Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data

Abstract Irrigation accounts for 70% of global water use by humans and 33–40% of global food production comes from irrigated croplands. Accurate and timely information related to global irrigation is therefore needed to manage increasingly scarce water resources and to improve food security in the face of yield gaps, climate change and extreme events such as droughts, floods, and heat waves. Unfortunately, this information is not available for many regions of the world. This study aims to improve characterization of global rain-fed, irrigated and paddy croplands by integrating information from national and sub-national surveys, remote sensing, and gridded climate data sets. To achieve this goal, we used supervised classification of remote sensing, climate, and agricultural inventory data to generate a global map of irrigated, rain-fed, and paddy croplands. We estimate that 314 million hectares (Mha) worldwide were irrigated circa 2005. This includes 66 Mha of irrigated paddy cropland and 249 Mha of irrigated non-paddy cropland. Additionally, we estimate that 1047 Mha of cropland are managed under rain-fed conditions, including 63 Mha of rain-fed paddy cropland and 985 Mha of rain-fed non-paddy cropland. More generally, our results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources. However, regions with rapidly changing irrigation or complex mixtures of irrigated and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.

[1]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[2]  B. Gao,et al.  Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data , 1995 .

[3]  C. Field,et al.  Crop yield gaps: their importance, magnitudes, and causes. , 2009 .

[4]  J. Famiglietti,et al.  Satellite-based estimates of groundwater depletion in India , 2009, Nature.

[5]  D. Toll,et al.  Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data , 2010 .

[6]  Liangzhi You,et al.  Generating Global Crop Distribution Maps: From Census to Grid , 2014 .

[7]  N. Ramankutty,et al.  Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000 , 2008 .

[8]  R. Lunetta,et al.  Remote sensing and Geographic Information System data integration: error sources and research issues , 1991 .

[9]  P. Jones,et al.  Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .

[10]  M. G. Chandrakanth,et al.  Groundwater depletion in India: institutional management regimes , 1990 .

[11]  Brian G. Wolff,et al.  Forecasting Agriculturally Driven Global Environmental Change , 2001, Science.

[12]  Steve Frolking,et al.  New district-level maps of rice cropping in India: a foundation for scientific input into policy assessment , 2006 .

[13]  Z. Wan New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .

[14]  Navin Ramankutty,et al.  Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world? , 2010 .

[15]  Roderick A. Scofield,et al.  Satellite-Based Estimates Of Heavy Precipitation , 1984, Other Conferences.

[16]  Yang Yang,et al.  Remote Sensing of Irrigated Agriculture: Opportunities and Challenges , 2010, Remote. Sens..

[17]  R. Reedy,et al.  Groundwater depletion and sustainability of irrigation in the US High Plains and Central Valley , 2012, Proceedings of the National Academy of Sciences.

[18]  R. B. Erb,et al.  The Use of LANDSAT Data in a Large Area Crop Inventory Experiment (LACIE) , 1975 .

[19]  Md Shahriar Pervez,et al.  Mapping Irrigated Lands at 250-m Scale by Merging MODIS Data and National Agricultural Statistics , 2010, Remote. Sens..

[20]  Guenther Fischer,et al.  Global Agro-ecological Assessment for Agriculture in the 21st Century , 2002 .

[21]  David B. Lobell,et al.  Irrigation cooling effect on temperature and heat index extremes , 2008 .

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

[23]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[24]  Prasad S. Thenkabail,et al.  Mapping rice areas of South Asia using MODIS multitemporal data , 2011 .

[25]  C. Rosenzweig,et al.  Potential impact of climate change on world food supply , 1994, Nature.

[26]  Damien Sulla-Menashe,et al.  A global land-cover validation data set, part I: fundamental design principles , 2012 .

[27]  François Anctil,et al.  Thermal‐water stress index from satellite images , 2006 .

[28]  J. Maclean,et al.  Rice Almanac: source book for the most important economic activity on earth. , 2002 .

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

[30]  Jacinto F. Fabiosa,et al.  Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change , 2008, Science.

[31]  Marijn van der Velde,et al.  A European irrigation map for spatially distributed agricultural modelling , 2009 .

[32]  J. Nørskov,et al.  Farming and the Fate of Wild Nature , 2009 .

[33]  Hong Yang,et al.  Spatially explicit assessment of global consumptive water uses in cropland: Green and blue water , 2010 .

[34]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[35]  R. Huke Rice area by type of culture, South, Southeast, and East Asia , 1982 .

[36]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[37]  M. Velde,et al.  Estimating irrigation water requirements in Europe , 2009 .

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

[39]  C. W. Thornthwaite An approach toward a rational classification of climate. , 1948 .

[40]  J. Norman,et al.  The global distribution of cultivable lands: current patterns and sensitivity to possible climate change , 2002 .

[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]  Alexandre Bouvet,et al.  Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[43]  A. Bouwman,et al.  Mapping contemporary global cropland and grassland distributions on a 5 × 5 minute resolution , 2007 .

[44]  C. Willmott,et al.  A More Rational Climatic Moisture Index , 1992 .

[45]  M. Ek,et al.  Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water , 2011 .

[46]  J. Qi,et al.  Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization , 2009 .

[47]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

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

[49]  P. Döll,et al.  MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling , 2010 .

[50]  Xiangming Xiao,et al.  Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005 , 2011 .

[51]  Donald N. Duvick,et al.  Feeding the Ten Billion: Plants and Population Growth , 1999 .

[52]  N. Ramankutty,et al.  Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000 , 2008 .

[53]  D. Landgrebe,et al.  Results of the 1971 Corn Blight Watch experiment , 1972 .

[54]  Mutlu Ozdogan,et al.  A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: An application example in the continental US , 2008 .

[55]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[56]  Alan H. Strahler,et al.  Validation of the global land cover 2000 map , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[57]  Mark A. Friedl,et al.  Using prior probabilities in decision-tree classification of remotely sensed data , 2002 .

[58]  Edmar I. Teixeira,et al.  Global Agro-Ecological Zones (GAEZ v3.0) , 2012 .

[59]  Christelle Vancutsem,et al.  GlobCover: ESA service for global land cover from MERIS , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[60]  M. D. A. Rounsevella,et al.  Future scenarios of European agricultural land use II . Projecting changes in cropland and grassland , 2005 .

[61]  Elisabetta Carfagna,et al.  Action Plan of the Global Strategy to Improve Agricultural and Rural Statistics , 2012 .

[62]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[63]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[64]  James E. Hook,et al.  ASSESSING AGRICULTURAL GROUNDWATER NEEDS FOR THE FUTURE: IDENTIFYING IRRIGATED AREA AND SOURCES , 2009 .

[65]  Changsheng Li,et al.  Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China , 2002 .

[66]  S. Carpenter,et al.  Solutions for a cultivated planet , 2011, Nature.

[67]  J. Thompson,et al.  Irrigated Agriculture and Wildlife Conservation: Conflict on a Global Scale , 2000, Environmental management.

[68]  T. Carter,et al.  Future scenarios of European agricultural land use: II. Projecting changes in cropland and grassland , 2005 .