{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sandbox Metadata Document\n",
    "\n",
    "This document aims to provide an overview of the different data sets available in the Open Data Cube Sandbox. It will provide details on which products are available, what regions are indexed for each product, and the time series available within that extent.\n",
    "\n",
    "For information on how to get started with the Datacube, please see the read_me_first.ipynb available [here](https://sandbox.test.frontiersi.io/user/sandbox/notebooks/Read_me_first.ipynb).\n",
    "\n",
    "* [Datacube Information](#datacube)  \n",
    "    * [ALOS 2](#alos2)  \n",
    "    * [Landsat 5](#ls5)  \n",
    "    * [Landsat 7](#ls7)  \n",
    "    * [Landsat 8](#ls8)  \n",
    "    * [Sentinel 1](#s1)\n",
    "* [Product coverage illustration](#map)\n",
    "    \n",
    "* [System Information](#system)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"datacube\"></a>\n",
    "## Datacube Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>creation_time</th>\n",
       "      <th>lat</th>\n",
       "      <th>platform</th>\n",
       "      <th>lon</th>\n",
       "      <th>format</th>\n",
       "      <th>time</th>\n",
       "      <th>instrument</th>\n",
       "      <th>product_type</th>\n",
       "      <th>label</th>\n",
       "      <th>crs</th>\n",
       "      <th>resolution</th>\n",
       "      <th>tile_size</th>\n",
       "      <th>spatial_dimensions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>alos2_palsar_AMA_ingest</td>\n",
       "      <td>ALOS-2 PALSAR mosaic tiles generated for use i...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>ALOS_2</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTIFF</td>\n",
       "      <td>None</td>\n",
       "      <td>PALSAR</td>\n",
       "      <td>gamma0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ls5_level1_usgs</td>\n",
       "      <td>Landsat 5 USGS Level 1 Collection-1 OLI-TIRS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_5</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>TM</td>\n",
       "      <td>L1TP</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ls5_usgs_sr_scene</td>\n",
       "      <td>Landsat 5 USGS Collection 1 Level2 Surface Ref...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_5</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>TM</td>\n",
       "      <td>LEVEL2_USGS</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ls7_collection1_AMA_ingest</td>\n",
       "      <td>Landsat 7 USGS Collection 1 Higher Level SR sc...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_7</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>ETM</td>\n",
       "      <td>LEDAPS</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ls7_level1_usgs</td>\n",
       "      <td>Landsat 7 USGS Level 1 Collection-1 OLI-TIRS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_7</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>ETM</td>\n",
       "      <td>L1TP</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ls7_usgs_sr_scene</td>\n",
       "      <td>Landsat 7 USGS Collection 1 Level2 Surface Ref...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_7</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>ETM</td>\n",
       "      <td>LEVEL2_USGS</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ls8_collection1_AMA_ingest</td>\n",
       "      <td>Landsat 8 USGS Collection 1 Higher Level SR sc...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTIFF</td>\n",
       "      <td>None</td>\n",
       "      <td>OLI_TIRS</td>\n",
       "      <td>LaSRC</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ls8_l1_pc_usgs</td>\n",
       "      <td>Landsat 8 USGS Level 1 Pre-Collection OLI-TIRS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>OLI_TIRS</td>\n",
       "      <td>L1T</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ls8_level1_usgs</td>\n",
       "      <td>Landsat 8 USGS Level 1 Collection-1 OLI-TIRS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>OLI_TIRS</td>\n",
       "      <td>L1TP</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ls8_usgs_sr_scene</td>\n",
       "      <td>Landsat 8 USGS Collection 1 Higher Level SR sc...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>LANDSAT_8</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTiff</td>\n",
       "      <td>None</td>\n",
       "      <td>OLI_TIRS</td>\n",
       "      <td>LEVEL2_USGS</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s1_gamma0_scene</td>\n",
       "      <td>Sentinel-1A/B SAR Gamma0 scenes, processed to ...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>SENTINEL_1</td>\n",
       "      <td>None</td>\n",
       "      <td>GeoTIFF</td>\n",
       "      <td>None</td>\n",
       "      <td>SAR</td>\n",
       "      <td>gamma0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name  \\\n",
       "id                               \n",
       "15     alos2_palsar_AMA_ingest   \n",
       "6              ls5_level1_usgs   \n",
       "19           ls5_usgs_sr_scene   \n",
       "17  ls7_collection1_AMA_ingest   \n",
       "5              ls7_level1_usgs   \n",
       "18           ls7_usgs_sr_scene   \n",
       "16  ls8_collection1_AMA_ingest   \n",
       "7               ls8_l1_pc_usgs   \n",
       "4              ls8_level1_usgs   \n",
       "21           ls8_usgs_sr_scene   \n",
       "13             s1_gamma0_scene   \n",
       "\n",
       "                                          description creation_time   lat  \\\n",
       "id                                                                          \n",
       "15  ALOS-2 PALSAR mosaic tiles generated for use i...          None  None   \n",
       "6        Landsat 5 USGS Level 1 Collection-1 OLI-TIRS          None  None   \n",
       "19  Landsat 5 USGS Collection 1 Level2 Surface Ref...          None  None   \n",
       "17  Landsat 7 USGS Collection 1 Higher Level SR sc...          None  None   \n",
       "5        Landsat 7 USGS Level 1 Collection-1 OLI-TIRS          None  None   \n",
       "18  Landsat 7 USGS Collection 1 Level2 Surface Ref...          None  None   \n",
       "16  Landsat 8 USGS Collection 1 Higher Level SR sc...          None  None   \n",
       "7      Landsat 8 USGS Level 1 Pre-Collection OLI-TIRS          None  None   \n",
       "4        Landsat 8 USGS Level 1 Collection-1 OLI-TIRS          None  None   \n",
       "21  Landsat 8 USGS Collection 1 Higher Level SR sc...          None  None   \n",
       "13  Sentinel-1A/B SAR Gamma0 scenes, processed to ...          None  None   \n",
       "\n",
       "      platform   lon   format  time instrument product_type label  crs  \\\n",
       "id                                                                       \n",
       "15      ALOS_2  None  GeoTIFF  None     PALSAR       gamma0  None  NaN   \n",
       "6    LANDSAT_5  None  GeoTiff  None         TM         L1TP  None  NaN   \n",
       "19   LANDSAT_5  None  GeoTiff  None         TM  LEVEL2_USGS  None  NaN   \n",
       "17   LANDSAT_7  None  GeoTiff  None        ETM       LEDAPS  None  NaN   \n",
       "5    LANDSAT_7  None  GeoTiff  None        ETM         L1TP  None  NaN   \n",
       "18   LANDSAT_7  None  GeoTiff  None        ETM  LEVEL2_USGS  None  NaN   \n",
       "16   LANDSAT_8  None  GeoTIFF  None   OLI_TIRS        LaSRC  None  NaN   \n",
       "7    LANDSAT_8  None  GeoTiff  None   OLI_TIRS          L1T  None  NaN   \n",
       "4    LANDSAT_8  None  GeoTiff  None   OLI_TIRS         L1TP  None  NaN   \n",
       "21   LANDSAT_8  None  GeoTiff  None   OLI_TIRS  LEVEL2_USGS  None  NaN   \n",
       "13  SENTINEL_1  None  GeoTIFF  None        SAR       gamma0  None  NaN   \n",
       "\n",
       "    resolution  tile_size  spatial_dimensions  \n",
