a moderately high SCS  (or SCS R²) value of 0.84 between the major irrigation target series and unsupervised classes spectra show that both the shape and magnitudes match. For supplemental irrigation the match is even better: a very small SSV of 0.16 and a very high SCS R² value of 0.97 When dissimilar classes like major irrigation and supplemental irrigation are matched, there is an obvious mismatch, captured by high SSV and low SCS R² values.

Figure 15. The spectral similarity value (SSV) to match spectra. In this figure, unsupervised class spectra are compared with ideal spectra of distinct irrigated classes: (a) major irrigation in Ganges basin (ideal spectra in red, unsupervised grouped class spectra in magenta), (b) supplemental irrigation from mid-west USA (pivot sprinkler) and Syria (underground water) (ideal spectra in light blue and actual unsupervised class spectra in deep blue), and (c) delta irrigation from Bangladesh (ideal spectra in light green, unsupervised class spectra in deep green). The smaller the SSV, the greater the match in shape and magnitude.

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5.3 Google Earth as a resource for class naming

Once the classes are grouped by spectral similarity, each one is  investigated for their class characteristic by taking 20-50 sample points on Google Earth spread across the world (Figure 16). If there is a overwhelming evidence that the class falls into a particular category, an indicative name is assigned. The interpretation of a class is based on visual indicators such as shape (e.g., central pivot circles), size (e.g., reservoir size for large and small scale), pattern (e.g., contiguous farms), and texture (e.g., smooth texture of a farm compared to rough texture of a forest). The process is repeated for every class in a group. If the Google Earth sample points for a class indicate a mixed land use/land cover, then the class is further processed either through decision trees or is re-classified, or GIS spatial modeling is applied to derive homogeneous classes.

Figure 16. Google Earth ?zoom in? views to identify a class. One preliminary class is spread out across the world. The class was investigated using 50 Google sample points that were randomly chosen. The figure shows the spread of the class across the world and Google Earth hi-res image at 2 locations: center pivot ground water irrigation in the USA and surface irrigation in Sudan.  

Overall, ~10,726 points (e.g., yellow points also called ?place marks? in figure 16) were used in identifying and providing indicative class labels in generic 951 class GIAM10km map.

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5.4 Advanced techniques for class identification

A summary of the application of brightness, greenness and wetness characteristics applied to multi-temporal imagery is discussed recently by Thenkabail et al (2005) with respect to irrigation mapping in the Ganges Basin in India. A brief review is given here.

A 2-dimensional near-infrared vs. red band spectral reflectivity plot of unsupervised classes is referred to as a brightness-greenness-wetness (BGW) plot (Figure 17).  The BGW plots help determine whether a class is: (a) green, (b) bright, (c) wet, or (d) somewhere in-between these classes. Classes that occupy green area have high NIR reflectivity and low red reflectivity. Typically, these areas are forests, agricultural lands, and natural vegetation. Classes that occupy bright areas have high NIR and high red reflectivity. The land use/land cover (LULC) categories of these classes are likely to be open/barren areas, sparse vegetation, dry vegetation, clouds and built-up areas. Classes that occupy wet areas have low NIR and low red reflectivity. These classes are likely to be wetlands, moist lands, water bodies, cloud shadows and swamp forests. The classes that are in between have different combinations of these broad LULC classes.

Figure 17. Brightness-greenness-wetness (BGW) plot fundamental principles.

The BGW plots provide clear and useful information on class dynamics over time and are a very helpful tool in identifying and labeling a class.

5.4.1    Brightness, greenness and wetness for a single date

Single date BGW plots do not capture the dynamics of a time series, but they show why NDVI (as the most commonly used metric of vegetation) can be low but represent different land uses ? for instance, when it is either wet or dry, or closer or father away from the soil line for the date in question. Similar plots are then established for individual dates of the time-series data. The class location in 2-D space is dependant on time, and classes such as continuous cropping, forests and deserts do not move much. Bigger changes over time can be seen in classes such as rainfed crops, irrigated crops and rain dependant grasslands. More subtle changes occur when, for example, a crop develops from an early to a maximum vegetative growth stage, in a short time period. This is more clearly illustrated in multi-date plots.

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5.4.2 Space-time dynamics of brightness, greenness and wetness

BGW plots could be created for every image date in a time-series. However, plotting for a series every few months, to highlight different cropping seasons provides sufficient dynamic information to characterize different land uses. For example, the band 1 vs. band 2 spectral reflectivity of AVHRR data for unsupervised classes are plotted over the peak months of the cropping seasons: January (blue), May (green), and September (red) in a given year on a single plot. This allows tracking a particular class through the peak months of each of 3 seasons. We can see that class 10 has very considerable changes over the seasons.

In Figure 18, the spectral reflectivity of AVHRR bands 1 and 2 is tracked at monthly intervals for 8 classes (class numbers 7, 10-18, 27, and 46). Each class has its own territory in space and, depending on the time of the year, has its own characteristic reflectivity. The 2-dimensional (2-d) space time spiral curves (ST-SCs) provide the best information on class behavior. For example, classes 27 and 46 experience big changes in a year and have a large ?territory? in 2-d feature space (2-d FS).  In contrast, classes 7 to 11 occupy narrow territories. Class 12-18 show moderate changes during the year and a small territory in 2-d FS.

Figure 18. Space-time spiral curve (ST-SCs) to track class changes in 2-dimensional (2-d) space and time.

This approach is used to match and group classes that: (a) fall within similar 2-d FS of a ST-SC plot, (b) have characteristic territory that leads to more precise interpretation of the nature of the class (based on sound field knowledge of at least one or more classes in a group). A large change in territory implies agriculture and irrigation; very small changes imply forests, plantations, continuous cropping; moderate changes imply rangelands. Time series analysis of NDVI and brightness temperature

Apart from the application of spectral similarity techniques to group similar classes, we can also extract diagnostic information from the vegetation dynamics shown by the time series itself, both independently and in conjunction with crop calendars.

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5.4.3 NDVI time series and cropping intensity

The NDVI time series can categorize and identify irrigated area classes into categories such as double crop, continuous crop, and single crop. Once the classes have been identified using the approaches and methods described above, further categorization is done using the time series NDVI plots. The example presented here is for SPOT VGT data. Double crops exhibit swift rise in NDVI from 0.1 or 0.2 or less to 0.5 to 0.7 within 1-2 months and then quickly fall back to NDVI of 0.2 or less. After 2-4 months of low NDVI, there is again a swift rise in NDVI during the second crop. In contrast, continuous irrigated areas have NDVI of about 0.3 or higher throughout the year.

During the class identification process, time-series NDVI are plotted, compared, and contrasted resulting in distinct categories. This is illustrated for 4 distinct classes (a) dry shrub/grasses, b) rainfed crops, c) woody savannah and irrigated crops in the Indus Basin, and d) for coniferous boreal forest) in Figure 19.

Figure 19.       AVHRR NDVI spectral profile to identify and delineate classes.

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5.4.4 Brightness temperature

During the class identification process, the AVHRR time series earth skin temperatures were also plotted along with time series NDVI (Figure 20). In the tropics, the greater the biomass levels of a crop, the lower is the skin temperature and vice versa (Figure 20). The skin temperatures of irrigated crops are low due to crop transpiration and background moisture/wetness. In temperate climates, crops grown in summer exhibit high NDVI and high skin temperatures. In contrast, during winter snow and leaf-off conditions there is low NDVI and low skin temperature. Thus the skin temperature time series helps identify LULC classes in different climatic zones of the world.

Figure 20. AVHRR derived skin temperature versus AVHRR NDVI for semi-arid and tropical crops.

