Crop type maps for operational global agricultural monitoring

Abstract

Ensuring food security is one of the major challenges we face in this century, especially in the face of a changing climate and a growing global population. With a rapidly growing demand for food, increasing conflicts, a highly interconnected global market, and increasingly extreme weather events, timely and accurate projections and estimates of global crop production are more important than ever1. Such estimates are a key component for well-functioning agricultural commodity markets and early warning and mitigation systems. One of the key international activities in support of transparent agricultural markets is the Group on Earth Observations Global Agriculture Monitoring (GEOGLAM) Crop Monitor for the G20 Agricultural Market Information System (AMIS) which provides a public good of open, timely, science-driven information on global crop conditions2. T he AMIS and GEOGLAM initiatives were launched by the G20 Ministers of Agriculture following the food price crises in 2007/08 and 20103. While the GEOGLAM initiative is focused on enhancing crop monitoring capabilities, in support of policies, investments, and decisions in food security and agricultural markets using satellite and in situ Earth observations (EO), AMIS provides an inter-agency platform of economists and policymakers who work together to enhance food market transparency and policy response for food security. Bringing together the principal trading countries of agricultural commodities, AMIS assesses global food supplies (focusing on wheat, maize, rice, and soybeans) and provides a platform to coordinate policy action in times of market uncertainty. In support of these activities, AMIS requested that GEOGLAM develop monthly crop condition assessments likely to impact production for these four main commodity crops. Foundational in providing such information is the identification of where each crop of interest is growing. Together with crop calendars, crop type maps enable the extraction of crop specific signals from satellite data during the agricultural growing season that can track crop development through the season and forecast yields ahead of harvest4,5. Despite their high value for trade and food security assessments, within-season maps at a sufficiently granular resolution to enable field to global-scale analyses of crop condition and crop yield do not exist across all of the world?s agricultural areas6,7. While for years this dearth was owing at least in part to insufficient satellite data and limits on computational processing8,9, today the principal challenges are the lack of high-quality ground reference data for calibration and validation of crop classifications10,11. Nevertheless, a range of crop type map products derived from satellite imagery does exist at national and regional scales (e.g.12,13). In addition, at the global scale, there are products such as the IFPRI SPAM-201014, M3-Crops15, and MIRCA200016 that provide information on crop type distribution based on sub-national statistics and a spatial allocation model at the 10Km resolution. While these represent the current state of the art for global crop type distribution, they are based on spatial models and subnational statistics rather than the spectral signal of a crop and they are at a very coarse resolution (10 km) and are out of date (i.e. represent croplands circa 2010). In short, they may represent the national or subnational total land area of each crop, though the spatial location of crops may not be correct, which presents a critical issue for their application in masking for within-season crop monitoring. To meet the needs of the GEOGLAM Crop Monitor to accurately mask crop type with as up-to-date information as available, we developed a harmonized global set of crop specific maps for the four major grains (wheat, maize, rice, and soybeans) following an exhaustive identification and collection of the most recent, highest quality existing crop type maps at national and regional sources. Similar to the efforts by Fritz et al.17 and Waldner et al.18 that created a unified general cropland product based on existing cropland products, we designed a criteria system to assess the best data sets with regards to timeliness, accuracy, spatial resolution, and data source. The result is the first set of global crop type maps, at the 0.05 degree resolution, derived from satellite imagery, covering the major producer and export countries for the four main crops, referred to herein as the GEOGLAM Global Best Available Crop Specific Masks (GEOGLAM-BACS). These maps are used operationally within the GEOGLAM Crop Monitor in the creation of monthly global crop condition assessments and are updated on an annual basis as new crop type maps become available. The dataset is made publicly available with this publication on CropMonitor.org as well as on Zenodo and at the time of submission refers to version v.1.0.