Since then, Geo-Wiki has evolved into a tool for collecting land cover data at different resolutions (from 10 m to 1 km) via different campaigns and mobile/gaming applications.  During some of these campaigns, which are documented in See et al. (2015), we collected data on cropland and the size of agricultural fields (from very small to large). These data were subsequently used as training and validation data for the global cropland map as part of the hybrid data fusion approach described in the paper. 

3 Workshop on Characterizing and Validating Global Agricultural Landcover  

In 2011, we held a workshop at the International Institute for Applied Systems Analysis (IIASA) in 2011 on global agricultural land cover. The workshop was motivated by the fact  that there is not a single product that accurately characterizes global cropland and that many good national land cover products exist (which include cropland) but they are not shared. Thus, we agreed to fund the participation of people in the workshop if they shared their national products for inclusion in the global cropland product. The workshop resulted in the sharing of cropland products from 14 different countries (which included the major world crop producers) as well as the collection of various global and regional data sets, e.g. CORINE land cover and Africover from FAO. The data sets collected from the workshop became the inputs to the global cropland map reported in the paper.

What's Significant about the Paper

For us this paper represents a community-based product on global cropland for the year 2005, which brings together existing global, regional and national products and fuses them into a single, better product using crowdsourced data obtained from visual interpretation of Google Earth imagery. This product is like a convergence of evidence, using a data fusion approach to merge a range of information products on cropland into a single, better global representation. This was only possible because of the open sharing of cropland data sets, facilitated through the aforementioned workshop. Such a workshop-based approach could be a model for producing future hybrid land cover products or for the sharing of key data sets, e.g. in-situ or reference data, which currently sit on computers and laptops around the world. 
Another key point is that the product was calibrated using statistics from the United Nations Food and Agriculture Organization (FAO). Although there are definitely problems with FAO statistics, they represent the only source of agreed national statistics on agriculture. Hence this global product can be used in economic models that need to be aligned with national figures. This product is also used in bulletins produced by the Group on Earth Observation's Global Agricultural Monitoring system (GEOGLAM).
One thing that surprised is how much attention we got from the field size map. Although there have been some regional and national field size products produced in the past, this was the first global product. Field size is an interesting parameter since it can be used to look at spatial patterns of agricultural intensification, or in agricultural monitoring, it can determine what type and resolution of satellite sensor is needed for operational monitoring. The field size product was subsequently used in the research by Herrero et al. (2017) on the spatial distribution of nutrient production, which showed that the diversity of agricultural and nutrient production diminishes as farm size increases. Hence smallholder farming is a critical contributor to nutrient production. In September 2017, we held a new crowdsourcing campaign with Geo-Wiki focused on gathering a much larger data set on agricultural field sizes globally, which we will use to improve the current global field size map in the future.

References

 
Friedl, M.A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X. 2010. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168-182.  
Fritz, S., Bartholomé, E., Belward, A., Hartley, A., Stibig H.J., Eva, H., Mayaux, P., Bartalev, S., Latifovic, R., Kolmert, S., Roy, P., Agrawal, S., Bingfang, W., Wenting, X., Ledwith, M., Pekel, F.J., Giri, C., Mücher, S., de Badts, E., Tateishi, R., Champeaux, J-L., Defourny, P. 2003. Harmonisation, mosaicing and production of the Global Land Cover 2000 database (Beta Version), Luxembourg: Office for Official Publications of the European Communities, EUR 20849 EN, 41 pp., ISBN 92-894-6332-5*. 
   
Fritz, S., McCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F., Obersteiner, M.  2009. Geo-Wiki.Org: The use of crowd-sourcing to improve global land cover.  Rem. Sens., 1(3), 345-354.
Fritz, S., McCallum, I., Schill, C., Perger, C., See, L., Schepaschenko, D., van der Velde, M., Kraxner, F., Obersteiner, M. 2012. Geo-Wiki: An online platform for land cover validation and the improvement of global land cover. Environmental Modelling and Software, 31, 110-123.
Fritz, S., See, L., McCallum, I., Schill, C., Obersteiner, M., van der Velde, M., Boettcher, H., Havlik, P., Achard, F. 2011. Highlighting continued uncertainty in global land cover maps to the user community. Environmental Research Letters, 6, 044005.
Fritz, S. and See, L. 2005. Comparison of land cover maps using fuzzy agreement. International Journal of GIS, 19(7), 787-807.  
Fritz, S. and See, L.M. 2008. Quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications. Global Change Biology, 14, 1-23, doi: 10.1111/j.1365-2486.2007.01519.x
Herrero, M., Thornton, P.K., Power, B., Bogard, J.R., Remans, R., Fritz, S., Gerber, J.S., Nelson, G., See, L., Waha, K., Watson, R.A., West, P.C., Samberg, L.H., van de Steeg, J., Stephenson E., van Wijk, M., Havlík, P. 2017. Farming and the geography of nutrient production for human use: a transdisciplinary analysis. Lancet Planetary Health, Apr 1(1):e33-e42. doi: 10.1016/S2542-5196(17)30007-4.
See, L., Fritz, S., Perger, C., Schill, C., McCallum, I., Schepaschenko, D., Karner, M., Kraxner F., Obersteiner, M. 2015. Harnessing the power of volunteers, the Internet and Google Earth to collect and validate global spatial information using Geo-Wiki. Technological Forecasting and Social Change, 98, 324–335. doi:10.1016/j.techfore.2015.03.002