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5.5 Class refinement

Some classes from the unsupervised classification could not be properly identified, as they appeared to have characteristics of a number of other classes (which are anyway mixed land-use categories). To resolve them and identify ones that have irrigation, a number of decision tree models (e.g., Figure 21 and 22) were developed.

Further metrics had to be incorporated to allow this refinement: 1) the principal components determined for the SPOT and AVHRR time series, and 2) the locations of the classes were investigated and matched against Forest Cover Density and GeoCover images for both 1990 and 2000 at full resolution (15-30m) and using the ESRI 150-m product. Typically the unresolved classes were split up into 10 to 20 sub-classes before applying the decision tree and contextual ground truthing process.

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5.5.1 Rule based decision trees

Figure 21 shows a rule based decision tree to resolve one of the conflict classes, where annual rainfall is greater than 2,400mm or 200mm per month (PGT2400). There were 4 conflict/mixed classes (classes 13, 15, 18 and 19) which were masked, reclassified using SPOT monthly time series NDVI, and labeled. The 4 mixed classes were resolved using different approaches and discussed below.

Figure 21. Decision trees to resolve mixed classes.
Forest cover density (%) is used to resolve mixed class # 13 in the precipitation > 2400mm per year segment.

Class13, labeled initially as ?Forest and some Irrigated?, was reclassified into five classes and then identified and labeled based on existing LULC maps, GeoCover images and GT data. Based on the forest cover density (FCD) the class 13 was resolved into 3 distinct classes (Figure 21): cropland/woodlands mosaic, woodland/cropland mosaic, and evergreen forest. This implies that the original class name ?forest and some irrigated? was incorrect and further refinement of the classes showed no irrigation at all.

The decision trees for the following mixed classes are not illustrated, but explain other types of class resolution:

o       Class15- Mixed: irrigated and savannas: SPOT NDVI was used to separate irrigated area from savannas. However, results were not very satisfactory due to persistent cloud problems over forest in the SPOT data. The same approach adopted for Class 15 was used to the extract irrigated area class.

o       Class18- Irrigated (rice dominant) with some forest: the approach used for Class 13 was repeated to refine this class.

o       Class19- Croplands with forest, grassland and some irrigated: This class was further reclassified in to 10 clusters using all possible combinations of SPOT NDVI, SPOT PCA, AVHRR NDVI (monthly 1999 and min), AVHRR PCA, and tree cover density. Finally, we selected SPOT NDVI to enhance class separability. Most of the classes separated well but subclasses 7, 8 and 9 were a mosaic of forests and croplands and cloud cover. The cloud problem was resolved using the SPOT NDVI MVC (1999) to reclassify them into five subclasses, which were labeled based on the GeoCover 2000 images, secondary information and GT data.

Class 17, in the PLT360-segment contains irrigated areas, natural vegetation and grasslands. In order to resolve it, a decision tree algorithm was built (Figure 22). First, class 17 is masked out and reclassified using the time-series AVHRR data into 10 sub-classes. Of the sub-classes, only class 6 remained mixed. Class 6 is resolved using: (a) yearly NDVI_max, NDVI_min, and (b) winter average NDVIs. When max NDVI>0.9 but min NDVI <0.1 it is only irrigation. However, when max NDVI>0.9 but min NDVI>0.1, then it is a mix of irrigation and grassland. The outcome is further division of class 6 into distinct categories of: (i) grassland and natural vegetation mix, (b) irrigated areas (pure), and (c) irrigated areas mixed with grasslands.

Figure 22. Decision tree rules to resolve mixed classes.
The Maximum, minimum, and average NDVI were used, in a decision tree framework, to separate out distinct areas within class 17.

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5.5.2 Simple decision trees with Principal Components

Principal Component Analysis (PCA) is also useful in resolving undefined classes. The monthly AVHRR NDVI images over one year were stacked for class 20 in the PLT360-segment and a PCA was performed. This resulted in 12-principal components (PCs). The month of July had the greatest factor loading, accounting for most of the 99 percent of variability explained by PC1. An unsupervised classification was performed on the PC images resulting in 10 sub-classes. The decision tree helped resolve the mixed classes. When (a) PC 1 (Jan) > 40, the class is irrigated; (b) PC 7, 8 and 9 (Jul, Aug, Sep) > 18 indicates mixed irrigation with woodland; and (c) when PC 6, 7, 8 and 9  (June, July, Aug., Sept) < 18 it classifies as non-irrigated area.

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5.5.3 GIS spatial modeling

When classes continue to be mixed, in spite of the various methods and techniques discussed in previous sections, we adopted the Geographical Information Systems (GIS) spatial modeling approaches to resolve classes. This involved taking a mixed class and applying GIS spatial modeling techniques such as overlay, matrix, recode, sieve and proximity analysis. The GIS spatial data layers used include precipitation zone, elevation zones, Koppen ecological zone, temperature zone, and tree cover categories (see Figure 8). Any one or more of these classes help separate the mixed classes.

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5.6 Class labeling

The disaggregated classes from different segments of the World were combined to create a single global disaggregated map of the World with 951 classes. The map, the image, and associated product-line for the 951 disaggregated classes are presented digitally in the IWMI?s Global irrigated area map (IWMI-GIAM) web site (http://www.iwmigiam.org). The product is referred to as the ?Generic-IWMI-951-class-map?. Each of the 951 classes also have class characteristics (Table 5) that include: elevation, tree cover, precipitation, temperature, AVHRR 10-km temporal signatures from 1997-1998 (band 1 and band 2 reflectivity; band 4 and band 5 skin temperature, NDVI), and SPOT 1-km NDVI for every month during 1999.

We first labeled each of the IWMI-generic-951classes based on the classification techniques described earlier in this section. Then we compared these with the corresponding global or regional classifications (USGS LULC, USGS seasonal LULC, GLC2000, IGBP, and Olson eco-regions of the world). The main objectives were to: (a) identify easy classes such as forests and savannas and label them with a name that is consistent with globally established class names, and (c) determine indicative class names that will help further investigation to come up with a final class name. If, for a tentative class, the assigned names from three or more global or regional classifications match, the common class name is provisionally adopted. Table 6 illustrates the identification process for first twenty classes of the AOAW-segment. The class naming is further verified using the hi-resolution imagery, leading to a final class name. The process is repeated for each of the 951 classes and is very time consuming, but leads to robust results.

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5.6.1 GeoCover, ground-truth and DCP data

The ESRI 150-m Geocover mosaic of the world was extensively used in the class naming process. Unsupervised classes from each segment were geo-linked with the 150-m GeoCover product to study the class characteristics spatially. The value of Geocover is in: (a) providing clear differentiation between agricultural lands and other land uses, (b) obtaining spatial details of landscapes of interest; and (c) visualization of patterns and features. These features enabled naming a class with greater confidence and/or renaming a class with additional set of information. Some examples of the how GeoCover was used to identify and label classes is illustrated in Figure 23.

Figure 23. Geocover Landsat 150-m data of the World in class identification and labeling process.

Examples of groundtruth from the Krishna and Ganges field campaigns are shown in Figure 24 (a-d) and examples of older IWMI ground-truth (see Figure 6) are shown in Figure 25 (a-d) for a broader range of locations in the world.  Digital photos (see Figure 7) sourced from the Degree Confluence Project (DCP) are illustrated in Figure 26 (a-d).

Figure 24. Irrigated areas and other LULC in the Ganges basin India. 
Irrigation in the Ganges includes tube wells in alluvial areas, reservoirs, and river diversions

Figure 25. Irrigated areas and LULC classes from different parts of the World.


Figure 26. Irrigated areas and LULC classes from different parts of the World from the degree confluence project.

