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UID:pretalx-global-workshop-2026-WHG977@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T164500
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DESCRIPTION:Robert N Masolele1\, Katja Berger2\, Zoltan Szantoi3\, Camilo Z
 amora2\, Johannes Reiche1\n\n1 Wageningen University\, Wageningen\, The Ne
 therlands\; robert.masolele@wur.nl\n2 GFZ\, German GeoResearch Center Pots
 dam\, Germany\n3 Directorate of Earth Observation Programmes\, European Sp
 ace\nAgency (ESA)\, Frascati\, RM\, Italy\n\nCoffee cultivation underpins 
 agricultural economies worldwide\, supporting millions of livelihoods and 
 contributing significantly to global production [1]. At the same time\, co
 ffee is among the leading commodities associated with global deforestation
  risks linked to European Union (EU) consumption. However\, accurately map
 ping coffee farm locations remains challenging due to the heterogeneous la
 ndscapes in which coffee is grown\, including dense vegetation\, diverse l
 and cover types\, varying management practices\, and phenological stages [
 2]\, [3]\, [4]. Existing mapping efforts are largely limited to major prod
 ucers such as Brazil\, Vietnam\, Ethiopia\, and Colombia\, leaving substan
 tial gaps across other coffee-growing regions [5].\nTo address this\, we f
 irst present a global benchmarking framework for commodity crop mapping. W
 e evaluate a combination of Sentinel-1 and Sentinel-2 data\, alongside loc
 ational variables. Using a comprehensive reference dataset spanning >40 co
 ffee-producing countries\, we show that models integrating Sentinel-1 and 
 Sentinel-2 data with location encoding achieve the highest performance (F1
 -score: 89%)\, outperforming models without contextual information [4].\nB
 uilding on this\, we apply the best-performing deep learning framework to 
 generate the first high-resolution global map of coffee farm extent\, achi
 eving an F1-score of 86%. The integration of Sentinel-1 (radar) and Sentin
 el-2 (optical) data enables robust feature extraction across diverse condi
 tions\, while location encodings enhance geographic contextualization of c
 offee systems.\nThis work delivers a consistent\, high-resolution global c
 offee map\, supporting sustainable land management\, supply chain transpar
 ency\, and conservation in tropical regions. It directly aligns with the E
 U Deforestation Regulation (EUDR\, Regulation (EU) 2023/1115)\, which requ
 ires monitoring the deforestation footprint of seven key commodities\, inc
 luding coffee relative to the December 31\, 2020 cut-off date. The approac
 h is being operationalized within cloud-based platforms (e.g.\, Copernicus
  Data Space Ecosystem)\, facilitating access for policymakers\, certificat
 ion bodies\, and stakeholders.\n\n[1]	R. Grüter\, T. Trachsel\, P. Laube\
 , and I. Jaisli\, ‘Expected global suitability of coffee\, cashew and av
 ocado due to climate change’\, PLoS One\, vol. 17\, no. 1\, p. e0261976\
 , Jan. 2022\, doi: 10.1371/JOURNAL.PONE.0261976.\n[2]	D. A. Hunt et al.\, 
 ‘Review of Remote Sensing Methods to Map Coffee Production Systems’\, 
 Remote Sensing 2020\, Vol. 12\, Page 2041\, vol. 12\, no. 12\, p. 2041\, J
 un. 2020\, doi: 10.3390/RS12122041.\n[3]	G. Maskell\, A. Chemura\, H. Nguy
 en\, C. Gornott\, and P. Mondal\, ‘Integration of Sentinel optical and r
 adar data for mapping smallholder coffee production systems in Vietnam’\
 , Remote Sens. Environ.\, vol. 266\, Dec. 2021\, doi: 10.1016/j.rse.2021.1
 12709.\n[4]	R. N. Masolele et al.\, ‘Mapping the diversity of land uses 
 following deforestation across Africa’\, Sci. Rep.\, vol. 14\, p. 1681\,
  2024\, doi: 10.1038/s41598-024-52138-9.\n[5]	A. Escobar-López\, M. Á. C
 astillo-Santiago\, J. F. Mas\, J. L. Hernández-Stefanoni\, and J. O. Lóp
 ez-Martínez\, ‘Identification of coffee agroforestry systems using remo
 te sensing data: a review of methods and sensor data’\, Geocarto Int.\, 
 vol. 39\, no. 1\, p. 2297555\, 2024\, doi: 10.1080/10106049.2023.2297555\;
 WGROUP:STRING:PUBLICATION.
DTSTAMP:20260624T042124Z
LOCATION:Room 18
SUMMARY:High-Resolution Global Maps of Coffee Farms Extent - Robert Masolel
 e
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/WHG977/
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