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# CropClimateX: A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the United States from 2018–2022
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Adrian Höhl, Stella Ofori-Ampofo, Miguel-Ángel Fernández-Torres, Rıdvan Salih Kuzu, and Xiao Xiang Zhu
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- **Repository:** [github.com/drnhhl/CropClimateX](https://github.com/drnhhl/CropClimateX)
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- **Paper:**
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- **License:** CC-BY-4.0
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- **Contact**: adrian.hoehl@tum.de
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The database includes 15,500 small data cubes (i.e., minicubes), each with a spatial coverage of 12x12km, spanning 1527 counties in the US. The minicubes comprise data from multiple sensors (Sentinel-2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and U.S. drought monitor maps), as well as soil and terrain features, making it suitable for various agricultural monitoring tasks. It integrates crop- and climate-related tasks within a single, cohesive dataset.
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In detail, the following data sources are provided:
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The following data providers were used to gather the data: Planetary Computer, Google Earth Engine, SentinelHub, and Copernicus Data Space Ecosystem. The APIs were accessed with [terragon](https://github.com/drnhhl/terragon).
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## Citation
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If you use this dataset, please consider citing:
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# CropClimateX: A large-scale, multitask, multisensory dataset for climate-aware crop monitoring in the United States from 2018–2022
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Adrian Höhl, Stella Ofori-Ampofo, Miguel-Ángel Fernández-Torres, Rıdvan Salih Kuzu, and Xiao Xiang Zhu
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- **Repository:** [github.com/drnhhl/CropClimateX](https://github.com/drnhhl/CropClimateX)
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- **Paper:** [https://doi.org/10.1038/s41597-026-06611-x](https://doi.org/10.1038/s41597-026-06611-x)
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- **License:** CC-BY-4.0
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- **Contact**: adrian.hoehl@tum.de
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The database includes 15,500 small data cubes (i.e., minicubes), each with a spatial coverage of 12x12km, spanning 1527 counties in the US. The minicubes comprise data from multiple sensors (Sentinel-1/2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and U.S. drought monitor maps), as well as soil and terrain features, making it suitable for various agricultural monitoring tasks. It integrates crop- and climate-related tasks within a single, cohesive dataset.
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In detail, the following data sources are provided:
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The following data providers were used to gather the data: Planetary Computer, Google Earth Engine, SentinelHub, and Copernicus Data Space Ecosystem. The APIs were accessed with [terragon](https://github.com/drnhhl/terragon).
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## Citation
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If you use this dataset, please consider citing:
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```
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@article{hohlLargescaleMultitask2026,
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title = {A Large-Scale, Multitask, Multisensory Dataset for Climate-Aware Crop Monitoring in the {{US}} from 2018--2022},
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author = {H{\"o}hl, Adrian and {Ofori-Ampofo}, Stella and {Fern{\'a}ndez-Torres}, Miguel-{\'A}ngel and Kuzu, R{\i}dvan Salih and Zhu, Xiao Xiang},
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year = 2026,
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month = jan,
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journal = {Scientific Data},
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issn = {2052-4463},
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doi = {10.1038/s41597-026-06611-x}
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}
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```
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