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5.6.2 Class naming convention

The GIAM work lead to over 1000 classes. Over a two year period, involving a core team of 8 members and 2 other support staff plus a number of other useful contributors, these classes were resolved and labeled using extensive interpretation techniques described in the previous sections (see Figure 8). However, synthesizing these classes becomes extremely complex. In order to avoid such a situation, we adopted a standard class naming convention that involved the watering method, type of irrigation, crop type, scale, intensity, location, and type of signature (see Figure 27).

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5.7 Class aggregation and simplification

The Generic-IWMI-951 disaggregated class map forms the basis of all other maps, images, and associated product-lines. The products derived from the generic maps were:

• Disaggregated 28-class Global irrigated area map (GIAM10km-28 class);
• Aggregated 8-class Global irrigated area map (GIAM10km-8 classes);
• Aggregated 3-class Global irrigated area map (GIAM10km-3 class);
• Disaggregated 323-class Global Irrigated Area Map (GIAM10km-323 class);
• Disaggregated 229-class Global Map of Rainfed Cropped Areas (GMRCA229);
• Aggregated 22-class map of Global Map of Rainfed Cropped Areas (GMRCA22);
• Disaggregated 76-class Global Map of Land use/Land cover Areas (GMLULCA75); and
• Aggregated 10-class Global Map of Land use/Land cover Areas (GMLULCA10);

The classes that had similar names and characteristics were grouped into a single class and named uniquely. The process of aggregation is illustrated in Table 7.

The GIAM10km-28 class irrigated area map is the main irrigated area product, but two simplified GIAM10kmM-8 class, and GIAM10km-3 class maps have been produced to ease visualization and understanding by irrigation practitioners. The GIAM10km-3 class  map consists of the following classes:

• Irrigated, surface water;
• Irrigated, ground water;
• Irrigated, conjunctive use.

Figure 27. Class naming convention. The standardized class naming convention is depicted in this figure. At different levels,
the class naming may or may not include a particular category such as the scale of irrigation or the intensity.

Table 7. Process of aggregation of classes from the generic map. The irrigated area classes were aggregated from 951 class map

based on the methods discussed in sections 5, 6, and 7. Similar approach was used to aggregated classes into 28 or 8 or 3 class map

Irrigated area classes from				Final Class names
Extracted from 951	recode to 102	recode to 53	recode to 34
273, 586, 622, 265	5, 7, 8, 12	3	1	Irrigated, large scale
522, 422	38, 51	54	2	Irrigated, in river valleys and deltas
261	4	5	3	Irrigated, in arid zones near lakes and valleys
624	26	6	4	Irrigated, small scale
497, 570	19, 20	17	5	Irrigated, small scale-tanks, supplemental
504, 498, 502, 499	10, 22, 23, 24	2	6	Irrigated, small scale, mixed with rangelands
578, 215, 309, 500	16, 17, 18, 25	18	7	Irrigated, large scale, double crop, mixed crops
523	32	15	8	Irrigated, continuous, forest fragments
391	9	12	9	Irrigated, continuous, plantations
477, 554	6, 14	9	10	Irrigated, single crop, more natural vegetation
550, 53	13, 35	10	11	Irrigated, single crop, less natural vegetation
478	27	16	12	Irrigated, deltas and wetlands
313	21	4	13	Irrigated, rice/wheat, natural vegetation
39, 40	1, 2	1	14	Irrigated, patches along large scale
471	15	7	15	Irrigated, grasslands/shrub lands
322	33	14	16	Irrigated, natural vegetation
269	31	11	17	Irrigated, savannas
451, 492, 493	28, 29, 30	8	18	Irrigated, woodlands
51, 62	3, 34	13	19	Irrigated, forest fragments
605, 501, 503	11, 49, 50	19	20	Irrigated, large scale, double crop, rice dominant
47, 548, 506, 534,367,547, 559	46, 47, 62, 63, 43, 48, 39	31, 22, 20	21	Supplemental, large/small scale
522, 422	38, 51	28	22	Supplemental small scale (pivot, drip), rainfed dominant
532, 260	44, 55	30	23	Supplemental, centre pivot dominant, grasslands
621, 366, 257	40, 42, 56	21	24	Supplemental, centre pivot dominant
349, 56	45, 57	24	25	Supplemental, small scale
368, 221	36, 37	25	26	Supplemental patches, forest fragments
468, 201, 469	54, 52, 53	29, 32	27	Supplemental, grasslands
581, 65, 623, 516	58, 59, 41, 61	26, 23, 27	28	Supplemental, forest fragments
589, 625	77, 67	43, 34	29	LULC: irrigated fragments, forests/savannas
369, 467, 370, 36, 496, 562, 566, 495	98, 99, 100, 86, 87, 88, 96	44, 46, 40	30	LULC: supplemental irrigation, woodlands
361, 362, 359, 48, 569, 360, 363	81, 82, 83, 73, 89, 92, 93	37, 39, 38	31	LULC: supplemental irrigation, rangelands
355, 354, 561, 545, 579, 563, 564, 565	85, 90, 91, 95, 84, 64, 65, 66	36, 48, 50, 33	32	LULC: supplemental irrigation, grasslands/shrub lands
491, 577, 528, 529, 476	71, 70, 68, 69, 72	45, 49, 35, 47	33	LULC: wetlands seasonally irrigated
482, 521, 63, 538, 619, 378, 348, 524, 344, 549	79, 80, 97, 101, 76, 102, 74, 78, 75, 94	41, 42, 51, 53, 52	34	LULC: forests, rainfed croplands, irrigated mosaic

 

Figure 33. GIAM10km V2.0 28 class map.

Figure 34.       GIAM10km V2.0 8 class map.

Figure 35. GIAM10km V2.0 3 class map. 

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9.2 Areas of irrigation derived  from GIAM10km map V2.0

The irrigated areas of the World were estimated by the three methods (section 7) and the results are presented here.

First, the areas determined using irrigated area fraction from Google Earth estimates (GEE) totals 412 million hectares or Mha, without any specific information on cropping intensity.

The seasonal and annualized irrigated areas are determined using irrigated area fraction from the high resolution imagery and sub-pixel decomposition technique. For each of the 28 classes (Figure 33), we used the average IAF coefficients calculate seasonal and annualized  areas (summed over all seasons). The estimated total global irrigated area for the 3 seasons are (Table 9a): (a) 263 Mha for season 1, (b) 176 Mha for season 2, (c) 41 Mha for season 3. The annualized global irrigated area at the end of the last millennium was 480 Mha.

The areas have also been summarized (Table 9b and 9c) for the 8 class map and the 3 class map.

The major finding of the IWMI analysis is that the net (412 Mha) and the annualized (480 Mha) cropped area under irrigation very significantly exceeds the estimates of equipped area (274 million ha) by FAO, due to the extent of multiple cropping and private and community developed irrigation. The area estimates in the map are derived for each characteristic agricultural system around the world (e.g. long season winter sown cereals in the northern hemisphere; triple rice cropping in SE Asia; wet monsoon season (Kharif) and dry winter (rabi) systems in the Indian sub-continent). The development of global irrigated area over last two centuries is summarized in the Figure 36, with and without estimates of cropping intensity. The presence of a large number of classes in GIAM10km-28 classes (Figure 33) ensures varying seasonality of classes by taking more precise cropping calendars between northern and southern hemispheres, the tropics, and the higher latitudes. The aggregated maps (Figure 34 and 35 and Table 9b and 9c) lose this distinction. The spatial characteristics of the GIAM class information can be visualized using the higher resolution Landsat ETM+ re-sampled 150-m images, digital photographs, and google earth images from the specific locations (Figure 37). The GIAM class information, presented in this manner is of considerable value for the user who would like to have a ?visual picture? (Figure 37)

Table 9a.  Irrigated areas of the World from the GIAM10km-28 classes V2.0 map using IAF from HRI and SPDT.