       "id                                             \n",
       "15         NaN        NaN                 NaN  \n",
       "6          NaN        NaN                 NaN  \n",
       "19         NaN        NaN                 NaN  \n",
       "17         NaN        NaN                 NaN  \n",
       "5          NaN        NaN                 NaN  \n",
       "18         NaN        NaN                 NaN  \n",
       "16         NaN        NaN                 NaN  \n",
       "7          NaN        NaN                 NaN  \n",
       "4          NaN        NaN                 NaN  \n",
       "21         NaN        NaN                 NaN  \n",
       "13         NaN        NaN                 NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "import pandas as pd\n",
    "\n",
    "import datacube\n",
    "dc = datacube.Datacube()\n",
    "dc.list_products()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"alos2\"></a>\n",
    "### alos2_palsar_AMA_ingest\n",
    "\n",
    "**Caqueta**  \n",
    "``Time: 2015-01-01 and 2016-01-01 ``   \n",
    "``latitude:  (  0,            3.2)``    \n",
    "``longitude: (-76.26666668, -73.6)``      \n",
    "\n",
    "**Vietnam**  \n",
    "``Time: 2015-01-01 and 2016-01-01 ``   \n",
    "``latitude:  (  7.46666667,  12.8)``   \n",
    "``longitude: (103.46666668, 110.4)``     \n",
    "\n",
    "*****\n",
    "<a class=\"anchor\" id=\"ls5\"></a>\n",
    "### ls5_level1_usgs\n",
    "*No Data Indexed*\n",
    "*****\n",
    "### ls5_usgs_sr_scene\n",
    "\n",
    "``Time: 1984-05-26 to 2011-11-13``  \n",
    "``y: (-587700.0, 2187300.0)``  \n",
    "``x: (  68100.0,  932400.0)``  \n",
    "\n",
    "*****\n",
    "<a class=\"anchor\" id=\"ls7\"></a>\n",
    "### ls7_collection1_AMA_ingest\n",
    "\n",
    "**Caqueta**  \n",
    "``Time: 1999-08-21 to 2018-03-25``  \n",
    "``latitude:   (  0.000134747292617865, 1.077843593651382)``  \n",
    "``longitude:  (-74.91935994831539,   -73.30266193148462)``  \n",
    "``resolution: ( -0.000269494585236,    0.000269494585236)``  \n",
    "\n",
    "**Lake Baringo, Kenya**  \n",
    "``Time: 2005-01-08 to 2016-12-24``  \n",
    "``latitude:   ( 0.4997747685,  0.7495947795)``  \n",
    "``longitude:  (35.9742163305, 36.473586859499996)``  \n",
    "``resolution: (-0.000269493,   0.000269493)``  \n",
    "\n",
    "*****\n",
    "### ls7_level1_usgs\n",
    "*No Data Indexed*\n",
    "\n",
    "*****\n",
    "### ls7_usgs_sr_scene\n",
    "\n",
    "``Time: 1999-07-08 to 2018-11-02``  \n",
    "``y: (-586200.0, 2185200.0)``  \n",
    "``x: (  70800.0,  935100.0)``  \n",
    "\n",
    "*****\n",
    "<a class=\"anchor\" id=\"ls8\"></a>\n",
    "### ls8_collection1_AMA_ingest\n",
    "\n",
    "**Caqueta**  \n",
    "``Time: 2013-04-13 to 2018-03-26``  \n",
    "``latitude:   (  0.000134747292617865, 1.077843593651382)``  \n",
    "``longitude:  (-74.91935994831539,   -73.30266193148462) ``  \n",
    "``resolution: ( -0.000269494585236,    0.000269494585236)``  \n",
    "\n",
    "**Vietnam**  \n",
    "``Time: 2014-01-14 to 2016-12-21``  \n",
    "``latitude:   ( 10.513927001104687, 12.611133863411238)``    \n",
    "``longitude:  (106.79005909290998, 108.91906631627438) ``  \n",
    "``resolution: ( -0.000269494585236,  0.000269494585236)``  \n",
    "\n",
    "*****\n",
    "### ls8_l1_pc_usgs\n",
    "*No Data Indexed*\n",
    "\n",
    "*****\n",
    "### ls8_level1_usgs \n",
    "[Global](https://sandbox.test.frontiersi.io/user/Sandbox/view/examples/images/indexed_areas.png)\n",
    "\n",
    "\n",
    "*****\n",
    "### ls8_usgs_sr_scene\n",
    "``Time: 2013-03-21 to 2018-11-16``  \n",
    "``y: (-596700.0, 2193300.0)``  \n",
    "``x: (  82500.0,  930300.0)``  \n",
    "\n",
    "*****\n",
    "<a class=\"anchor\" id=\"s1\"></a>\n",
    "### s1_gamma0_scene\n",
    "\n",
    "``Time: 2015-05-12 to 2018-07-20``\n",
    "\n",
    "**Caqueta**   \n",
    "``latitude:  (  1.00018083,  2.)``  \n",
    "``longitude: (-75.,        -74.)``  \n",
    "\n",
    "**Samoa**  \n",
    "``latitude:  ( -13.99981917, -13.)``  \n",
    "``longitude: (-171.,        -173.)``    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"map\"></a>\n",
    "### Plotting Indicative Data Extents\n",
    "\n",
    "The following code generates an overview of the approximte extents of the product selected on line 1, shown in red. The yellow base image shows the extents of the global ls8_level1_usgs product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time: 2013-04-13 to 2018-03-26\n"
     ]
    }
   ],
   "source": [
    "product = 'ls8_collection1_AMA_ingest'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from pyproj import Proj, transform\n",
    "\n",
    "import datacube\n",
    "dc = datacube.Datacube()\n",
    "\n",
    "outProj = Proj(init='epsg:4326')\n",
    "\n",
    "x_bounds = []\n",
    "y_bounds = []\n",
    "unique_bounds=set()\n",
    "bounds=[]\n",
    "times=[]\n",
    "tiles = dc.find_datasets(product=product)\n",
    "if max(tiles[0].bounds) > 180: XY = True\n",
    "else: XY = False\n",
    "\n",
    "for tile in tiles:\n",
    "    if XY:\n",
    "        inProj = Proj(init=tile.crs)\n",
    "        l_lon,t_lat = transform(inProj,outProj,tile.bounds.left,tile.bounds.top)\n",
    "        r_lon,b_lat = transform(inProj,outProj,tile.bounds.right,tile.bounds.bottom)\n",
    "        x_bounds = [l_lon, r_lon, r_lon, l_lon]\n",
    "        y_bounds = [t_lat, t_lat, b_lat, b_lat]\n",
    "    \n",
    "    else:\n",
    "        x_bounds = [tile.bounds.left, tile.bounds.right, tile.bounds.right, tile.bounds.left]\n",
    "        y_bounds = [tile.bounds.top, tile.bounds.top, tile.bounds.bottom, tile.bounds.bottom]\n",
    "        \n",
    "    times.append(tile.time)\n",
    "    bounds_key = \"\".join(str(x) for x in x_bounds) + \"\".join(str(y) for y in y_bounds)\n",
    "    if bounds_key not in unique_bounds:\n",
    "        bounds.append([x_bounds, y_bounds, tile.time])\n",
    "\n",
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(1, figsize = (18,9))\n",
    "for i in bounds:\n",
    "    ax.fill(i[0], i[1], c='red')\n",
    "ax.axis('equal')\n",
    "\n",
    "plt.title(product)\n",
    "ax.set_xlabel('Longitude')\n",
    "ax.set_ylabel('Latitude')\n",
    "basemap = plt.imread('../images/indexed_areas.png')\n",
    "ax.imshow(basemap, extent=[-180,180,-90,90])\n",
    "ax.set_xlim(-180,180)\n",
    "ax.set_ylim(-90, 90)\n",
    "plt.show()\n",
    "print('Time:', min(times[-1]).strftime('%Y-%m-%d'), 'to', max(times[0]).strftime('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a class=\"anchor\" id=\"system\"></a>\n",
    "# System Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version 3.6.7 (default, Oct 22 2018, 11:32:17) \n",
      "[GCC 8.2.0]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print('Python Version', sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: datacube\n",
      "Version: 1.6.1+179.gc4ed2929.dirty\n",
      "Summary: An analysis environment for satellite and other earth observation data\n",
      "Home-page: https://github.com/opendatacube/datacube-core\n",
      "Author: Open Data Cube\n",
      "Author-email: None\n",
      "License: Apache License 2.0\n",
      "Location: /usr/local/lib/python3.6/dist-packages\n",
      "Requires: affine, cachetools, click, cloudpickle, dask, gdal, jsonschema, netcdf4, numpy, psycopg2, pypeg2, python-dateutil, pyyaml, rasterio, singledispatch, sqlalchemy, toolz, xarray\n",
      "---\n",
      "Name: xarray\n",
      "Version: 0.10.7\n",
      "Summary: N-D labeled arrays and datasets in Python\n",
      "Home-page: https://github.com/pydata/xarray\n",
      "Author: xarray Developers\n",
      "Author-email: xarray@googlegroups.com\n",
      "License: Apache\n",
      "Location: /usr/local/lib/python3.6/dist-packages\n",
