Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 18, issue 5
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.
https://doi.org/10.5194/essd-18-3069-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 5
Data description article
 | 
08 May 2026
Data description article |  | 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, Yin Liu, and Chunyuan Diao

Viewed

Total article views: 2,557 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,508 981 68 2,557 68 81
  • HTML: 1,508
  • PDF: 981
  • XML: 68
  • Total: 2,557
  • BibTeX: 68
  • EndNote: 81
Views and downloads (calculated since 03 Nov 2025)
Cumulative views and downloads (calculated since 03 Nov 2025)

Viewed (geographical distribution)

Total article views: 2,557 (including HTML, PDF, and XML) Thereof 2,410 with geography defined and 147 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 04 Aug 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint