---
license: cc-by-nc-4.0
task_categories:
- image-to-image
tags:
- climate
pretty_name: HydroChronos
size_categories:
- 100K<n<1M
---

# HydroChronos
<!-- Provide a quick summary of the dataset. -->
HydroChronos is designed to forecast water dynamics using multispectral satellite images, climate variables, and DEM. 
It covers lakes and rivers of the US, Europe, and Brazil.

- **Repository:** [GitHub](https://github.com/DarthReca/hydro-chronos)
- **Paper:** [Arxiv](https://arxiv.org/abs/2506.14362)

## Dataset Details
<!-- Provide a longer summary of what this dataset is. -->
The dataset comprises Landsat-5 (L) TOA and Sentinel-2 (S) TOA images. There are 6 coherently aligned bands for both satellites:

| Landsat | Sentinel | Description | Central Wavelength (L/S) |
|:-------:|:--------:|:-----------:|:------------------------:|
|    B1   |    B2    |     Blue    |        485/492 nm        |
|    B2   |    B3    |    Green    |        560/560 nm        |
|    B3   |    B4    |     Red     |        660/665 nm        |
|    B4   |    B8    |     NIR     |        830/833 nm        |
|    B5   |    B11   |     SWIR    |       1650/1610 nm       |
|    B7   |    B12   |     SWIR    |       2220/2190 nm       |

They are coupled with climate variables from [TERRACLIMATE](https://www.nature.com/articles/sdata2017191) and Copernicus GLO30-DEM.
There is no ground truth. We directly work with Modified Normalized Difference Water Index (MNDWI)

- **Curated by:** Daniele Rege Cambrin
- **License:** Creative Commons Attribution Non Commercial 4.0

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
To load the dataset with TorchGeo, please refer to the [repository](https://github.com/DarthReca/hydro-chronos).

All climate data are contained in *climate.h5* with the following structure:
```bash
```
The data for the two satellites and DEM are contained in the respective folders. 
For portability, the whole dataset is divided into parts. You can easily iterate over the whole dataset using the *_main.h5* files, 
since they contain the [external links](https://docs.h5py.org/en/stable/high/group.html#external-links) to the correct file
```bash
```

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
```bibtex
@misc{cambrin2025hydrochronosforecastingdecadessurface,
      title={HydroChronos: Forecasting Decades of Surface Water Change}, 
      author={Daniele Rege Cambrin and Eleonora Poeta and Eliana Pastor and Isaac Corley and Tania Cerquitelli and Elena Baralis and Paolo Garza},
      year={2025},
      eprint={2506.14362},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.14362}, 
}
```