      "Requires: pandas, numpy\n",
      "---\n",
      "Name: numpy\n",
      "Version: 1.15.4\n",
      "Summary: NumPy: array processing for numbers, strings, records, and objects.\n",
      "Home-page: http://www.numpy.org\n",
      "Author: Travis E. Oliphant et al.\n",
      "Author-email: None\n",
      "License: BSD\n",
      "Location: /home/jovyan/.local/lib/python3.6/site-packages\n",
      "Requires: \n",
      "---\n",
      "Name: pandas\n",
      "Version: 0.23.4\n",
      "Summary: Powerful data structures for data analysis, time series, and statistics\n",
      "Home-page: http://pandas.pydata.org\n",
      "Author: None\n",
      "Author-email: None\n",
      "License: BSD\n",
      "Location: /home/jovyan/.local/lib/python3.6/site-packages\n",
      "Requires: python-dateutil, pytz, numpy\n",
      "---\n",
      "Name: GDAL\n",
      "Version: 2.3.2\n",
      "Summary: GDAL: Geospatial Data Abstraction Library\n",
      "Home-page: http://www.gdal.org\n",
      "Author: Howard Butler\n",
      "Author-email: hobu.inc@gmail.com\n",
      "License: MIT\n",
      "Location: /usr/lib/python3/dist-packages\n",
      "Requires: \n",
      "---\n",
      "Name: matplotlib\n",
      "Version: 3.0.2\n",
      "Summary: Python plotting package\n",
      "Home-page: http://matplotlib.org\n",
      "Author: John D. Hunter, Michael Droettboom\n",
      "Author-email: matplotlib-users@python.org\n",
      "License: BSD\n",
      "Location: /home/jovyan/.local/lib/python3.6/site-packages\n",
      "Requires: pyparsing, cycler, python-dateutil, numpy, kiwisolver\n"
     ]
    }
   ],
   "source": [
    "!pip3 show datacube xarray numpy pandas gdal matplotlib "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extent Query Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Extent\n",
      "``Time: 2015-05-12 to 2018-07-20``  \n",
      "``latitude:  (-13.9998191681736, 2.0)``  \n",
      "``longitude: (-173.0, -74.0)``  \n",
      "Discrete Extents\n",
      "``Time: 2015-05-12 to 2018-07-20``  \n",
      "``latitude:  ([-13.99981917   1.00018083], [-13.   2.])``  \n",
      "``longitude: ([-173. -172.  -75.], [-172. -171.  -74.])``  \n"
     ]
    }
   ],
   "source": [
    "#Generates text extents of data\n",
    "product = 's1_gamma0_scene'\n",
    "\n",
    "import datacube\n",
    "dc = datacube.Datacube()\n",
    "import numpy as np\n",
    "\n",
    "left = np.array([])\n",
    "bottom = np.array([])\n",
    "right = np.array([])\n",
    "top = np.array([])\n",
    "times = np.array([])\n",
    "\n",
    "tiles = dc.find_datasets(product=product)\n",
    "for tile in tiles: #Each tile (unique time for each)\n",
    "    left = np.append(left, tile.bounds[0])\n",
    "    right = np.append(right, tile.bounds[2])\n",
    "    top = np.append(top, tile.bounds[3])\n",
    "    bottom = np.append(bottom, tile.bounds[1])\n",
    "    times = np.append(times, tile.time)\n",
    "    #corners = np.append(corners, [])\n",
    "print('Max Extent')\n",
    "print('``Time:', min(times).strftime('%Y-%m-%d'), 'to',  max(times).strftime('%Y-%m-%d')+'``  ')\n",
    "print('``latitude:  ('+str(min(np.unique(bottom)))+', '+str(max(np.unique(top)))+')``  ')\n",
    "print('``longitude: ('+str(min(np.unique(left)))+', '+str(max(np.unique(right)))+')``  ')\n",
    "\n",
    "print('Discrete Extents')\n",
    "print('``Time:', min(times).strftime('%Y-%m-%d'), 'to',  max(times).strftime('%Y-%m-%d')+'``  ')\n",
    "print('``latitude:  ('+str(np.unique(bottom))+', '+str(np.unique(top))+')``  ')\n",
    "print('``longitude: ('+str(np.unique(left))+', '+str(np.unique(right))+')``  ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>aliases</th>\n",
       "      <th>dtype</th>\n",
       "      <th>flags_definition</th>\n",
       "      <th>name</th>\n",
       "      <th>nodata</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product</th>\n",
       "      <th>measurement</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">alos2_palsar_AMA_ingest</th>\n",
       "      <th>hh</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hh</td>\n",
       "      <td>0</td>\n",
       "      <td>DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hv</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hv</td>\n",
       "      <td>0</td>\n",
       "      <td>DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>date</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>incidence_angle</th>\n",
       "      <td>NaN</td>\n",
       "      <td>uint8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>incidence_angle</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mask</th>\n",
       "      <td>NaN</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'cloud_confidence': {'bits': [0, 1, 2, 3, 4, ...</td>\n",
       "      <td>mask</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">ls5_level1_usgs</th>\n",
       "      <th>blue</th>\n",
       "      <td>[band_1, blue]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_2, green]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_3, red]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_4, nir]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_5, swir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, swir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>[QUALITY, quality]</td>\n",
       "      <td>int16</td>\n",
       "      <td>{'cloud': {'bits': [4], 'values': {'0': False,...</td>\n",
       "      <td>quality</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ls5_usgs_sr_scene</th>\n",
       "      <th>blue</th>\n",
       "      <td>[band_1, sr_band1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_2, sr_band2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_3, sr_band3]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_4, sr_band4]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_5, sr_band5]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, sr_band7]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr_atmos_opacity</th>\n",
       "      <td>[atmos_op]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sr_atmos_opacity</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel_qa</th>\n",
       "      <td>[pixel_qa]</td>\n",
       "      <td>uint16</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>pixel_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radsat_qa</th>\n",
       "      <td>[radsat_qa]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...</td>\n",
       "      <td>radsat_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr_cloud_qa</th>\n",
       "      <td>[pixel_qa]</td>\n",
       "      <td>uint16</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>sr_cloud_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ls7_collection1_AMA_ingest</th>\n",
       "      <th>blue</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atmos_opacity</th>\n",
       "      <td>NaN</td>\n",
       "      <td>uint8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atmos_opacity</td>\n",
       "      <td>0</td>\n",
       "      <td>unitless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int32</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>pixel_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radsat_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...</td>\n",
       "      <td>radsat_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloud_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'cloud_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7]...</td>\n",
       "      <td>cloud_qa</td>\n",
       "      <td>0</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">ls7_level1_usgs</th>\n",
       "      <th>blue</th>\n",
