examples/aws-earth-search.ipynb

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    "Intake-STAC + sat-search\n",
    "================\n",
    "\n",
    "Commonly, we'll use an API to search a large STAC catalog to narrow down the data we want to work with. Here we'll demonstrate using the [sat-search](https://github.com/sat-utils/sat-search) library that uses https://www.element84.com/earth-search/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import intake\n",
    "import satsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox = [35.48, -3.24, 35.58, -3.14] # (min lon, min lat, max lon, max lat)\n",
    "dates = '2020-07-01/2020-08-15'\n",
    "\n",
    "URL='https://earth-search.aws.element84.com/v0'\n",
    "results = satsearch.Search.search(url=URL,\n",
    "                                  collections=['sentinel-s2-l2a-cogs'], # note collection='sentinel-s2-l2a-cogs' doesn't work\n",
    "                                  datetime=dates,\n",
    "                                  bbox=bbox,    \n",
    "                                  sort=['<datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('%s items' % results.found())\n",
    "items = results.items()\n",
    "# Save this locally for use later\n",
    "items.save('my-s2-l2a-cogs.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog = intake.open_stac_item_collection(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list(catalog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item = catalog['S2A_36MYB_20200814_0_L2A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assets for STAC Item\n",
    "list(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image\n",
    "Image(item['thumbnail'].urlpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# full STAC JSON accessible as item._stac_obj._data\n",
    "item.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da = item.B04(chunks=dict(band=1, y=2048, x=2048)).to_dask()\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stack_bands() method should be identical to landsat\n",
    "bands = ['nir','red']\n",
    "stack = item.stack_bands(bands)\n",
    "type(stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bug? currently need to specify chunks:\n",
    "da = stack(chunks=dict(band=1, x=2048, y=2048)).to_dask()\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorganize into xarray DataSet with common band names\n",
    "da['band'] = bands\n",
    "ds = da.to_dataset(dim='band')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract and plot NDVI subset\n",
    "NDVI = (ds['nir'] - ds['red']) / (ds['nir'] + ds['red'])\n",
    "#subset = NDVI.sel(y=slice(9.63e6,9.64e6), x=slice(7.55e5,7.71e5))\n",
    "subset = NDVI.isel(y=slice(6000,7000), x=slice(4500,5500))\n",
    "subset.plot.imshow(cmap='BrBG', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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