Table 9b.  Irrigated areas of the World derived from the GIAM10km-8 classes map V2.0.

Figure 37. The global irrigated area class snap-shot illustrations for GIAM classes. The snap-shots (e.g., photos, high-resolution images) of 4 distinct classes for a GIAM10km V2.0 class.

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9.3 Irrigated areas of continents, Countries, and river basins

Irrigated areas were also calculated, based on combined IAF-HRI and IAF-SPDT, for the continents (Table 10a), the Countries (Table 10b), and the IWMI and challenge program benchmark river basins (Table 10c).

Of the 480 Mha annualized irrigated areas in the world, 78 percent (375 Mha) is in Asia, 8 percent in Europe, 7 percent in North America, 4 percent in South America, 2 percent in Africa, and 2 percent in Australia.  The area distribution for the seasons follow similar trend (Table 10a). In Europe and North America overwhelming proportion of irrigation is during the one main cropping season. In Asia, 154 Mha is irrigated in season 2 compared with 195 Mha during season 1. The surface water irrigation in the world is 61 percent, the rest 39 percent is conjunctive use (surface and ground water) and ground water. The surface water is well separated. The ground water is often ingrained (and often dominates) in the conjunctive use class.

Of the total global irrigated are of 480,697,105 hectares, China (31.5 percent), India (27.5 percent) constitute a total of 59 percent (Table 10b). The next countries have comparatively low percentage irrigated areas: USA (5 percent), Russia (3.5 percent), Pakistan (3.3 percent). There are 9 Countries (Argentina, Australia, Thailand, Bangaldesh, Turkey, Kazakhstan, Myanmar, Uzbekistan, and Vietnam) with 1 to 2 percent. Brazil is ranked 15^th with 0.85 percent (Table 10a). All other countries of the world have less than 1 percent or less irrigated area. Forty countries have nearly 96 percent of all annualized irrigated areas of the world (Table 10b). Normally (see Droogers, 2002, Postel, 1999), India is considered the leading irrigated area country, closely followed by China. However, our estimates show, China has 151 Mha of annualized irrigated area with India having 132 Mha. In the first season China with 76 Mha and India with 73 Mha are close. However in the second season China has 68 Mha and India 54 Mha (Table 10b). In summer there is only about 7 Mha in China and even less in India (about 6 Mha). The irrigated area fraction (IAF) for the classes in China were higher leading to greater sub-pixel area. For example, class 4 (see Figure 33) which is  mainly in China has IAFs of 0.53 and 0.67 for China. The class 8 and 24, 2 of the classes with substantial full pixel area (FPA), have low IAFs. Class 8 for example, has IAFs of 0.37 for season 1 and season 2 bringing the sub-pixel area (SPA) down. Almost all previous irrigated area maps either calculated areas based on FPA or from national statistics (which also often ignores fallow areas).

The irrigated areas of the continents and countries have been calculated based on the cropping calendars and irrigated area fractions (IAFs) obtained from the global map. Our expectation is that the calculation of irrigated areas for the countries will be much more precise if cropping calendars are developed for individual countries and irrigated area fractions developed separately for every country. For this the GIAM team plans to work with national partners in 2007. However, we do not expect the trends in irrigated areas to change and only a certain (probably + 10 percent) adjustments to irrigated areas (maintaining the present trend) is possible, especially for smaller countries.

Table 10a. Irrigated areas of the Continents. The GIAM10km continental areas are compared with the FAO Aquastat and the National statistics.

Table 10b. Irrigated areas of the Countries. The GIAM10km country areas are compared with the FAO Aquastat and the National statistics.

 

The irrigated areas of the IWMI and CP benchmark river basins have been reported in Table 10c. Maintaining the irrigated area trends of the continents and countries, the river basins of Asia and in particular India and China, dominate in irrigated area percentages (see Table 10c). The annualized areas in Ganges is nearly 50 million hectares, Indus about 26 million hectares, and Yellow River about 20 million hectares. These are staggering figures, given a basin like Nile with nearly 6000 years of irrigation history has only about 5 million hectares.

Table 10c. Irrigated areas of the river basins. The GIAM10km river basin areas are compared with the FAO Aquastat and the National statistics.

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9.4 Accuracy assessment of the GIAM10km map V2.0 and its comparison with other maps

The accuracies were determined through two methods:

• Ground-truth data
• Google earth data

First, we discuss accuracies assessed using ground truth data and follow that with accuracies determined using Google Earth data. Accuracies are assessed to determine whether the class mapped is irrigated or not.

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9.4.1Accuracies and errors of GIAM10km map V2.0 using ground truth data

There were 2 independent ground truth data sets used in accuracy assessment. First a 895 point ground truth data collected by GIAM team. Second the 1861 point ground truth data from the degree confluence project (DCP).

Based on the GIAM teams 895 points, the accuracy of irrigated mapped as irrigated was 84 percent with 16 percent error of omission and 21 percent error of commission (Table 11a). In comparison, the FAO map showed an accuracy of 79 percent with 21 percent for errors of omission and commission (Table 11a). With DCP 1861 the accuracy reduces to 77 percent but errors of omission and commission stay low at 23 percent. In comparison the FAO\UF V3.0 map shows an accuracy of 70 percent and the errors of omissions and commissions of 30 percent (Table 11a).

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9.4.2 Accuracies and errors using Google Earth ground truth (GEGT) data for the World

The GEGT points are randomly distributed around the world, with higher density of distribution of points where irrigated area is dense. Accuracies using GEGT can be considered even better than the ground truth data as a result of: (a) better distribution of points around the world, and (b) precise spatial view of the landscape in determining irrigation at 10-km scale which can often be unrealistic from the ground.

The GEGT determined accuracy of GIAM irrigated area classes to be 92 percent with very low error of omission of 8 percent and low error of commission of 17 percent (Table 11b). The FAO/UF V3.0 map had an accuracy of 79 percent with higher errors of omission with 21 percent but lower errors of commission with 11 percent (Table 11b).

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9.4.3 Accuracies and errors for India in GIAM10km V2.0 for India

The accuracies and errors of the irrigated area classes were also determined for India for two main reasons: (a) the ground truth data for India is dense and well distributed as a result of several GT missions, at various times, by the GIAM team; and (2) India is one of the two largest irrigating nations in the world. Accuracies and errors are determined for IWMI GIAM10km V2.0 India portion and compared with: (a) FAO/UF V3.0 map, and (b) India?s Central Board of Irrigation and Power (CBIP) map.

The accuracy of GIAM V2.0 map in India was 86 percent with errors of omission of 14 percent and errors of commission of 20 percent. In comparison the FAO|UF V3.0 map and the India?s Central Board of Irrigation and Power (CBIP) map have substantially low accuracies and higher errors of omissions and commissions (Table 11a). In comparison, FAO/UF V3.0 map had an accuracy of 76 percent with 24 percent errors of omissions and 26 percent errors of commission. The CBIP map had much lower accuracy at 61 percent and much higher errors of omission (39 percent) and errors of commissions (23 percent). This is because, the CBIP irrigated area map for India almost completely ignores groundwater irrigation, conjunctive (surface water plus ground water) use within irrigated areas, and the supplemental irrigated area as its focus is almost completely on large scale surface water irrigated areas with some medium to small scale surface water irrigated areas.

The trends in accuracies and errors between GIAM V2.0, FAO\UF V3.0, and CBIP using the Google Earth ground truth (GEGT) remains the same, with higher accuracies and lower errors in GIAM V2.0 (see Table 11b).

