From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
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Abstract (arXiv)
Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in particular, offers the opportunity to monitor agricultural land at a continental scale and in a timely manner. However, so far the potential of S1 has not been explored at such a scale. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, we present the first continental crop type map at 10-m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018. Random forest classification algorithms are tuned to detect 19 different crop types. We assess the accuracy of this EU crop map with three approaches. First, the accuracy is assessed with independent LUCAS core in-situ observations over the continent. Second, an accuracy assessment is done specifically for main crop types from farmers declarations from 6 EU member countries or regions totaling >3M parcels and 8.21 Mha. Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat. The overall accuracy for the map is reported as 80.3% when grouping main crop classes and 76% when considering all 19 crop type classes separately. Highest accuracies are obtained for rape and turnip rape with user and produced accuracies higher than 96%. The correlation between the remotely sensed estimated and Eurostat reported crop area ranges from 0.93 (potatoes) to 0.99 (rape and turnip rape). Finally, we discuss how the framework presented here can underpin the operational delivery of in-season high-resolution based crop mapping.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 0 of its 75 references that are in the map. First posted on arXiv 19 May 2021.
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Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 3; 2026: 3.
10 papers.
- 2023Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Datacited by 8
- 2022Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distributioncited by 4
- 2022Towards Global Crop Maps with Transfer Learningcited by 1
- 2025A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Featurescited by 1
- 2022Vegetation Mapping by UAV Visible Imagery and Machine Learningcited by 0
- 2025Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mappingcited by 0
- 2025LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labelscited by 0
- 2026Fields of the Planet: Field Boundary Mapping Beyond 10mcited by 0
- 2026Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modalitycited by 0
- 2026SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoringcited by 0
- 2026SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring11 Aug 2026
- 2026Multi-Year Geospatial Reasoning using Interannually-Consistent Historical Predictions as a Free Input Modality6 Aug 2026
- 2026Fields of the Planet: Field Boundary Mapping Beyond 10m5 Jul 2026
- 2025LC-SLab -- An object-based deep learning framework for large-scale land cover classification from satellite imagery and sparse in-situ labels19 Sep 2025
- 2025Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping16 Apr 2025
- 2025A Deep Learning Architecture for Land Cover Mapping Using Spatio-Temporal Sentinel-1 Features10 Mar 2025
- 2023Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data19 May 2023
- 2022Towards Global Crop Maps with Transfer Learning9 Nov 2022
- 2022Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution23 Aug 2022
- 2022Vegetation Mapping by UAV Visible Imagery and Machine Learning23 May 2022