       "      <td>[band_1, blue]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_2, green]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_3, red]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_4, nir]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_5, swir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, swir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>[QUALITY, quality]</td>\n",
       "      <td>int16</td>\n",
       "      <td>{'cloud': {'bits': [4], 'values': {'0': False,...</td>\n",
       "      <td>quality</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ls7_usgs_sr_scene</th>\n",
       "      <th>blue</th>\n",
       "      <td>[band_1, sr_band1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_2, sr_band2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_3, sr_band3]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_4, sr_band4]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_5, sr_band5]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, sr_band7]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr_atmos_opacity</th>\n",
       "      <td>[atmos_op]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sr_atmos_opacity</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel_qa</th>\n",
       "      <td>[pixel_qa]</td>\n",
       "      <td>uint16</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>pixel_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radsat_qa</th>\n",
       "      <td>[radsat_qa]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...</td>\n",
       "      <td>radsat_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr_cloud_qa</th>\n",
       "      <td>[cloud_qa]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'cloud_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7]...</td>\n",
       "      <td>sr_cloud_qa</td>\n",
       "      <td>0</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ls8_collection1_AMA_ingest</th>\n",
       "      <th>coastal_aerosol</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coastal_aerosol</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int32</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>pixel_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aerosol_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int16</td>\n",
       "      <td>{'aerosol_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, ...</td>\n",
       "      <td>aerosol_qa</td>\n",
       "      <td>0</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radsat_qa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>int32</td>\n",
       "      <td>{'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...</td>\n",
       "      <td>radsat_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">ls8_l1_pc_usgs</th>\n",
       "      <th>coastal_aerosol</th>\n",
       "      <td>[band_1, coastal_aerosol]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coastal_aerosol</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>[band_2, blue]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_3, green]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_4, red]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_5, nir]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_6, swir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, swir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panchromatic</th>\n",
       "      <td>[band_8, panchromatic]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>panchromatic</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cirrus</th>\n",
       "      <td>[band_9, cirrus]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cirrus</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lwir1</th>\n",
       "      <td>[band_10, lwir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lwir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lwir2</th>\n",
       "      <td>[band_11, lwir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lwir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>[QUALITY, quality]</td>\n",
       "      <td>int16</td>\n",
       "      <td>{'water': {'bits': [4, 5], 'values': {'0': Fal...</td>\n",
       "      <td>quality</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">ls8_level1_usgs</th>\n",
       "      <th>coastal_aerosol</th>\n",
       "      <td>[band_1, coastal_aerosol]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coastal_aerosol</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>[band_2, blue]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_3, green]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_4, red]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_5, nir]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_6, swir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, swir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panchromatic</th>\n",
       "      <td>[band_8, panchromatic]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>panchromatic</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cirrus</th>\n",
       "      <td>[band_9, cirrus]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cirrus</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lwir1</th>\n",
       "      <td>[band_10, lwir1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lwir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lwir2</th>\n",
       "      <td>[band_11, lwir2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lwir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <td>[QUALITY, quality]</td>\n",
       "      <td>int16</td>\n",
       "      <td>{'cloud': {'bits': [4], 'values': {'0': False,...</td>\n",
       "      <td>quality</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">ls8_usgs_sr_scene</th>\n",
       "      <th>coastal_aerosol</th>\n",
       "      <td>[band_1, sr_band1]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>coastal_aerosol</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue</th>\n",
       "      <td>[band_2, sr_band2]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blue</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>green</th>\n",
       "      <td>[band_3, sr_band3]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>green</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>[band_4, sr_band4]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>red</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nir</th>\n",
       "      <td>[band_5, sr_band5]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nir</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir1</th>\n",
       "      <td>[band_6, sr_band6]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir1</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>swir2</th>\n",
       "      <td>[band_7, sr_band7]</td>\n",
       "      <td>int16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swir2</td>\n",
       "      <td>-9999</td>\n",
       "      <td>reflectance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pixel_qa</th>\n",
       "      <td>[pixel_qa]</td>\n",
       "      <td>uint16</td>\n",
       "      <td>{'snow': {'bits': 4, 'values': {'0': 'no_snow'...</td>\n",
       "      <td>pixel_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sr_aerosol</th>\n",
       "      <td>[sr_aerosol_qa, aerosol_qa, aerosol]</td>\n",
       "      <td>uint8</td>\n",
       "      <td>{'aerosol_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, ...</td>\n",
       "      <td>sr_aerosol</td>\n",
       "      <td>0</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radsat_qa</th>\n",
       "      <td>[radsat_qa]</td>\n",
       "      <td>uint16</td>\n",
       "      <td>{'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...</td>\n",
       "      <td>radsat_qa</td>\n",
       "      <td>1</td>\n",
       "      <td>bit_index</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">s1_gamma0_scene</th>\n",
       "      <th>vh</th>\n",
       "      <td>NaN</td>\n",
       "      <td>float32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vh</td>\n",
       "      <td>0</td>\n",
       "      <td>dB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vv</th>\n",
       "      <td>NaN</td>\n",
       "      <td>float32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vv</td>\n",
       "      <td>0</td>\n",
       "      <td>dB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                          aliases  \\\n",
       "product                    measurement                                              \n",
       "alos2_palsar_AMA_ingest    hh                                                 NaN   \n",
       "                           hv                                                 NaN   \n",
       "                           date                                               NaN   \n",
       "                           incidence_angle                                    NaN   \n",
       "                           mask                                               NaN   \n",
       "ls5_level1_usgs            blue                                    [band_1, blue]   \n",
       "                           green                                  [band_2, green]   \n",
       "                           red                                      [band_3, red]   \n",
       "                           nir                                      [band_4, nir]   \n",
       "                           swir1                                  [band_5, swir1]   \n",
       "                           swir2                                  [band_7, swir2]   \n",
       "                           quality                             [QUALITY, quality]   \n",
       "ls5_usgs_sr_scene          blue                                [band_1, sr_band1]   \n",
       "                           green                               [band_2, sr_band2]   \n",
       "                           red                                 [band_3, sr_band3]   \n",
       "                           nir                                 [band_4, sr_band4]   \n",
       "                           swir1                               [band_5, sr_band5]   \n",
       "                           swir2                               [band_7, sr_band7]   \n",
       "                           sr_atmos_opacity                            [atmos_op]   \n",
       "                           pixel_qa                                    [pixel_qa]   \n",
       "                           radsat_qa                                  [radsat_qa]   \n",
       "                           sr_cloud_qa                                 [pixel_qa]   \n",
       "ls7_collection1_AMA_ingest blue                                               NaN   \n",
       "                           green                                              NaN   \n",
       "                           red                                                NaN   \n",
       "                           nir                                                NaN   \n",
       "                           swir1                                              NaN   \n",
       "                           swir2                                              NaN   \n",
       "                           atmos_opacity                                      NaN   \n",
       "                           pixel_qa                                           NaN   \n",
       "                           radsat_qa                                          NaN   \n",
       "                           cloud_qa                                           NaN   \n",
       "ls7_level1_usgs            blue                                    [band_1, blue]   \n",
       "                           green                                  [band_2, green]   \n",
       "                           red                                      [band_3, red]   \n",
       "                           nir                                      [band_4, nir]   \n",
       "                           swir1                                  [band_5, swir1]   \n",
       "                           swir2                                  [band_7, swir2]   \n",
       "                           quality                             [QUALITY, quality]   \n",
       "ls7_usgs_sr_scene          blue                                [band_1, sr_band1]   \n",
       "                           green                               [band_2, sr_band2]   \n",
       "                           red                                 [band_3, sr_band3]   \n",
       "                           nir                                 [band_4, sr_band4]   \n",
       "                           swir1                               [band_5, sr_band5]   \n",
       "                           swir2                               [band_7, sr_band7]   \n",
       "                           sr_atmos_opacity                            [atmos_op]   \n",
       "                           pixel_qa                                    [pixel_qa]   \n",
       "                           radsat_qa                                  [radsat_qa]   \n",
       "                           sr_cloud_qa                                 [cloud_qa]   \n",
       "ls8_collection1_AMA_ingest coastal_aerosol                                    NaN   \n",
       "                           blue                                               NaN   \n",
       "                           green                                              NaN   \n",
       "                           red                                                NaN   \n",
       "                           nir                                                NaN   \n",
       "                           swir1                                              NaN   \n",
       "                           swir2                                              NaN   \n",
       "                           pixel_qa                                           NaN   \n",
       "                           aerosol_qa                                         NaN   \n",
       "                           radsat_qa                                          NaN   \n",
       "ls8_l1_pc_usgs             coastal_aerosol              [band_1, coastal_aerosol]   \n",
       "                           blue                                    [band_2, blue]   \n",
       "                           green                                  [band_3, green]   \n",
       "                           red                                      [band_4, red]   \n",
       "                           nir                                      [band_5, nir]   \n",
       "                           swir1                                  [band_6, swir1]   \n",
       "                           swir2                                  [band_7, swir2]   \n",
       "                           panchromatic                    [band_8, panchromatic]   \n",
       "                           cirrus                                [band_9, cirrus]   \n",
       "                           lwir1                                 [band_10, lwir1]   \n",
       "                           lwir2                                 [band_11, lwir2]   \n",
       "                           quality                             [QUALITY, quality]   \n",
       "ls8_level1_usgs            coastal_aerosol              [band_1, coastal_aerosol]   \n",
       "                           blue                                    [band_2, blue]   \n",
       "                           green                                  [band_3, green]   \n",
       "                           red                                      [band_4, red]   \n",