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9.5 Accuracy assessment discussions

Overall, the results show that the accuracies of the IWMI GIAM V2.0 was about 7 to 12 percent higher than FAO\UF V3.0. The errors of omission and commission were only slightly better in GIAM. The area calculations in the two maps differ significantly since IWMI GIAM uses: (a) intensity of irrigation to obtain irrigated areas based on seasons, and (b) sub-pixel decomposition techniques to obtain the irrigated fraction within a pixel. The FAO/UF relays on the country statistics that may not include intensity. Further, there is no direct link between FAO spatial data and area statistics. The areas are reported directly from country statistics and the spatial distribution of irrigation is ?adjusted? to fit the country statistics, using known extents of surface irrigation and other secondary information. The India?s CBIP under-estimates irrigation since it, largely, ignores informal (e.g., ground water) irrigation. For India portion GIAM10km V2.0 map accuracies and errors were significantly better than that of FAO/UF V3.0 and CBIP (Table 11a and 11b). Especially, the errors of omissions and commissions were much better, indicating that IWMI GIAM is picking the informal (e.g., small reservoirs, tanks, ground water) irrigation better.

There are fundamental issues related to accuracy assessments at such large scales as 1-km or 10-kilometer resolution pixel size. There are considerable difficulties in ground truthing and establishing the exact percent of area irrigated in a 1-km x 1-km (100 hectares) and especially at 10 km x 10 km (or 10,000 hectares) resolutions. For example, when GT data is collected in a portion of a pixel that has land cover other than irrigation and has irrigation in patches (say 25 percent of pixel area), we may not even see  irrigated portions during GT data collection. This will lead to the pixel being labeled ?other LULC? in GT data, but whereas in reality it has 25 % irrigation. Satellite sensors capture the average reflectivity from the pixel and hence are influenced by both the irrigated as well as non-irrigated components within the pixel leading to a average spectra for the pixel. Whereas satellite data distinctly shows the difference in a pixel with zero irrigation and one with 25 percent irrigation, GT data often fails to do so. This will lead to situations such as, for example: (a) rainfed GT points falling on a pixel mapped as irrigated (commission error); (b) irrigated GT points falling on a pixel mapped as other LULC (omission error). This can lead to somewhat higher omission and commission errors. The phenomenon is acute when dealing with pixels of low percent (<20) of irrigation which have greater likelihood of being labeled as classes other than irrigation, resulting in highly exaggerated commission errors. This also implies an area based accuracy assessment maybe more powerful and robust than point based accuracy assessment. However, quality area based reference data is nearly non-existent. Offset against this spatial advantage of remote sensing, is the fact that there are multiple reasons for an average pixel scale signal, and it is therefore possible to confound on interpretation with another reality. The very high resolution (sub-meter to 4 meter) images available in google earth facilitate determining the land cover and irrigation structural patterns which will be invaluable in determining irrigation vs. non-irrigation. Hence, the GEGT is considered a better data for accuracy assessment.

The accuracy assessment comparison between the GIAM10km, FAO/UF, and CBIP maps (Table 11a and 11b) are indicative and not definitive. In a strict sense, none of these maps can be directly compared with each other as a result of considerable differences in scale/resolution, primary datasets used to derive the map information, and due to differences in methods, techniques, and approaches on how these maps are derived.

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9.6 Accuracies and areas

Even if the accuracies and errors between the IWMI GIAM10km and FAO/UF maps are similar, the calculated areas differ as a result of fundamental differences in how the maps are produced. In IWMI-GIAM, the global annualized (i.e., taking cropping intensity or seasons) irrigated area is 494.4 Mha and the net areas per season are: 278 Mha, 176.5 Mha, and 39.9 Mha (Figure 33 and Table 9a). In contrast the other global irrigated area estimates 274 Ma (Siebert,et al., 2005; Siebert, S., Döll, P., Hoogeveen, J., 2002), in which the FAO/UF provides area ?equipped for irrigation? to be 274 Mha (Siebert,et al., 2005; Siebert, S., Döll, P., Hoogeveen, J., 2002). Accuracies can be similar, but areas can differ because:

1. Intensity (seasonality) consideration: The IWMI GIAM10km V2.0 provides gross areas based on cropping intensity (single crop, double crop, triple crop, continuous crop). Other area estimates count the area once (net) based on area equipped and assuming irrigation once during a major cropping season;
2. Sub-pixel fraction differences: The irrigation fraction in IWMI method depends on the 3 methods (GEE, HRI, and SPDT). The FAI/UF is dependant on Country statistics at sub national level.; and
3. Area estimation approaches: The FAO/UF area calculations are dependant on the national statistics and their extrapolation onto spatial maps. The IWMI GIAM is interpreted directly from the satellite image characteristics.

Table 11a. Accuracy assessment of IWMI GIAM V2.0 Vs. FAO/UF V3.0 vs. CBIP using ground truth data. The IWMI Global irrigated area map (GIAM) is compared with the: (a) Global irrigated area map of the FAO/Frankfurt University, and (b) India's Central Board of Irrigation and Power (CBIP).

Table 11b. Accuracy assessment of IWMI GIAM V2.0 Vs. FAO/UF V3.0 vs. CBIP using Google Earth ground truth (GEGT). The IWMI Global irrigated area map (GIAM) is compared with the: (a) Global irrigated area map of the FAO/Frankfurt University, and (b) India's Central Board of Irrigation and Power (CBIP).

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10.0 A discussion on mapping irrigated areas and comparison of maps

Irrigated area maps of IWMI GIAM10km V2.0, Food and Agricultural Organization\University of Frankfurt (FAO\UF) V3.0, and India?s Central Board of Irrigation and Power (CBIP) maps are compared and discussed. We shall begin with detailed illustrations of comparisons in India where we have detailed ground truth data collected by the GIAM team and very reliable and detailed maps from the CBIP. The extensive ground truth data collected during the field campaigns were invaluable in these comparisons.

10.1 Major irrigation

First we shall illustrate a comparison of maps for major irrigation. The CBIP map, basically, represents major irrigated areas (leaving out informal irrigation) and is considered accurate for major command area irrigation. For the purpose of comparison we took 5 random ground truth (GT) points falling within CBIP map (Figure 38b) and overlaid them on the IWMI GIAM10km V2.0 for India (Figure 38a) and CBIP irrigated area map for India (Figure 38b). According to GT data, 2 were informal (tank, ground water) irrigation, 1 was major irrigation, 1 naturally irrigated, and 1 rainfed. The GIAM10km classes (Figure 38a) showed 3 informal (2 conjunctive and 1 ground water) and 2 surface water. The CBIP (Figure 38b) showed all points as major irrigation. These results clearly implied that the GIAM10km has closer match with ground reality in terms of type of irrigation.

10.2 Informal irrigation

Next, we illustrate how well the informal (e.g., small reservoirs, tanks, ground water) irrigation is captured between maps. For the purpose, we randomly select 5 GT points with informal irrigation. The CBIP map misses all the randomly selected groundwater check points (Figure 38c). The GIAM10km V2.0 idenitifies all of them- 3 as conjunctive and 1 as ground water irrigation (Figure 38c). This is very close to ground truth data which also had 3 supplemental irrigated area classes. Finally, identification of small scale irrigation from minor reservoirs and ground water is illustrated in Figure 38e for CBIP and Figure 38f for IWMI GIAM10km V2.0. Of the 5 randomly chosen GT points (2 irrigated small scale, 1 irrigated large scale and 2 rainfed) CBIP misses all (Figure 38e) whereas GIAM maps 3 as conunctive and 2 outside. It actually maps 2 correctly as informal irrigation, 1 rainfed correctly as ?outside? irrigated areas. Of the other two points, it maps a rainfed class as informal irrigation and and informal irrigation as ?outside irrigated area?. Leading to some omission and commission errors. However, as we have seen in Figure 38a through 44f, informal irrigation is well captured in GIAM10km.

