<img src="https://github.com/compgeolab/xlandsat/raw/main/doc/_static/banner.jpg" alt="xlandsat">
<p align="center"><strong>Analyze Landsat remote sensing images using xarray</strong></p>
<p align="center">
<a href="https://www.compgeolab.org/xlandsat"><strong>Documentation</strong> (latest)</a> •
<a href="https://github.com/compgeolab/xlandsat/blob/main/CONTRIBUTING.md"><strong>Contributing</strong> (how you can help)</a> •
<a href="https://www.compgeolab.org"><strong>CompGeoLab</strong></a>
</p>
<p align="center">
<a href="https://pypi.python.org/pypi/xlandsat"><img src="http://img.shields.io/pypi/v/xlandsat.svg?style=flat-square" alt="Latest version on PyPI"></a>
<a href="https://github.com/conda-forge/xlandsat-feedstock"><img src="https://img.shields.io/conda/vn/conda-forge/xlandsat.svg?style=flat-square" alt="Latest version on conda-forge"></a>
<a href="https://codecov.io/gh/compgeolab/xlandsat"><img src="https://img.shields.io/codecov/c/github/compgeolab/xlandsat/main.svg?style=flat-square" alt="Test coverage status"></a>
<a href="https://pypi.python.org/pypi/xlandsat"><img src="https://img.shields.io/pypi/pyversions/xlandsat.svg?style=flat-square" alt="Compatible Python versions."></a>
<a href="https://doi.org/10.5281/zenodo.7395473"><img src="https://img.shields.io/badge/doi-10.5281%2Fzenodo.7395473-blue?style=flat-square" alt="DOI used for citations"></a>
</p>
## About
**xlandsat** is Python library for loading and analyzing Landsat scenes
downloaded from [USGS EarthExplorer](https://earthexplorer.usgs.gov) with
the power of [xarray](https://xarray.dev/).
We take care of reading the metadata from the `*_MTL.txt` files provided by
EarthExplorer and organizing the bands into a single `xarray.Dataset` data
structure for easier manipulation, processing, and visualization.
## Example
Here's a quick example of loading and plotting this
[Landsat 9 scene from the city of Manaus, Brazil](https://doi.org/10.6084/m9.figshare.24167235.v1),
which is where the Solimões (brown water) and Negro (black water) rivers merge
to form the Amazon river:
```python
import xlandsat as xls
import matplotlib.pyplot as plt
# Download a sample Landsat 9 scene in EarthExplorer format
path_to_scene_file = xls.datasets.fetch_manaus()
# Load the data from the file into an xarray.Dataset
scene = xls.load_scene(path_to_scene_file)
# Make an RGB composite as an xarray.DataArray
rgb = xls.composite(scene, rescale_to=[0.02, 0.2])
# Plot the composite using xarray's plotting machinery
rgb.plot.imshow()
# Annotate the plot with the rich metadata xlandsat adds to the scene
plt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}")
plt.axis("scaled")
plt.show()
```
<img src="https://raw.githubusercontent.com/compgeolab/xlandsat/main/doc/_static/readme-example.jpg" alt="RGB image showing the city on the left and the black waters of the Negro river merging with the brown waters of the Solimões river" width="100%">
## Project goals
* Loading single scenes in the EarthExplorer format.
* Provide some calculation, like composites, but leave most of the rest to the
user and xarray.
Our goal is **not** to provide a solution for large-scale data processing.
Instead, our target is smaller scale analysis done on individual computers.
* For cloud-based data processing, see the [Pangeo Project](https://pangeo.io).
* For other satellites and more powerful features, use [Satpy](https://github.com/pytroll/satpy).
## Project status
**xlandsat is ready for use but still changing.**
This means that we sometimes break backwards compatibility as we try to
improve the software based on user experience, new ideas, better design
decisions, etc. Please keep that in mind before you update xlandsat to a newer
version.
**We welcome feedback and ideas!** This is a great time to bring new ideas on
how we can improve the project.
Submit [issues on GitHub](https://github.com/compgeolab/xlandsat/issues).
## License
This is free software: you can redistribute it and/or modify it under the terms
of the **MIT License**. A copy of this license is provided in
[`LICENSE.txt`](https://github.com/compgeolab/xlandsat/blob/main/LICENSE.txt).