       "                           nir                                      [band_5, nir]   \n",
       "                           swir1                                  [band_6, swir1]   \n",
       "                           swir2                                  [band_7, swir2]   \n",
       "                           panchromatic                    [band_8, panchromatic]   \n",
       "                           cirrus                                [band_9, cirrus]   \n",
       "                           lwir1                                 [band_10, lwir1]   \n",
       "                           lwir2                                 [band_11, lwir2]   \n",
       "                           quality                             [QUALITY, quality]   \n",
       "ls8_usgs_sr_scene          coastal_aerosol                     [band_1, sr_band1]   \n",
       "                           blue                                [band_2, sr_band2]   \n",
       "                           green                               [band_3, sr_band3]   \n",
       "                           red                                 [band_4, sr_band4]   \n",
       "                           nir                                 [band_5, sr_band5]   \n",
       "                           swir1                               [band_6, sr_band6]   \n",
       "                           swir2                               [band_7, sr_band7]   \n",
       "                           pixel_qa                                    [pixel_qa]   \n",
       "                           sr_aerosol        [sr_aerosol_qa, aerosol_qa, aerosol]   \n",
       "                           radsat_qa                                  [radsat_qa]   \n",
       "s1_gamma0_scene            vh                                                 NaN   \n",
       "                           vv                                                 NaN   \n",
       "\n",
       "                                               dtype  \\\n",
       "product                    measurement                 \n",
       "alos2_palsar_AMA_ingest    hh                  int16   \n",
       "                           hv                  int16   \n",
       "                           date                int16   \n",
       "                           incidence_angle     uint8   \n",
       "                           mask                uint8   \n",
       "ls5_level1_usgs            blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           quality             int16   \n",
       "ls5_usgs_sr_scene          blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           sr_atmos_opacity    uint8   \n",
       "                           pixel_qa           uint16   \n",
       "                           radsat_qa           uint8   \n",
       "                           sr_cloud_qa        uint16   \n",
       "ls7_collection1_AMA_ingest blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           atmos_opacity       uint8   \n",
       "                           pixel_qa            int32   \n",
       "                           radsat_qa           uint8   \n",
       "                           cloud_qa            uint8   \n",
       "ls7_level1_usgs            blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           quality             int16   \n",
       "ls7_usgs_sr_scene          blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           sr_atmos_opacity    uint8   \n",
       "                           pixel_qa           uint16   \n",
       "                           radsat_qa           uint8   \n",
       "                           sr_cloud_qa         uint8   \n",
       "ls8_collection1_AMA_ingest coastal_aerosol     int16   \n",
       "                           blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           pixel_qa            int32   \n",
       "                           aerosol_qa          int16   \n",
       "                           radsat_qa           int32   \n",
       "ls8_l1_pc_usgs             coastal_aerosol     int16   \n",
       "                           blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           panchromatic        int16   \n",
       "                           cirrus              int16   \n",
       "                           lwir1               int16   \n",
       "                           lwir2               int16   \n",
       "                           quality             int16   \n",
       "ls8_level1_usgs            coastal_aerosol     int16   \n",
       "                           blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           panchromatic        int16   \n",
       "                           cirrus              int16   \n",
       "                           lwir1               int16   \n",
       "                           lwir2               int16   \n",
       "                           quality             int16   \n",
       "ls8_usgs_sr_scene          coastal_aerosol     int16   \n",
       "                           blue                int16   \n",
       "                           green               int16   \n",
       "                           red                 int16   \n",
       "                           nir                 int16   \n",
       "                           swir1               int16   \n",
       "                           swir2               int16   \n",
       "                           pixel_qa           uint16   \n",
       "                           sr_aerosol          uint8   \n",
       "                           radsat_qa          uint16   \n",
       "s1_gamma0_scene            vh                float32   \n",
       "                           vv                float32   \n",
       "\n",
       "                                                                              flags_definition  \\\n",
       "product                    measurement                                                           \n",
       "alos2_palsar_AMA_ingest    hh                                                              NaN   \n",
       "                           hv                                                              NaN   \n",
       "                           date                                                            NaN   \n",
       "                           incidence_angle                                                 NaN   \n",
       "                           mask              {'cloud_confidence': {'bits': [0, 1, 2, 3, 4, ...   \n",
       "ls5_level1_usgs            blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           quality           {'cloud': {'bits': [4], 'values': {'0': False,...   \n",
       "ls5_usgs_sr_scene          blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           sr_atmos_opacity                                                NaN   \n",
       "                           pixel_qa          {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "                           radsat_qa         {'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...   \n",
       "                           sr_cloud_qa       {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "ls7_collection1_AMA_ingest blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           atmos_opacity                                                   NaN   \n",
       "                           pixel_qa          {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "                           radsat_qa         {'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...   \n",
       "                           cloud_qa          {'cloud_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7]...   \n",
