Add comprehensive model card and pipeline tag

This PR adds a comprehensive model card for the GreenHyperSpectra project, including:
- A link to the paper: [GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction](https://huggingface.co/papers/2507.06806)
- A brief description from the abstract.
- A link to the associated code and data repository, inferred from the paper's abstract.
- The `pipeline_tag: image-feature-extraction`, which will make the model discoverable on the Hub at `https://huggingface.co/models?pipeline_tag=image-feature-extraction`.

README.md

@@ -1,3 +1,12 @@
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-license: mit
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+---
+license: mit
+pipeline_tag: image-feature-extraction
+---
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+# GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction
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+This repository contains models and data associated with the paper **[GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction](https://huggingface.co/papers/2507.06806)**.
+
+GreenHyperSpectra introduces a pretraining dataset of real-world cross-sensor and cross-ecosystem hyperspectral samples. This dataset is designed to benchmark trait prediction using semi- and self-supervised methods. The work demonstrates how leveraging GreenHyperSpectra can lead to label-efficient multi-output regression models that outperform state-of-the-art supervised baselines, significantly improving the learning of spectral representations for plant trait prediction.
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+All code and data for this project are available at the official GitHub repository: [https://github.com/GreenHyperSpectra/GreenHyperSpectra](https://github.com/GreenHyperSpectra/GreenHyperSpectra)