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"description": "<img src=\"https://github.com/compgeolab/xlandsat/raw/main/doc/_static/banner.jpg\" alt=\"xlandsat\">\n<p align=\"center\"><strong>Analyze Landsat remote sensing images using xarray</strong></p>\n<p align=\"center\">\n<a href=\"https://www.compgeolab.org/xlandsat\"><strong>Documentation</strong> (latest)</a> \u2022\n<a href=\"https://github.com/compgeolab/xlandsat/blob/main/CONTRIBUTING.md\"><strong>Contributing</strong> (how you can help)</a> \u2022\n<a href=\"https://www.compgeolab.org\"><strong>CompGeoLab</strong></a>\n</p>\n\n<p align=\"center\">\n<a href=\"https://pypi.python.org/pypi/xlandsat\"><img src=\"http://img.shields.io/pypi/v/xlandsat.svg?style=flat-square\" alt=\"Latest version on PyPI\"></a>\n<a href=\"https://github.com/conda-forge/xlandsat-feedstock\"><img src=\"https://img.shields.io/conda/vn/conda-forge/xlandsat.svg?style=flat-square\" alt=\"Latest version on conda-forge\"></a>\n<a href=\"https://codecov.io/gh/compgeolab/xlandsat\"><img src=\"https://img.shields.io/codecov/c/github/compgeolab/xlandsat/main.svg?style=flat-square\" alt=\"Test coverage status\"></a>\n<a href=\"https://pypi.python.org/pypi/xlandsat\"><img src=\"https://img.shields.io/pypi/pyversions/xlandsat.svg?style=flat-square\" alt=\"Compatible Python versions.\"></a>\n<a href=\"https://doi.org/10.5281/zenodo.7395473\"><img src=\"https://img.shields.io/badge/doi-10.5281%2Fzenodo.7395473-blue?style=flat-square\" alt=\"DOI used for citations\"></a>\n</p>\n\n## About\n\n**xlandsat** is Python library for loading and analyzing Landsat scenes\ndownloaded from [USGS EarthExplorer](https://earthexplorer.usgs.gov) with\nthe power of [xarray](https://xarray.dev/).\nWe take care of reading the metadata from the `*_MTL.txt` files provided by\nEarthExplorer and organizing the bands into a single `xarray.Dataset` data\nstructure for easier manipulation, processing, and visualization.\n\n## Example\n\nHere's a quick example of loading and plotting this\n[Landsat 9 scene from the city of Manaus, Brazil](https://doi.org/10.6084/m9.figshare.24167235.v1),\nwhich is where the Solim\u00f5es (brown water) and Negro (black water) rivers merge\nto form the Amazon river:\n\n```python\nimport xlandsat as xls\nimport matplotlib.pyplot as plt\n\n# Download a sample Landsat 9 scene in EarthExplorer format\npath_to_scene_file = xls.datasets.fetch_manaus()\n\n# Load the data from the file into an xarray.Dataset\nscene = xls.load_scene(path_to_scene_file)\n\n# Make an RGB composite as an xarray.DataArray\nrgb = xls.composite(scene, rescale_to=[0.02, 0.2])\n\n# Plot the composite using xarray's plotting machinery\nrgb.plot.imshow()\n\n# Annotate the plot with the rich metadata xlandsat adds to the scene\nplt.title(f\"{rgb.attrs['title']}\\n{rgb.attrs['long_name']}\")\nplt.axis(\"scaled\")\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/compgeolab/xlandsat/main/doc/_static/readme-example.jpg\" alt=\"RGB image showing the city on the left and the black waters of the Negro river merging with the brown waters of the Solim\u00f5es river\" width=\"100%\">\n\n## Project goals\n\n* Loading single scenes in the EarthExplorer format.\n* Provide some calculation, like composites, but leave most of the rest to the\n user and xarray.\n\nOur goal is **not** to provide a solution for large-scale data processing.\nInstead, our target is smaller scale analysis done on individual computers.\n\n* For cloud-based data processing, see the [Pangeo Project](https://pangeo.io).\n* For other satellites and more powerful features, use [Satpy](https://github.com/pytroll/satpy).\n\n## Project status\n\n**xlandsat is ready for use but still changing.**\nThis means that we sometimes break backwards compatibility as we try to\nimprove the software based on user experience, new ideas, better design\ndecisions, etc. Please keep that in mind before you update xlandsat to a newer\nversion.\n\n**We welcome feedback and ideas!** This is a great time to bring new ideas on\nhow we can improve the project.\nSubmit [issues on GitHub](https://github.com/compgeolab/xlandsat/issues).\n\n## License\n\nThis is free software: you can redistribute it and/or modify it under the terms\nof the **MIT License**. A copy of this license is provided in\n[`LICENSE.txt`](https://github.com/compgeolab/xlandsat/blob/main/LICENSE.txt).\n",
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