       "ls7_level1_usgs            blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           quality           {'cloud': {'bits': [4], 'values': {'0': False,...   \n",
       "ls7_usgs_sr_scene          blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           sr_atmos_opacity                                                NaN   \n",
       "                           pixel_qa          {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "                           radsat_qa         {'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...   \n",
       "                           sr_cloud_qa       {'cloud_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7]...   \n",
       "ls8_collection1_AMA_ingest coastal_aerosol                                                 NaN   \n",
       "                           blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           pixel_qa          {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "                           aerosol_qa        {'aerosol_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, ...   \n",
       "                           radsat_qa         {'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...   \n",
       "ls8_l1_pc_usgs             coastal_aerosol                                                 NaN   \n",
       "                           blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           panchromatic                                                    NaN   \n",
       "                           cirrus                                                          NaN   \n",
       "                           lwir1                                                           NaN   \n",
       "                           lwir2                                                           NaN   \n",
       "                           quality           {'water': {'bits': [4, 5], 'values': {'0': Fal...   \n",
       "ls8_level1_usgs            coastal_aerosol                                                 NaN   \n",
       "                           blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           panchromatic                                                    NaN   \n",
       "                           cirrus                                                          NaN   \n",
       "                           lwir1                                                           NaN   \n",
       "                           lwir2                                                           NaN   \n",
       "                           quality           {'cloud': {'bits': [4], 'values': {'0': False,...   \n",
       "ls8_usgs_sr_scene          coastal_aerosol                                                 NaN   \n",
       "                           blue                                                            NaN   \n",
       "                           green                                                           NaN   \n",
       "                           red                                                             NaN   \n",
       "                           nir                                                             NaN   \n",
       "                           swir1                                                           NaN   \n",
       "                           swir2                                                           NaN   \n",
       "                           pixel_qa          {'snow': {'bits': 4, 'values': {'0': 'no_snow'...   \n",
       "                           sr_aerosol        {'aerosol_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, ...   \n",
       "                           radsat_qa         {'radsat_qa': {'bits': [0, 1, 2, 3, 4, 5, 6, 7...   \n",
       "s1_gamma0_scene            vh                                                              NaN   \n",
       "                           vv                                                              NaN   \n",
       "\n",
       "                                                         name  nodata  \\\n",
       "product                    measurement                                  \n",
       "alos2_palsar_AMA_ingest    hh                              hh       0   \n",
       "                           hv                              hv       0   \n",
       "                           date                          date       0   \n",
       "                           incidence_angle    incidence_angle       0   \n",
       "                           mask                          mask       0   \n",
       "ls5_level1_usgs            blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           quality                    quality       0   \n",
       "ls5_usgs_sr_scene          blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           sr_atmos_opacity  sr_atmos_opacity       0   \n",
       "                           pixel_qa                  pixel_qa       1   \n",
       "                           radsat_qa                radsat_qa       1   \n",
       "                           sr_cloud_qa            sr_cloud_qa       1   \n",
       "ls7_collection1_AMA_ingest blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           atmos_opacity        atmos_opacity       0   \n",
       "                           pixel_qa                  pixel_qa       1   \n",
       "                           radsat_qa                radsat_qa       1   \n",
       "                           cloud_qa                  cloud_qa       0   \n",
       "ls7_level1_usgs            blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           quality                    quality       0   \n",
       "ls7_usgs_sr_scene          blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           sr_atmos_opacity  sr_atmos_opacity       0   \n",
       "                           pixel_qa                  pixel_qa       1   \n",
       "                           radsat_qa                radsat_qa       1   \n",
       "                           sr_cloud_qa            sr_cloud_qa       0   \n",
       "ls8_collection1_AMA_ingest coastal_aerosol    coastal_aerosol   -9999   \n",
       "                           blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           pixel_qa                  pixel_qa       1   \n",
       "                           aerosol_qa              aerosol_qa       0   \n",
       "                           radsat_qa                radsat_qa       1   \n",
       "ls8_l1_pc_usgs             coastal_aerosol    coastal_aerosol   -9999   \n",
       "                           blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           panchromatic          panchromatic   -9999   \n",
       "                           cirrus                      cirrus   -9999   \n",
       "                           lwir1                        lwir1   -9999   \n",
       "                           lwir2                        lwir2   -9999   \n",
       "                           quality                    quality       0   \n",
       "ls8_level1_usgs            coastal_aerosol    coastal_aerosol   -9999   \n",
       "                           blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           panchromatic          panchromatic   -9999   \n",
       "                           cirrus                      cirrus   -9999   \n",
       "                           lwir1                        lwir1   -9999   \n",
       "                           lwir2                        lwir2   -9999   \n",
       "                           quality                    quality       0   \n",
       "ls8_usgs_sr_scene          coastal_aerosol    coastal_aerosol   -9999   \n",
       "                           blue                          blue   -9999   \n",
       "                           green                        green   -9999   \n",
