This Product has Three Main Modules:
Global Irrigated Area Mapping Data portal
http://www.iwmigiam.org/info/gmia/default.asp
Global Irrigated Area Mapping Statistics categorized into Global, Continent, National, River-Basin and India’s Sub-National level. http://www.iwmigiam.org/stats/
Global Map of Rain-fed Cropland Areas (GMRCS)
http://www.iwmigiam.org/info/gmrca/default.asp
You can access and download FREE DATA of all above products and many more at : http://www.iwmigiam.org/
INTRODUCTION: A Global irrigated area map has been produced for a nominal year of 1999 using multiple satellite sensor and secondary data. Multiple resolution time series data used in the study were: (a) AVHRR 4-band and NDVI 10-km monthly time series for 1981-1999, (b) SPOT vegetation NDVI 1-km monthly time series for 1999, and (c) East Anglia University Climate Research Unit Rainfall 50-km monthly time series for 1961-2000. Additional major global data sets used were (a) GTOPO-30 1-km elevation, (b) JERS SAR data for the rainforests during two seasons in 1996, and (c) University of Maryland Global Tree Cover 1-km data for 1992-93.
A number of new methods and techniques were developed. The study first segmented the world into climate and elevation zones and analyzed satellite images separately for these zones. The class identification and labeling process began with spectral matching technique (SMTs). Since time-series data are analogous to hyperspectral data, we adopted hyperspectral analysis techniques such as SMTs to identify, group, and label classes with similar time series characteristics. The time-series spectra of classes were also compared with the target ones obtained from ground truthed locations. The spectral correlation similarity was found to be the most useful spectral matching technique (SMT). Classes are then ?verified?, at 30-50 randomly chosen locations that are well distributed across the globe, by inspection of Google Earth images for which the resolution varies between sub-meter to 30-meter.
Multiple image interpretation techniques such as bispectral plots, space time spiral curves (ST-SCs), time-series plots of normalized difference vegetation index (NDVI), and a host of secondary data (e.g., national and global land\use and land cover data) were used, including ESRI 150-m Landsat Geocover mosaic of the world.. Broadly sourced ground truth data were used in identifying, labeling and refining classes. First: IWMI?s primary ground truth data set of nearly 2000 points that include: a) three missions conducted in 2004 and 2005 that cover the whole of India; b) extensive data from river basins with extensive irrigation areas such as the Ganges and Krishna in India, Ruhuna in Sri Lanka, Syr Darya in Central Asia and Limpopo in Southern Africa; and c) a past data catalogue from the Middle East and 14 Countries in West Africa. Second: data sourced from the Degree Confluence Project with about 4000 points that collates land use data for 1 by 1 degree tile over the globe. In addition nearly 11,000 ?zoom in views? of high or very high resolution Google earth points. Decision tree algorithms, NDVI time series plots, NDVI thresholds, principal component analysis, unsupervised clustering algorithms, and GIS spatial modeling using data such a agroecological zones, temperature, precipitation, evapotranspiration, and elevation were widely used to define and refine classes especially to resolve mixed classes.
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