Crop Type Maps for Operational Global Agricultural Monitoring

Abstract

Crop type maps identify the spatial distribution of crop types and underpin a large range of agricultural monitoring applications ranging from early warning of crop shortfalls, crop condition assessments, production forecasts, and damage assessment from extreme weather, to agricultural statistics, agricultural insurance, and climate mitigation and adaptation decisions. Despite their importance, harmonized, up-to-date global crop type maps of the main food commodities do not exist to date. To address this critical data gap of global-scale consistent, up-to-date crop type maps, we harmonized 24 national and regional datasets from 21 sources covering 66 countries to develop a set of Best Available Crop Specific masks (BACS) over the major production and export countries for wheat, maize, rice, and soybeans, in the context of the G20 Global Agriculture Monitoring Program, GEOGLAM.

CROPGRIDS: a global geo-referenced dataset of 173 crops

“… 30 4 GEOGLAM Global gridded f [%] for 4 crops at a resolution of 0.05 degree (~5.6 km at the equator) in 2020. 13 , 31 5 OIPA Global gridded w [−] for oil palm at a resolution of 0.0000898 degree (~0.01 km at the equator) in 2019. 32 6 RAP Global gridded w [−] for rapeseed at a resolution of 0.0000898 degree (~0.01 km at the equator) in 2019.…”

“… 6 for a comprehensive review). More recently, important initiatives such as those promoted by the European Space Agency (ESA) 7 , 8 , by the USA National Aeronautics and Space Administration (NASA) 9 , and by the G20 Ministers of Agriculture were launched and are already contributing considerable new information 10 – 13 . However, none of these efforts has matched the original MRF scope and crop coverage, so much so that many global assessments of agricultural impacts have continued to use MRF as a reference 14 – 17 .…”

“…CROPGRIDS was produced using a similar approach as in ref. 13 with MRF data used as starting point and updated through hybridisation of more recent information, by merging all available, published and gridded datasets for periods more recent than 2000 and using a set of endogenous and exogenous data quality indicators, within a multi-criteria ranking scheme, to determine best-fit data by crop type and country. For some crop types and countries where gridded data more recent than 2000 were not available, we spatialized recent subnational data obtained from National Statistical Offices (NSOs) following a similar algorithm used in MRF but with a new cropland agreement map circa 2020 18 as the cropland mask.…”

Satellite Observations of Water Origins Reveal New Dimensions of Hydroclimate Vulnerability of Global Croplands

“…To conduct analyses of the impacts of hydrological variability on crop production, we used the Best Available Crop Specific masks (BACS) of wheat, maize, rice, and soybean at the 0.05° resolution, in the context of the G20 Global Agricultural Monitoring Program (GEOGLAM) 55 . This product harmonizes 24 latest national and regional datasets with the highest quality from 21 resources covering 66 countries spanning from 2010-2019.…”

Untangling Intercropping in Heterogeneous Smallholder Maize-Cassava Farming Systems with Remote Sensing

“…Remote Sensing provides timely and cost-efficient input for agricultural monitoring across large regions for pre-and within-growing season decision support (Johnson, 2014). Most approaches currently applied for mapping crop types were developed for large-scale crop monocultures, especially in industrialized economies (Fritz et al, 2019;Taiwo et al, 2023;Becker-Reshef et al, 2023). However, these approaches have limited applicability in heterogeneous agricultural systems such as those dominating smallholder agriculture worldwide, especially in developing countries.…”