FROM-GC: 30 m global cropland extent derived through multisource data integration

We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps (i.e. FROM-GLC, Finer Resolution Observation and Monitoring, Global Land Cover; FROM-GLC-agg) and a 250-m cropland probability map. A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples. A decision tree was then applied to combine two 250-m cropland masks: one existing mask from the literature and the other produced in this study, with the 30-m global land cover map FROM-GLC-agg. For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical (FAOSTAT) database, a final global cropland extent map was composited from the FROM-GLC, FROM-GLC-agg, and two masked cropland layers. From this map FROM-GC (Global Cropland), we estimated the global cropland areas to be 1533.83 million hectares (Mha) in 2010, which is 6.95 Mha (0.45%) less than the area reported by the Food and Agriculture Organization (FAO) of the United Nations for the year 2010. A country-by-country comparison between the map and the FAOSTAT data showed a linear relationship (FROM-GC = 1.05*FAOSTAT −1.2 (Mha) with R2= 0.97). Africa, South America, Southeastern Asia, and Oceania are the regions with large discrepancies with the FAO survey.

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

[2]  Chenghai Yang,et al.  Original paper: Evaluating high resolution SPOT 5 satellite imagery for crop identification , 2011 .

[3]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

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

[5]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

[6]  B. Griscom,et al.  Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data , 2004 .

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

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

[9]  S. Fritz,et al.  Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .

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

[11]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[12]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[13]  T. L. Toan,et al.  Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta , 2011 .

[14]  Kees Klein Goldewijk,et al.  The HYDE 3.1 spatially explicit database of human‐induced global land‐use change over the past 12,000 years , 2011 .

[15]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[16]  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.

[17]  Berrien Moore,et al.  Uncertainties in estimates of cropland area in China: a comparison between an AVHRR-derived dataset and a Landsat TM-derived dataset , 2003 .

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

[19]  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 .

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

[21]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[22]  Prasad S. Thenkabail,et al.  A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches , 2010, Remote. Sens..

[23]  Heather McNairn,et al.  Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping , 2010 .

[24]  Sushma Panigrahy,et al.  Discrimination of rice crop grown under different cultural practices using temporal ERS-1 synthetic aperture radar data , 1997 .

[25]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[26]  Gianfranco De Grandi,et al.  Central African Forest Cover Revisited: A Multisatellite Analysis , 2000 .

[27]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[28]  Steffen Fritz,et al.  Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale , 2012, Remote. Sens..

[29]  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 .

[30]  W. Steffen,et al.  Human modification of global water vapor flows from the land surface. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Le Yu,et al.  Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach , 2013 .

[32]  Ryutaro Tateishi,et al.  Production of global land cover data – GLCNMO , 2011, Int. J. Digit. Earth.

[33]  Peng Gong,et al.  China needs no foreign help to feed itself , 2011, Nature.