 

Figure 38. Evaluation of the GIAM for large scale, small scale, informal, and supplemental irrigation. The IWMI GIAM and India?s Central board of irrigation and power (CBIP) irrigated area maps are evaluated for: (a) large scale irrigation- (Figure 38a and 38b); (b) informal irrigation such as ground water and tanks (Figure 38c and 38d); and (c) small scale (e.g., minor reservoirs) irrigation (Figure 38e and 38f).

10.3 Comparing global products in India

The comparison between FAO/UF global irrigated area map and GIAM10km V2.0 highlights the distinct features of areas where the two maps: (a) perfectly match (e.g., Figure 39a in Upper Ganges basin), (b) broadly match (e.g., Figure 39b in Cauvery delta), and (c) do not match at all (e.g., Figure 39c in Ganges delta). This illustration is a ?representative? comparison of the two global irrigated area maps as we see the similar trends in other places of the World. The countrywise area statistics of the 40 best ranked irrigated area Countries of the World are plotted for the two maps taking FAO\UF Country statistics and IWMI GIAM10km V2.0 season 1 areas which showed an R² value of 0.92  (Figure 40).

Figure 39 (a-c). Comparison of the two global irrigated area maps: GIAM10km V2.0 and FAO/UF V3.0.

Figure 40. Comparison of irrigated areas of 40 leading countries between IWMI GIAM10km V2.0 vs. FAO\UF V3.0.

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11.0 Irrigated area class names

At this stage it is useful, to discuss the issues involved in final class labeling and the approach used in IWMI GIAM10km V2.0. Classes were named based on a set protocol and rigorous methods (see Figure 8) that had a clear class naming convention (Figure 27). In addition, the final class labeling (see Figure 33, 34, and 35) was also based on consultation with irrigation experts so that the class names represent the commonly understood meaning of a particular irrigation type. More generic and detailed names are provided in GIAM10km-28 classes (Figure 33) and a much simpler, broadly understood names are provided in GIAM10km-8 classes (Figure 34), and GIAM10km-3 classes (Figure 35).

The final class labeling were categorized under the following groups:

Irrigated, surface water, single crop, crop type or dominance.
Irrigated, surface water, double crop, crop type or dominance.
Irrigated, surface water, continuous crop, crop type or dominance.

The watering method (irrigated or rainfed) and irrigation type (surface, ground or conjunctive use) are determined based on the protocols and methods (see Figure 8 and sections 5 and 6). The single, double or continuous crop is determined based on the spectral signature for every class based on time-series satellite imagery (see example in for class 1 and 4 in Figure 41). The same class 1 and 4 also occur in Iran showing somewhat different signature characteristics (in magnitude and timming of peaks and lows). Indeed, it is possible to get a cropping calendar for every pixel of irrigated area classes by simply clicking on any point on irrigated area class and looking through the time-series imagery of a mega-file as we have done in Figure 41 and 42. Final variable (crop type or dominance) in naming is based on ground truth data and literature.

The above naming convention is repeated for ground water and conjunctive use irrigation.

Figure 41.  Single crop (red) and double crop (cyan) irrigation in lower Ganges.

Figure 42. Double crop (left) and single crop (right) irrigation in Zahandeh and Rud.

In Figure 33, Classes 1-10 are surface water irrigation, classes 11-15 are ground water irrigation, and classes 16-28 are conjunctive use irrigation. These classes were combined appropriately to produce the simplified 8 call and 3 class irrigated area maps (see Figure 34 and 35).

The class labeling process of one class has been discussed in detail. First, we go through a normal protocol (Figure 8), Methods (sections 5 and 6), and class naming convention (Figure 27). In addition, the detailed approach to name a class is illustrated below for one class. The class 28 (Figure 43) was labeled ?irrigated, conjunctive use, continuous crop, mixed crop? in GIAM10km - 28 class map (Figure 33). It occurs mainly in the Pampas of Argentina, which is predominantely rainfed. However, different degrees of supplemental groundwater irrigation (e.g., pivots, drip) and some pumping from rivers also exist. Centre pivot irrigation is used in humid plains of pampas to supplement rainfall (Maletta, 1998 and Maletta, 1999).

The spectral characteristics of the class show near continuous cropping, with AVHRR NDVI greater than 0.35 or more throughout the year (Figure 43). Rainfall during May-September is low, averaging less than 40 mm per month (see Figure 43) and is insufficient to sustain such high vegetation in an agricultural belt. The pampas is a humid plain, which is  very flat and is poorly drained. This and man made obstacles such as roads and railroad embankments lead to flooding and waterlogging for months, favoring growth of weeds and natural pasture in the vicinity even during relatively dry spells (Maletta, personnel communication). The long period of deficit rainfall, and continuously high NDVI strongly implies some degree of irrigation.

Evidence (Maletta, personal communication) from the field suggest that centre-pivot irrigation in the Pampas is mostly used for complementary and drip irrigation is used for  horticulture. Maletta summarizes the situation: ?There is indeed a need to irrigate more, as witnessed by the fact that average yields (especially for maize) are quite below potential. But (1) massive use of irrigation is not yet happening, (2) aquifers may not support such an extensive use of underground water, and (3) gravity irrigation is in general difficult due to very flat land, thus requiring pumping (which is not generally done) from the many streams flowing through the plains.? The data from the Government administration (http://www.indec.mecon.ar/) shows nearly 1.4 million hectares irrigated. These do not account for an occasional irrigation (e.g., one or two irrigations during the cropping period, during deficit rainfall periods) or informal (individual farmers irrigating without governmental knowledge mainly through ground water pumping). Overall, the pampas region depends on rainfall, but has significant proportion of irrigated land (pivots, drip, river pumps), humid flat-water logged regions and scattered informal irrigation. These characteristics lead the class to be named: ?conjunctive use?.  In the past, irrigated area maps only included areas with formal canal networks and major works such as  reservoirs or barrages. But many parts of the world have various levels of irrigation that need to be accounted to obtain a realistic estimate of actual irrigated areas.

 

Figure 43. Evaluation of GIAM for conjunctive irrigation. The rainfed class with significant central pivot supplemental irrigation in the Pampas in Argentina.

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12.0 GIAM10km V2.0 products and dissemination

The IWMI GIAM10km V2.0 data and products are distributed via dedicated web page at: http://www.iwmigiam.org

The web page consists of GIAM10km V2.0 products at global level mapped at 1-10 kilometers and include maps, images, class characteristics, area calculations, snap-shots (high-resolution images) and photos, animations, and accuracies. The products are made available at nominal resolution of approximately 1 kilometer since all data were resampled and analyzed at 1-km scale. However, we urge the users to treat it as a nominal 10 km² since a overwhelming proportion of the data used in analysis is at this scale. But it must be noted that a significant proportion of the mega data used in analysis include SPOT time-series for 1999 and GTOPO30 were at 1-km. GIAM10km map is also available for Google Earth, please download GIAMv2.kmz file from the home page of GIAM main site (http://www.iwmigiam.org)

The primary GIAM10km V2.0 products are:

            GIAM10km V2.0 28 class map (GIAM10km-28 classes);
            GIAM10km V2.0 8 class map (GIAM10km-8 classes);
            GIAM10km V2.0 3 class map (GIAM10km-3 classes).