       "                           red                            red   -9999   \n",
       "                           nir                            nir   -9999   \n",
       "                           swir1                        swir1   -9999   \n",
       "                           swir2                        swir2   -9999   \n",
       "                           pixel_qa                  pixel_qa       1   \n",
       "                           sr_aerosol              sr_aerosol       0   \n",
       "                           radsat_qa                radsat_qa       1   \n",
       "s1_gamma0_scene            vh                              vh       0   \n",
       "                           vv                              vv       0   \n",
       "\n",
       "                                                   units  \n",
       "product                    measurement                    \n",
       "alos2_palsar_AMA_ingest    hh                         DN  \n",
       "                           hv                         DN  \n",
       "                           date                        1  \n",
       "                           incidence_angle             1  \n",
       "                           mask                        1  \n",
       "ls5_level1_usgs            blue                        1  \n",
       "                           green                       1  \n",
       "                           red                         1  \n",
       "                           nir                         1  \n",
       "                           swir1                       1  \n",
       "                           swir2                       1  \n",
       "                           quality                     1  \n",
       "ls5_usgs_sr_scene          blue              reflectance  \n",
       "                           green             reflectance  \n",
       "                           red               reflectance  \n",
       "                           nir               reflectance  \n",
       "                           swir1             reflectance  \n",
       "                           swir2             reflectance  \n",
       "                           sr_atmos_opacity            1  \n",
       "                           pixel_qa            bit_index  \n",
       "                           radsat_qa           bit_index  \n",
       "                           sr_cloud_qa         bit_index  \n",
       "ls7_collection1_AMA_ingest blue              reflectance  \n",
       "                           green             reflectance  \n",
       "                           red               reflectance  \n",
       "                           nir               reflectance  \n",
       "                           swir1             reflectance  \n",
       "                           swir2             reflectance  \n",
       "                           atmos_opacity        unitless  \n",
       "                           pixel_qa            bit_index  \n",
       "                           radsat_qa           bit_index  \n",
       "                           cloud_qa            bit_index  \n",
       "ls7_level1_usgs            blue                        1  \n",
       "                           green                       1  \n",
       "                           red                         1  \n",
       "                           nir                         1  \n",
       "                           swir1                       1  \n",
       "                           swir2                       1  \n",
       "                           quality                     1  \n",
       "ls7_usgs_sr_scene          blue              reflectance  \n",
       "                           green             reflectance  \n",
       "                           red               reflectance  \n",
       "                           nir               reflectance  \n",
       "                           swir1             reflectance  \n",
       "                           swir2             reflectance  \n",
       "                           sr_atmos_opacity            1  \n",
       "                           pixel_qa            bit_index  \n",
       "                           radsat_qa           bit_index  \n",
       "                           sr_cloud_qa         bit_index  \n",
       "ls8_collection1_AMA_ingest coastal_aerosol   reflectance  \n",
       "                           blue              reflectance  \n",
       "                           green             reflectance  \n",
       "                           red               reflectance  \n",
       "                           nir               reflectance  \n",
       "                           swir1             reflectance  \n",
       "                           swir2             reflectance  \n",
       "                           pixel_qa            bit_index  \n",
       "                           aerosol_qa          bit_index  \n",
       "                           radsat_qa           bit_index  \n",
       "ls8_l1_pc_usgs             coastal_aerosol             1  \n",
       "                           blue                        1  \n",
       "                           green                       1  \n",
       "                           red                         1  \n",
       "                           nir                         1  \n",
       "                           swir1                       1  \n",
       "                           swir2                       1  \n",
       "                           panchromatic                1  \n",
       "                           cirrus                      1  \n",
       "                           lwir1                       1  \n",
       "                           lwir2                       1  \n",
       "                           quality                     1  \n",
       "ls8_level1_usgs            coastal_aerosol             1  \n",
       "                           blue                        1  \n",
       "                           green                       1  \n",
       "                           red                         1  \n",
       "                           nir                         1  \n",
       "                           swir1                       1  \n",
       "                           swir2                       1  \n",
       "                           panchromatic                1  \n",
       "                           cirrus                      1  \n",
       "                           lwir1                       1  \n",
       "                           lwir2                       1  \n",
       "                           quality                     1  \n",
       "ls8_usgs_sr_scene          coastal_aerosol   reflectance  \n",
       "                           blue              reflectance  \n",
       "                           green             reflectance  \n",
       "                           red               reflectance  \n",
       "                           nir               reflectance  \n",
       "                           swir1             reflectance  \n",
       "                           swir2             reflectance  \n",
       "                           pixel_qa            bit_index  \n",
       "                           sr_aerosol          bit_index  \n",
       "                           radsat_qa           bit_index  \n",
       "s1_gamma0_scene            vh                         dB  \n",
       "                           vv                         dB  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_rows = 200\n",
    "dc.list_measurements()"
   ]
  }
 ],
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