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 Global Change Biology (2015Biology ( ) 21, 1980Biology ( -1992Biology ( , doi: 10.1111 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.

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

Highlights of the Paper 1. Geo-Wiki is a tool for visualisation, crowdsourcing and validation of information on global land cover.2. Recent enhancements to the tool include a Geo-Wiki for teaching, mobile phone apps and gamfication.3. Crowdsourced data from Geo-Wiki have been used to create new maps of agricultural field size, wilderness, cropland and land cover. AbstractInformation about land cover and land use is needed for a wide range of applications such as nature protection and biodiversity, forest and water management, urban and transport planning, natural hazard prevention and mitigation, monitoring of agricultural policies and economic land use modelling. A number of different remotely-sensed global land cover products are available but studies have shown that there are large spatial discrepancies between these different products when compared. To address this issue of land cover uncertainty, a tool called Geo-Wiki was developed, which integrates online and mobile applications, high resolution satellite imagery available from Google Earth, and data collection through crowdsourcing as a mechanism for validating and improving globally relevant spatial information on land cover and land use. Through its growing network of volunteers and a number of successful data collection campaigns, almost 5 million samples of land cover and land use have been collected at many locations around the globe. This paper provides an overview of the main features of Geo-Wiki, and then using a series of examples, illustrates how the crowdsourced data collected through Geo-Wiki have been used to improve information on land cover and land use.

13The production of global land cover products has accelerated significantly over the past decade thanks 14 to the availability of higher spatial and temporal resolution satellite data and increased computation 15 capabilities. The quality of these products should be assessed according to internationally promoted 16 requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and 17 Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 18 validation). Providing updated accuracies for the yearly maps would require considerable effort for 19 collecting validation datasets. To save time and effort on data collection, validation datasets should be 20 designed to suit multiple map assessments and should be easily adjustable for a timely validation of new 21 releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-22 purpose assessments and its applicability is demonstrated in three different assessments focusing on 23 validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. 24 The validation dataset is generated primarily to validate the newly released 100m spatial resolution land 25 cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset 26 includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 27 100m×100m. Within site, reference land cover information was collected at 100 subpixels of 10m×10m 28 allowing the land cover information to be suitable for different resolution and legends. Firstly, using this 29 dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. 30The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6+/-2.1% (at 95% 31 confidence level) for the African continent. Fraction cover products were found to have mean absolute 32 errors of 9. 3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. 33 Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from 34 four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). 35 Overall accuracies for these perspectives vary between 73.7% +/-2.1% and 93.5% ±0.9%, depending on 36 the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the 37 Globeland30-2010 map at 30m spatial resolution. Using the subpixel level validation data, we derived 38 15252 sample pixels at 30m spatial resolution. Based on these sample pixels, the overall accuracy of the 39 Globeland30-2010 map was found to be 66.6 ±2.4% for Africa. The three assessments exemplify the 40 applicability of multi-purpose validation datasets which are recommended to increase map validation 41 efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or 42 updating the dataset with sample sites in identi...