{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Planetary Computer: Sentinel-2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://colab.research.google.com/github/cloudsen12/easystac/blob/main/docs/tutorials/pc-sentinel-2.ipynb)\n",
"[](https://studiolab.sagemaker.aws/import/github/cloudsen12/easystac/blob/main/docs/tutorials/pc-sentinel-2.ipynb)\n",
"[](https://pccompute.westeurope.cloudapp.azure.com/compute/hub/user-redirect/git-pull?repo=https://github.com/cloudsen12/easystac&urlpath=lab/tree/easystac/docs/tutorials/pc-sentinel-2.ipynb&branch=main)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Tutorial created by **David Montero Loaiza**_: [GitHub](https://github.com/davemlz) | [Twitter](https://twitter.com/dmlmont)\n",
"\n",
"- GitHub Repo: [https://github.com/cloudsen12/easystac](https://github.com/cloudsen12/easystac)\n",
"- PyPI link: [https://pypi.org/project/easystac/](https://pypi.org/project/easystac/)\n",
"- Conda-forge: [https://anaconda.org/conda-forge/easystac](https://anaconda.org/conda-forge/easystac)\n",
"- Documentation: [https://easystac.readthedocs.io/](https://easystac.readthedocs.io/)\n",
"- More tutorials: [https://github.com/cloudsen12/easystac/tree/main/docs/tutorials](https://github.com/cloudsen12/easystac/tree/main/docs/tutorials)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Install `easystac`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -U easystac"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's start!\n",
"\n",
"First, import the `planetary` module from `easystac`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import easystac.planetary as pc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is not required to authenticate for having access to Planetary Computer, however, if you have a key, it is better to use it and avoid limits!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"To authorize access needed by \u001b[1mPlanetary Computer\u001b[0m, open the following URL: \u001b[1mhttp://planetarycomputer.microsoft.com/compute/hub/token\u001b[0m in your web browser. An API key is required to have a more favorable rate limiting in Planetary Computer , so please copy and paste it below.\n",
"\n",
"Successfully saved authorization token.\n"
]
}
],
"source": [
"pc.Authenticate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, in order to use the token, you have to intialize the module!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pc.Initialize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once intialized, you can start working!\n",
"\n",
"Planetary Computer needs every item to be signed, but you don't have to worry about that anymore! With `easystac`, querying through their STAC is easy and simple!\n",
"\n",
"The only thing you need is the collection ID `\"sentinel-2-l2a\"` and that's it:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"S2 = pc.ImageCollection(\"sentinel-2-l2a\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `ImageCollection` object will save all important stuff that you want to query, and then you just have to get the info from it. Let's filter this collection by date!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"S2 = S2.filterDate(\"2020-01-01\",\"2021-01-01\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's use a geometry to filter the collection by bounds!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from geojson import Point\n",
"\n",
"poi = Point([-76.1,3.4])\n",
"\n",
"S2 = S2.filterBounds(poi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! Now we just have to retrieve our request:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dmontero/.local/lib/python3.9/site-packages/stackstac/accumulate_metadata.py:168: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" np.array(\n",
"/home/dmontero/.local/lib/python3.9/site-packages/stackstac/accumulate_metadata.py:168: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" np.array(\n",
"/home/dmontero/.local/lib/python3.9/site-packages/stackstac/accumulate_metadata.py:168: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
" np.array(\n"
]
}
],
"source": [
"S2 = S2.getInfo(resolution = 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, your data is in a `xarray.DataArray`:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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