Articles | Volume 18, issue 5
https://doi.org/10.5194/essd-18-3069-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-3069-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
08 May 2026
Data description article |
| 08 May 2026
| 08 May 2026
CropSight-US: an object-based crop type ground truth dataset using street view and Sentinel-2 satellite imagery across the contiguous United States, 2013–2023
Zhijie Zhou
Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Chunyuan Diao
CORRESPONDING AUTHOR
Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
Related authors
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Yin Liu and Chunyuan Diao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-526, https://doi.org/10.5194/essd-2025-526, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
We developed CropPlantHarvest, the first long-term dataset of annual corn and soybean planting and harvesting dates across the U.S. Midwest at 500 m resolution for 2001–2024. Planting dates are estimated using the remotely sensed crop modeling system CropSow. Harvesting dates are retrieved using the novel Normalized Harvest Phenology Index (NHPI) to capture spectral transitions from senescent crops to crop residues. CropPlantHarvest is available at https://doi.org/10.5281/zenodo.16967482.
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Short summary
We developed an object-based crop type ground truth dataset CropSight-US across the Contiguous United States for 2013–2023. Using satellite and street view imagery, we created an operational way to identify crop types and field boundaries without in-person surveys. This novel dataset provides reliable crop type ground truth with delineated field boundaries that can support large-scale crop monitoring and decision-making. The dataset can be accessed via: https://doi.org/10.5281/zenodo.15702414.
We developed an object-based crop type ground truth dataset CropSight-US across the Contiguous...
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