Update README.md

README.md

@@ -25,6 +25,8 @@ tags:
 - remote-sensing
 - vegetation
 - multi-regression
+size_categories:
+- 100K<n<1M
 ---
 # 🌱 GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction 🌱
 
@@ -95,5 +97,32 @@ display(df.head())
 > 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:
+
+```bibtex
+@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:
+
+```bibtex
+@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
 ---