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A global irrigated area map has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth, and Groundtruth data. The data included:(a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10-km monthly time-series for 1997-1999,(b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1-km monthly time series for 1999,(c) East Anglia University Climate Research Unit (CRU) rainfall 50-km monthly time series for 1961-2000,(d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1-km digital elevation data of the world,(e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996, and (f) University of Maryland Global Tree Cover 1-km data for 1992-93. A single mega-file data-cube (MFDC) of the world with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1-kilometer resolution. The MFDC was segmented based on elevation, temperature, and precipitation zones. Classification was performed on the segments.

Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match the same with ideal or target spectra. A rigorous class identification and labeling process involved the use of:(a) space time spiral curve (ST-SCs) plots,(b) brightness-greennesswetness (BGW) plots,(c) time series NDVI plots,(d) Google Earth very high resolution imagery (VHRI)“zoon in views” in over 11,000 …