Global Field Sizes Dataset for Ecosystems Modeling

2018

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 dataset 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

2016

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. data integration objective • observation design • database creation objective • citizen science design Measurement Type(s) land cover Technology Type(s) image analysis Factor Type(s) Sample Characteristic(s) Earth • planetary surface

Mapping the changing characteristics of Africa's smallholder-dominated agricultural systems, including the sizes and numbers of fields, is crucial for understanding food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because of 1) the spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) the lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, which we used to map Ghana's annual croplands for the year 2018. To overcome the first problem, we converted daily, high resolution CubeSat (PlanetScope) imagery into two cloud-free seasonal composites covering a single agricultural year. To address the second problem, we created a labelling platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. Minimizing label errors improved model F1 scores by up to 25%. Active learning increased F1 scores by an average of 9.1% between first and last training iterations, and 2.3% more than models trained with randomly selected labels. We used the resulting 3.7 m map of cropland probabilities within a segmentation algorithm to delineate crop field boundaries. Based on an independent map reference sample (n=1,207), the cropland probability and field boundary maps have respective overall accuracies of 88% and 86.7%, user's accuracies for the cropland class of 61.2% and 78.9%, and producer's accuracies of 67.3% and 58.2%. Using the map reference sample to calculate an unbiased area estimate from the field boundary map, we found that cropland covers 17.1% (15.4-18.9%) of Ghana. Using the most accurately digitized labels to calculate and correct for biases in the segmented field boundaries map, we further estimated the average size (1.73 ha) and total number (1,662,281) of crop fields in Ghana. Our results demonstrate an adaptable and transferrable approach for mapping the characteristics of croplands on an annual basis and over national extents, with several features that effectively mitigate the errors inherent in remote sensing of smallholder-dominated agriculture.

2010

After reviewing the current state of the art in spatially explicit, global data base infrastructure for the analysis of agriculture, land use and the environment, we propose new infrastructure to support researchers working in this area. The proposed effort would do three things: 1) gather national and sub national statistics from various statistical agencies around the world to put together a consistent global data set, along with regional companion data sets, on agriculture and land use; 2) employ spatial disaggregation methods, including the use of satellite remote sensing technology and spatial statistics to develop geographically-explicit gridded data on a global scale; and 3) develop a data portal, including new tools for providing data in a variety of convenient formats to the global research community. Distinguishing features of this effort will be its focus on transparency, documentation and peer-review, quality control and sustainability over time. The majority of such previous efforts have only focused on the data portal elementthey rely on other researchers to provide them the data. By integrating data collection, development, and provision into one project, we will be able to support the global research community as it seeks to understand the long-run sustainability of the global agricultural system.

Global Biogeochemical Cycles, 2008

Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University's MODIS-derived land cover product and the GLC2000 data set). Our data are presented at 5 min (~10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2-17.1) million km2 of cropland (12% of the Earth's ice-free land surface) and 28.0 (90% confidence range of 23.6-30.0) million km2 of pasture (22%) in the year 2000.

Remote Sensing, 2017

Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. Cropland class areas are compared for the following datasets: FAO-GLCshare (FAO Global Land Cover Network), Geowiki IIASA-Hybrid (Hybrid global land cover map from the International Institute of Applied System Analysis), GLC2000 (Global Land Cover 2000), GLCNMO2008 (Global Land Cover by National Mapping Organizations), GlobCover, Globeland30, LC-CCI (Land Cover Climate Change Initiative) 2010 and 2015, and MODISLC (MODIS Land Cover product). The methodology involves: (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010).

Global Change Biology, 2015

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.

Earth System Science Data

Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns; thus they are less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010-the latest global spatially explicit datasets on agricultural production circa 2010-and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling, which mainly include the update of base year, the extension of crop list, and the expansion of subnational administrative-unit coverage. Specifically, it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data but also expands the estimates of crop area, yield, and production from 20 to 42 major crops under four farming systems across a global 5 arcmin grid. All the SPAM maps are freely available at the MapSPAM website (http://mapspam.info/, last access: 11 December 2020), which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedback from users but is also a platform providing archived global agricultural-production maps for better targeting the Sustainable Development Goals. In particular, SPAM2010 can be downloaded via an open-data repository (DOI:

The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016-2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28 × 10 6 km 2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the Published by Copernicus Publications. 4800 M. Zhang et al.: GCI30 first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

2018

Accurate, consistent and timely cropland information over large areas is critical to solve food security issues. To predict and respond to food insecurity, global cropland products are readily available from coarse and medium spatial resolution earth observation data. However, while the use of satellite imagery has great potential to identify cropland areas and their specific types, the full potential of this imagery has yet to be realized due to variability of croplands in different regions. Despite recent calls for statistically robust and transparent accuracy assessment, more attention regarding the accuracy assessment of large area cropland maps is still needed. To conduct a valid assessment of cropland maps, different strategies, issues and constraints need to be addressed depending upon various conditions present in each continent. This study specifically focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to t...

Scientific Data, 2017

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. data integration objective • observation design • database creation objective • citizen science design Measurement Type(s) land cover Technology Type(s) image analysis Factor Type(s) Sample Characteristic(s) Earth • planetary surface

Remote Sensing

Monitoring global agriculture systems relies on accurate and timely cropland information acquired worldwide. Recently, the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program has produced Global Food Security-support Analysis Data (GFSAD) cropland extent maps at three different spatial resolutions, i.e., GFSAD1km, GFSAD250m, and GFSAD30m. An accuracy assessment and comparison of these three GFSAD cropland extent maps was performed to establish their quality and reliability for monitoring croplands both at global and regional scales. Large area (i.e., global) assessment of GFSAD cropland extent maps was performed by dividing the entire world into regions using a stratification approach and collecting a reference dataset using a simple random sampling design. All three global cropland extent maps were assessed using a total reference dataset of 28,733 samples. The assessment results showed an overall accuracy of 72.3%, 80–98%, and 91.7% for GFSAD1...

Global Biogeochemical Cycles, 2008

Croplands cover ~15 million km 2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land-cover datasets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land-use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land-use datasets created by combining national-, state-, and county-level census statistics with a recently updated global dataset of croplands on a 5 minute by 5 minute (~10 km by 10 km) latitude-longitude grid. The resulting land-use datasets depict circa the year 2000 the area (harvested) and yield of 175 distinct crops of the world

Open-File Report

2001

In recent years the ability to collect spatial information from volunteers has greatly expanded through the combination of Google Earth, geo-tagged photos and the Internet. A Geo-Wiki has been created to aid in both the validation of existing spatial information and the collection of new information through the powerful resource of crowdsourcing. A case study of a land cover validation Geo-Wiki is described, in which the tool is used to validate existing global land cover products. The potential of such a tool for other applications is also recognized.