Mapping global cropland and field size

A new 1 km global IIASA‐IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5...

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Published inGlobal change biology Vol. 21; no. 5; pp. 1980 - 1992
Main Authors Fritz, Steffen, See, Linda, McCallum, Ian, You, Liangzhi, Bun, Andriy, Moltchanova, Elena, Duerauer, Martina, Albrecht, Fransizka, Schill, Christian, Perger, Christoph, Havlik, Petr, Mosnier, Aline, Thornton, Philip, Wood-Sichra, Ulrike, Herrero, Mario, Becker-Reshef, Inbal, Justice, Chris, Hansen, Matthew, Gong, Peng, Abdel Aziz, Sheta, Cipriani, Anna, Cumani, Renato, Cecchi, Giuliano, Conchedda, Giulia, Ferreira, Stefanus, Gomez, Adriana, Haffani, Myriam, Kayitakire, Francois, Malanding, Jaiteh, Mueller, Rick, Newby, Terence, Nonguierma, Andre, Olusegun, Adeaga, Ortner, Simone, Rajak, D. Ram, Rocha, Jansle, Schepaschenko, Dmitry, Schepaschenko, Maria, Terekhov, Alexey, Tiangwa, Alex, Vancutsem, Christelle, Vintrou, Elodie, Wenbin, Wu, van der Velde, Marijn, Dunwoody, Antonia, Kraxner, Florian, Obersteiner, Michael
Format Journal Article
LanguageEnglish
Published England Blackwell Publishing Ltd 01.05.2015
Subjects
agricultural intensity
Crop Production - statistics & numerical data
cropland
data fusion
field size
Geographic Information Systems - trends
Geographic Mapping
land cover
Satellite Imagery
synergy map
data fusion
cropland
agricultural intensity
field size
land cover
synergy map
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Summary:A new 1 km global IIASA‐IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo‐Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA‐IFPRI cropland product has been validated using very high‐resolution satellite imagery via Geo‐Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo‐Wiki. The first ever global field size map was produced at the same resolution as the IIASA‐IFPRI cropland map based on interpolation of field size data collected via a Geo‐Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.
Bibliography:Joint Research Centre of the European Union
ark:/67375/WNG-789Z9755-J
EU FP7 - No. 283080; No. 603719
Figure S1. The spatial distribution of validation points across South America and Africa. Green indicates validation points where agreement between multiple answers was >65% while red indicates agreement <65%. Only those validation points where agreement was greater than 65% were used in the paper to ensure a minimum quality in the validation data. Globally, 36% of the total validation points had an agreement higher than 65%. In South America 43% were used while in Africa 33% were used. The lower number used in Africa is due to the fact that there are smaller fields and more heterogeneous landscapes so the interpretation of satellite imagery is more difficult.Table S1. The 10 land cover classes in the Geo-Wiki simple legend. These were chosen to be consistent with the generalized land cover classes proposed by Herold et al. (2008), which allows for comparison of different land cover products.Appendix S1. Quantifying spatial variation in field size by region. Table S2. l2 statistics by region. Figure S2. Frequency distribution of field size by region and the respective estimate of l2. Regions are listed in Table S2.Appendix S2. Calculation of agreement. Table S3. Crowdsourced answers from two users for the same location.Appendix S3. The ranking procedure and production of the synergy map. Table S4. The ranking of products by country. Only those countries with more than 400 crowdsourced data points are included. Table S5. Synergy table for Russia where 4 input layers are available. Grey shading highlights those combinations with a tied ranking as the accuracy values for MODIS and GlobCover are the same in this example.Appendix S4. Comparison of change in cropland area from 2000 to 2005 compared with the size of the disagreement between the IIASA-IFPRI and EarthStat cropland products. Table S6. Countries ranked in descending order by cropland area in 2005 showing the difference in areas between 2005 and 2000 based on FAOSTAT, the area of disagreement between the IIASA-IFPRI and EarthStat cropland products and the latter two quantities in relation to one another, expressed as a percentage.
National Basic Research Program of China - No. 2010CB951502
istex:5A698A6AF39DFB5A31686FD54D3CF11AAA3C1F90
CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS)
ArticleID:GCB12838
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1354-1013
1365-2486
DOI:10.1111/gcb.12838