A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019

Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549, Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Pérez Guzmán, K. ORCID: https://orcid.org/0000-0001-5189-6570, Laso Bayas, J.C. ORCID: https://orcid.org/0000-0003-2844-3842, Shchepashchenko, M., Georgieva, I. ORCID: https://orcid.org/0000-0002-5556-794X, et al. (2024). A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019. Environmental Research Letters 19 (8) e084025. 10.1088/1748-9326/ad6a71.

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Abstract

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency's WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

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