🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱

GreenHySpectra is a collection of hyperspectral reflectance data of vegetation from different sources. It is intended for Regression machine learning task for plant trait prediction with self and semi-supervised learning.

Spatial coverage

📁 Configurations

1. GreenHyperSpectra: Unlabeled set

• Files: all CSVs under unlabeled/
• Contains:
  • Sample ID
  • Spectral bands (400-2450 nm) >> 1721 bands

Column	Description
400	Reflectance at 400nm
...	More spectral bands
2450	Reflectance at 2450nm

Check the data with Hugging Face datasets library

from datasets import load_dataset

### GreenHyperSpectra: unlabeled ###
ds_un = load_dataset("Avatarr05/GreenHyperSpectra", "unlabeled")
GreenHyperSpectra = ds_un['train'].to_pandas().drop(['Unnamed: 0'], axis=1)

display(GreenHyperSpectra.head())

---

2. Labeled set

• File: labeled/all.csv
• Contains:
  • Sample ID
  • Dataset ID
  • Spectral bands (400-2450 nm) >> 1721 bands
  • Trait measurements (e.g., leaf chlorophyll, nitrogen content etc.)

Column	Description
dataset	Reference to the source of the dataset
400	Reflectance at 400nm
...	More spectral bands
2450	Reflectance at 2450nm
Cp	Nitrogen content (g/cm²)
Cm	Leaf mass per area (g/cm²)
Cw	Leaf water content (cm)
LAI	Leaf area index (m²/m²)
Cab	Leaf chrolophyll content (µg/m²)
Car	Leaf carotenoids content (µg/m²)
Anth	Leaf anthocynins content (µg/m²)
Cbc	Carbon-based constituents (g/cm²)

Check the data with Hugging Face datasets library

from datasets import load_dataset

### Labeled data: labeled_all ###
ds = load_dataset("Avatarr05/GreenHyperSpectra", "labeled_all")
df = ds['train'].to_pandas().drop(['Unnamed: 0'], axis=1)

display(df.head())

---

3. Split labeled set

• Files: all CSVs under labeled_splits/ These files follow the same format as the previous set but are pre-split for machine learning purposes. The split is stratified based on the dataset ID, with 20% of the data reserved for testing.

Check the data with Hugging Face datasets library

from datasets import load_dataset

### Labeled splits: labeled_splits ###
annotated_ds_train = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="train")
annotated_ds_train = annotated_ds_train['train'].to_pandas().drop(['Unnamed: 0'], axis=1)

annotated_ds_test = load_dataset("Avatarr05/GreenHyperSpectra", 'labeled_splits', split="test")
annotated_ds_test = annotated_ds_test['train'].to_pandas().drop(['Unnamed: 0'], axis=1)

display(annotated_ds_train.head())
display(annotated_ds_test.head())

⚠️ Note: Due to the high dimensionality of spectral datasets—often containing hundreds or thousands of columns—Hugging Face Data Studio may not render these files properly. This is a known limitation, as the Studio interface is not optimized for wide tabular data.

To work effectively with this dataset, we recommend using the Hugging Face datasets library or the MLCroissant Python library for programmatic access and exploration.

Citation

If you use the GreenHyperSpectra dataset, please cite the following paper:

@article{cherif2025greenhyperspectra,
  title={GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction},
  author={Cherif, Eya and Ouaknine, Arthur and Brown, Luke A and Dao, Phuong D and Kovach, Kyle R and Lu, Bing and Mederer, Daniel and Feilhauer, Hannes and Kattenborn, Teja and Rolnick, David},
  journal={arXiv preprint arXiv:2507.06806},
  year={2025}
}

If you use the labeled data included in this repository, please also cite the following study for more details about the compiled datasets:

@article{cherif2023spectra,
  title={From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data},
  author={Cherif, Eya and Feilhauer, Hannes and Berger, Katja and Dao, Phuong D and Ewald, Michael and Hank, Tobias B and He, Yuhong and Kovach, Kyle R and Lu, Bing and Townsend, Philip A and others},
  journal={Remote Sensing of Environment},
  volume={292},
  pages={113580},
  year={2023},
  publisher={Elsevier}
}

license: cc-by-nc-4.0