The website additionally contains three other global agriculture products and their associated documentation:

·        Global map of Rainfed Cropped Areas (GMRCA)

- Dis-aggregated 229 class map

- Aggregated 22 class map

·        Global map of all land use/land cover (LULC) areas (GMLULCA)

- Dis-aggregated 76 class map

- Aggregated 10 class map

·        Global IWMI generic 951 class map (Generic-IWMI-951)

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13.0 Conclusions

The International Water Management Institute (IWMI) has produced a global irrigated area map at 10 kilometer scale (GIAM10km V2.0), for the end of last millennium, using remote sensing data. The total annualized irrigated areas of the World are 480 million hectares (or Mha). Globally, the area available for irrigation is 412 Mha. Annualized area takes into consideration irrigated areas during different seasons over same areas within a given year. Of the total annualized area of 480 Mha, a total of 75 percent (375 million hectares) of all irrigated areas of the world is in Asia, followed by Europe with 8 percent North America with 7 percent, South America 4 percent, Africa 2 percent, and Australia 2 percent. The irrigated areas spread across the season are: (a) 263 Mha for season 1, (b) 176 Mha for season 2, and (c) 41 Mha for continuous.

Two Countries, China and India, together have a staggering 59 percent (284 Mha) of all the Global annualized irrigated areas. Of the 59 percent, China has 31.5 percent and India 27.5 percent. China has an annualized area of 151 million hectares and India 132 million hectares. The first or the major cropping seasonal areas follow similar pattern to annualized areas. China and India have extensive double cropping. In the first season China has 76 Mha irrigated, followed by 68 Mha in the second season. In India, the area irrigated is 73 Mha in first season and 54 Mha in second season. The next leading irrigated area countries (as a percentage of the global annualized sum of 412 Mha) are USA (5 percent), Russia (3.5 percent), and Pakistan (3.3 percent). There are 9 countries (Argentina, Australia, Thailand, Bangladesh, Turkey, Kazakhstan, Myanmar, Uzbekistan, and Vietnam) between 1 to 2 percent. Every other country in the world, individually, only has less than 1 percent area irrigated. The 40 leading irrigated area countries have nearly 96 percent of all irrigation in the World. Surface water irrigation is 61 percent and the rest (39 percent) is conjunctive (surface and ground water) or pure ground water.

There are three global irrigated area maps produced by GIAM team: GIAM10km 28 class map, GIAM10km 8 class map, and GIAM10km 3 class map. The classes represent: (a) irrigation by surface water, ground water, and conjunctive use; (b) cropping intensity (e.g., single crop, double crop, and continuous crop) are provided for every class; and (c) crop type or dominance. The accuracy of mapping irrigated areas were determined using 3 independent datasets- 2 ground truth data sets and 1 Google earth estimate dataset. The accuracies varied between 84 to 91 percent, the errors of omissions less than 16 percent, and errors of commission less than 21 percent. The results of our study were compared with the irrigated area map statistics of the Food and Agricultural Organization of the United Nations (FAO) and the University of Frankfurt (UF) version 3.0 (FAO\UF V3.0). The FAO\UF used National statistics and GIS techniques to derive irrigated areas. FAO\UF V3.0 determined ?area equipped for irrigation? (but not necessarily irrigated) for the World as 271 Mha which is quite different from GIAM10 km V2.0 TAAI of 412 Mha. For the leading 40 countries, which constitute 91 percent of all irrigated areas of the World, the GIAM10km V2.0 season 1 areas versus FAO\UF V3.0 areas had a R²- value of 0.90.

The key achievements of the GIAM10km V2.0 work have been:

1. Methodology development: a comprehensive set of methods and techniques for mapping irrigated areas of the World using remote sensing data at various scales or pixel resolution has been developed (see this research report and also earlier work by Thenkabail, et al., 2005, Thenkabail et al., 2006, Biggs et al., 2006):

1.1 Advances in approaches and datasets: mega-file compositions through fusion of multi-resolution time-series imagery;

1.2  Advances in methods: hyperspectral techniques for multispectral time-series mega-file imagery. The methods include spectral matching techniques (SMTs) and space-time spiral curves;

1.3 Class identification and labeling: Rigorous strategies for class identification and labeling have been developed. Strategies for resolving mixed classes through GIS modeling in which wide array of secondary datasets have been used has been established;

1.4  Sub-pixel areas (SPAs) and irrigated area fractions (IAFs): Innovative sub-pixel area (SPA) calculation methods using irrigated area fractions (IAF) has been developed. Three IAF methods were developed: (a) IAF based on Google earth estimate (GEE), (b) IAF based on high resolution imagery (HRI), and (c) IAF based on sub-pixel decomposition technique.

Generally, the areas calculated from remote sensing are, almost always, reported as full pixel areas (FPAs). But the correct areas can be only obtained through SPA. This is especially true in coarser resolution imagery. Development of practical methods to obtain SPAs through IAFs is, thereby, a highly significant achievement.

2. Annualized areas (or Intensity of irrigation) and irrigated area fractions (IAFs): The study determined and provided IAFs through 3 methods. The irrigated area fractions from the Google eye (IAF-GEE) when used to multiply the full pixel areas (FPAs) provide total area available for irrigation (TAAI). The IAF from high resolution imagery (IAF-HRI) and sub-pixel decomposition technique (IAF-SPDT) can be obtained for different seasons (e.g., season 1 crops, season 2 crops and so on). The seasonal IAF coefficients helped determine irrigated areas of every class for season 1, season 2, and continuous. Annualized (or intensity) is summation of season 1, season 2, and continuous. The coefficients of IAF-HRI and IAF-SPDC were combined to provide more robust SPAs.

The annualized areas are very unique. Ability to determine annualized areas has huge implications of the intensity of irrigation in given land and the implications in determining the quantum of food production and water consumption.

3. Informal irrigation: The GIAM10km demonstrated the ability to map informal irrigation (i.e., irrigation from minor reservoirs, tanks, and ground water) well. This is especially crucial given the quantum of informal irrigation in the world, especially from millions of tube wells.

4. Crop characteristics: Every class (or for that matter every pixel within a class) will have its own characteristics in terms of its vegetation dynamics and seasonality. GIAM10km product is not just a map. It is a dynamic tool from which one can study variables such as cropping calendars, crop growth stages, biomass levels, and fraction areas irrigated.

5. Precise location of irrigated areas: Most irrigated area maps provide areas without showing precise location of irrigated areas. For example, an entire state or country is often shown to have certain percentage area irrigated without showing where exactly it is. GIAM10km map provides precise location. The errors of omissions (less than 16 percent) and commissions (less than 21 percent).

6. Product line: GIAM data and products are made accessible online for free as a global public good (GPG) from anywhere in the world (http://www.iwmigiam.org). The products consist of, for example, irrigated area maps, statistics, 20-year every month animations, snap-shots of higher resolution imagery to help visualization of classes, class characteristics, irrigated area fractions for area calculations, methods, and datasets.

Within the scope of GIAM project, irrigated areas are also mapped at 500-m resolution for India, as a start, and 30-m resolution for the Ruhuna basin in Sri Lanka and Krishna basin in India.

Currently, IWMI is in the process of developing a joint vision and strategy with FAO\UF on global irrigated area mapping. We are also developing partnerships and collaborations with the National Governments and Institutes. To that end, work continues on the development of techniques to map and test the accuracy of classification across the full extent of the Indian sub-continent (Pakistan, India, Bangladesh) and Sri Lanka, covering a range of agro-ecologies and degrees of difficulty for remote sensing (in terms of cloud cover, heterogeneity and scale of landscapes and land use). The work is expected to be expanded to China and other Countries. A Consortium for Irrigated Area Mapping and Assessment (CIAMA) is expected to be set up, with an array of International partners, during the GIAM2006 International Workshop to be held in Colombo, Sri Lanka.

