Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) 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 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 150 m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).

[1]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[2]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[3]  User's guide for the Solar Backscattered Ultraviolet (SBUV) and the Total Ozone Mapping Spectrometer (TOMS) RUT-S and RUT-T data sets: October 31, 1978 to November 1, 1980 , 1983 .

[4]  B. Holben Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[5]  C. Tomlin Geographic information systems and cartographic modeling , 1990 .

[6]  Jonathan Raper,et al.  Introductory Readings in Geographic Information Systems , 1991 .

[7]  C. Rao,et al.  Degradation of the visible and near-infrared channels of the advanced very high resolution radiometer on the NOAA-9 spacecraft : assessment and recommendations for corrections , 1993 .

[8]  Gururaj Hunsigi,et al.  Irrigation and drainage , 2009 .

[9]  R. H. Evans,et al.  Nonlinearity corrections for the thermal infrared channels of the advanced very high resolution radiometer: assessment and recommendations , 1993 .

[10]  J. Faundeen,et al.  The 1 km AVHRR global land data set: first stages in implementation , 1994 .

[11]  J. Schilfgaarde Irrigation — a blessing or a curse , 1994 .

[12]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[13]  W. Farrand Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho, through the use of a constrained energy minimization technique , 1997 .

[14]  Thomas R. Loveland,et al.  The IGBP-DIS global 1 km land cover data set , 1997 .

[15]  Ruth S. DeFries,et al.  The NOAA/NASA pathfinder AVHRR 8-Km land data set , 1997 .

[16]  B. Zhang,et al.  Study on the classification of hyperspectral data in urban area , 1998 .

[17]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[18]  Limin Yang,et al.  An analysis of the IGBP global land-cover characterization process , 1999 .

[19]  Sandra Postel,et al.  Pillar of Sand: Can the Irrigation Miracle Last? , 1999 .

[20]  F. Fierens,et al.  Development of cloud, snow, and shadow masking algorithms for VEGETATION imagery , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[21]  S. Saatchi,et al.  Mapping land cover types in the Amazon Basin using 1 km JERS-1 mosaic , 2000 .

[22]  J. Townshend,et al.  A new global 1‐km dataset of percentage tree cover derived from remote sensing , 2000 .

[23]  David Seckler,et al.  Water supply and demand, 1995 to 2025 , 2000 .

[24]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[25]  Petra Döll,et al.  A digital global map of irrigated areas. , 2000 .

[26]  J. N. Sweet,et al.  An evaluation of atmospheric correction techniques using the spectral similarity scale , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[27]  Felix Kogan,et al.  Evolution of long-term errors in NDVI time series: 1985–1999 , 2001 .

[28]  J W Schwarz,et al.  Adaptive Threshold for Spectral Matching of Hyperspectral Data , 2001 .

[29]  Peter Droogers,et al.  Global irrigated area mapping: overview and recommendations. , 2002 .

[30]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[31]  T. Shah Sustaining Asia's groundwater boom: an overview of issues and evidence , 2003 .

[32]  Pawan Kumar Joshi,et al.  SPOT vegetation multi temporal data for classifying vegetation in south central Asia , 2003 .

[33]  T. D. Mitchell,et al.  A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100). , 2004 .

[34]  C. Tucker,et al.  NASA’s Global Orthorectified Landsat Data Set , 2004 .

[35]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[36]  M. Ashton,et al.  Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .

[37]  Petra Döll,et al.  Development and validation of the global map of irrigation areas , 2005 .

[38]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[39]  Prasad S. Thenkabail,et al.  Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data , 2005 .

[40]  M. Schull,et al.  An irrigated area map of the world (1999) derived from remote sensing , 2006 .

[41]  P. Thenkabail,et al.  Irrigated area mapping in heterogeneous landscapes with MODIS time series, ground truth and census data, Krishna Basin, India , 2006 .

[42]  C. Woodcock,et al.  Resolution dependent errors in remote sensing of cultivated areas , 2006 .

[43]  D. Molden Water for food, water for life: a comprehensive assessment of water management in agriculture , 2007 .

[44]  D. Molden,et al.  Fourier analysis of historical NOAA time series data to estimate bimodal agriculture , 2007 .

[45]  P. Thenkabail,et al.  Spectral Matching Techniques to Determine Historical Land-use/Land-cover (LULC) and Irrigated Areas Using Time-series 0.1-degree AVHRR Pathfinder Datasets , 2007 .

[46]  Eric Viala,et al.  Water for food, water for life a comprehensive assessment of water management in agriculture , 2008 .

[47]  Prasad S. Thenkabail,et al.  Influence of Resolution in Irrigated Area Mapping and Area Estimation , 2009 .

[48]  Yuanjie Li,et al.  A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[49]  Regassa E. Namara,et al.  Spatial models for selecting the most suitable areas of rice cultivation in the Inland Valley Wetlands of Ghana using remote sensing and geographic information systems , 2009 .

[50]  Prasad S. Thenkabail,et al.  Irrigated areas of India derived using MODIS 500 m time series for the years 2001–2003 , 2010 .