The team seeks feedback from all users, readers and interested parties, and continues to harvest ground-truth data to verify and upgrade the map. The team welcomes any feedback on the methods and results, and actively seeks to expand the available ground-truth in order to build a global ground-truth database within the IWMIDSP (http://www.iwmidsp.org ). All the imagery and documentation associated with GIAM are made available through the dedicated portal: http://www.iwmigiam.org.

The products consist of maps, images, class characteristics, area calculations, snap-shots, animations, and accuracies. It is our hope that these products will, in time, be a useful resource for the remote sensing and water management community ? both for researchers and practitioners.

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15 Annexure

Annex 1 : Irrigated Areas of countries from GIAM10km V 2.0 and other sources

Annexure 2

The crop calendars of the classes for Australia differ significantly from rest of the world. Hence the Australian area was calculated based on the Country crop calendar. However, we have retained the same irrigated area fractions as global, except for class 19 for which the fraction was 0.10. Based on this approach, the Country area for Australia is shown in Table A1 below.

Table 1. Irrigated areas of Australia based on GIAM 10-km.

During the next phase, the irrigated areas will be computed based on:

A.    country-wise crop calendar; and B.    country-wise crop coefficient.

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16.0 Acronyms and Abbreviations

2d-FS AOAW                                  AVHRR                                  BGW                                      CRU                                        CBIP                                       DAAC                                    DCP                                        DTED                                     DEM                                       EDC                                        EGT1500                                ERDAS                                  EROS                                      ETM+                                     FAO                                       FGT75                                    Generic-IWMI-628               GIS                                          GLC2000                                GIAM                                     GMRCA                                 GMLULCA                            GPS                                         GSFC                                      GTOPO30                              IGBP                                       IMW                                      IWMI                                     IWMI-DSP                            ISOCLASS                            JERS-SAR                             JPEG2000                               LULC                                      MODIS                                  NPOESS                                 MIR                                        MODIS                                  MVC                                       NASA                                    NDVI                                      NESDIS                                  NIR                                         NGDC                                   NOAA                                    NPOESS                                 PCA                                        PGT2400                                RFSAR                                   SCS                                         SMT                                       SP-DCT                                  SSV                                         SPOT                                      SPOT VGT                             ST-SC?s                                 TAAI                             Terra                                       TLT280                                  USGS                                      UTM                                      VNIR                                      VIIRS                                      WGS84

2 dimensional feature space All other areas of the World segment Advanced Very High Resolution Radiometer Brightness-greenness-wetness Climatic Research Unit Central Board of Irrigation and Power Distributed Active Archive Centers Degree Confluence Project Digital Terrain Elevation Data Digital Elevation Model EROS Data Center Elevation greater than 1500 m segment Earth Resources Digital Analysis System Earth Resources Observation Systems Enhanced Thematic mapper plus Food & Agricultural Organization of UN Forest cover greater than 75 percent Generic IWMI 628 class map Geographic Information System Global Land Cover classification for the year 2000 Global irrigated area map Global map of rainfed cropland areas Global map of land use/land cover areas Global Positioning System Goddard Space Flight Center Global digital elevation model (DEM) with a horizontal grid spacing of 30 arc-seconds (approximately 1 kilometer) International Geosphere Biosphere Program International Map of the World International Water Management Institute International Water Management Institute Data Storehouse Pathway Statistical clustering algorithm in ERDAS Japanese Earth Resources Satellite-Synthetic Aperture Radar Joint Photographic Experts Group new imaging compression standard Land use/land cover Moderate Resolution Imaging Spectroradiometer National Polar Operational Environmental Satellite System Mid-Infrared Moderate-resolution Imaging Spectro-Radiometer Maximum value composite National Aeronautics and Space Administration Normalized Difference Vegetation Index National Environmental Satellite Data and Information System Near-Infrared National Geophysical Data Center National Oceanic and Atmospheric Agency National Polar Operational Environmental Satellite System Principal component analysis Precipitation greater than 2400 Rainforest Synthetic Aperture Radar Spectral correlation similarity Spectral matching technique Sub-pixel de-composition technique Spectral similarity value Satellites Pour l’Observation de la Terre or Earth-observing Satellites SPOT Vegetation sensor Space time spiral curves Total Area Available for Irrigation Earth Observing System (EOS) satellite-NASA flagship satellite under Earth System Enterprise Temperature less than 280 degree Kelvin United States Geological Survey Universal Transverse Mercator Visible and Near-Infrared Visible and Infrared Imaging Radiometer Suite World Geodetic System 1984

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17.0 Acknowledgements

A Global Irrigated Area Mapping (GIAM) project of this magnitude and complexity can never be done without substantial and persistent support from many places.

We are very grateful to Prof. Frank Rijsberman, Director General of IWMI, for his vision, guidance, intellectual, moral, and financial support. Such support is very rare to come by. Thank you Frank for your vision and leadership. Dr. David Molden, Principal Researcher at IWMI, was instrumental in initial funding of the GIAM through Comprehensive Assessment (CA), A UN Mellennium initiative. We also thank him for his moral and intellectual inputs. The project was initially conceptualized and lead throughout by Hugh Turral. Hugh?s energy has been amazing given that he has to juggle this with Theme leadership at IWMI. But he has always had time for sustained and stimulating discussions on GIAM. That is true leadership. Sarath Abayawardena, former Global Research Director (GRD), was instrumental in laying a strong foundation for the RS\GIS laboratory at IWMI. We remember that support very much. Julie Van der Bliek, the present GRD head, has continued this support and has steered us towards a spatial data policy at IWMI. All of these efforts have helped GIAM. Honestly, we can not list everyone who have helped us at various stages of the project in many different ways. Everyone in RS\GIS unit have helped in one way or the other. Thank you Aminul Islam for the excellent ground truth data of the world and for the rainfall data compilation. Wasantha Kulawardana for support when needed. Sarath Gunasinge, and Ranjith Alankara are always there for silent strong support in producing maps and flow charts. Jacintha Navaratne provided outstanding secretarial services, most cheerfully.  

IWMI India office was specially helpful. Thank you Trent Biggs, Muralikrishna, and Parthasarathi. We would like to thank the Food and Agricultural Organization (FAO)\University of Frankfurt (UF) for lively discussions on the two Global Irrigated Area Maps (FAO\UF and IWMI). Specifically, we would like to thank Stefan Siebert, Jippe Hoogeveen,

The NASA Goddard Space Flight Center (GSFC) made available the AVHRR time series used in this work. Special thanks to Dr. Ron Smith and group. The Landsat data were downloaded from the University of Maryland?s Global Land Cover Facility (GLCF). Several datasets such as the GTOPO30 1-km and SRTM 90 meter elevation data are downloaded from the USGS\EROS. The forest cover data from Dr. Ruth DeFries of University of Maryland. Rainfall data was provided by Dr. Tim Mitchell of East Anglica Climate Research Group. The JERS SAR data from Saatchi and group. The volunteer ground truth data from degree confluence project was invaluable. The Google Earth Data is state-of-art and was widely used. Without these great datasets, made available for free, the project would never have got started. So we are very, very grateful to these Agencies and numerous people behind it.

At times like this, the release of the first satellite sensor based Global Irrigated area map, we cherish the wonderful memories of our revered Gurus and venerable universities; from whom we learnt and got taste of knowledge. They were true giants to whom this credit should really go. It is unfair to mention one name and not several others. But it will at least take a page to do justice to everyone.

Finally, the entire team (see list of authors and also in acknowledgements) that worked on this project has been wonderful. Long hours and stiff deadlines were common. At times, our patience and resolve were tested severely due to nightmares of data chaos and organization. But the morale and motivation was always high. Support for each other as good as it can get. Intellectually very stimulating and challenging- something we always enjoyed and looked forward to. In many ways, we all learnt and made progress.