strong-beams-atl08.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "879766a9-70fc-455d-8d80-3b4d46298022",
   "metadata": {},
   "source": [
    "ICESat-2 orientation can be: backward, forward, and transition. During transition, ICESat-2 is maneuvering between the two orientations. Science quality is potentially degraded while in transition mode. (Source: https://nsidc.org/sites/nsidc.org/files/technical-references/ICESat2_ATL03_data_dict_v005.pdf)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffa1df3-38f0-4296-8f54-67e8bf660efd",
   "metadata": {},
   "source": [
    "The parameter \"/orbit_info/sc_orient\" in ATL03 denotes the orientation of ICESat-2.\n",
    "\n",
    "0 = backward\n",
    "\n",
    "1 = forward\n",
    "\n",
    "2 = transition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "769024d5-5a3d-4f5c-bd85-538b8e570612",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b00683-310d-4732-94c7-5339e8a260ff",
   "metadata": {},
   "source": [
    "Script reads the variable \"/orbit_info/sc_orient\" to determine orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "750a9ff0-220c-4e7d-a059-e6872313fffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def subsetBeamsv2(f, strength):\n",
    "    '''Subsets either strong or weak beams in ATL03 from a h5py dataset'''\n",
    "    orientation = f['orbit_info']['sc_orient'][0] ## Extract the orientation\n",
    "    if strength == 'strong':\n",
    "        if orientation == 0:\n",
    "            strong_left = pd.DataFrame(data={'lat': f['gt1l/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt1l/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt1l/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "            strong_central = pd.DataFrame(data={'lat': f['gt2l/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt2l/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt2l/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "            strong_right = pd.DataFrame(data={'lat': f['gt3l/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt3l/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt3l/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "        elif orientation == 1:\n",
    "            strong_left = pd.DataFrame(data={'lat': f['gt1r/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt1r/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt1r/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "            strong_central = pd.DataFrame(data={'lat': f['gt2r/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt2r/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt2r/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "            strong_right = pd.DataFrame(data={'lat': f['gt3r/land_segments/latitude'][:],\n",
    "                                            'lon': f['gt3r/land_segments/longitude'][:],\n",
    "                                            'ch': f['gt3r/land_segments/canopy/h_canopy'][:]})\n",
    "            \n",
    "        else:\n",
    "            print('Error: invalid strength input')\n",
    "            \n",
    "        return strong_left, strong_central, strong_right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8d7da6b-4bc9-4fdf-8d1a-63f30691820d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import icepyx as ipx\n",
    "\n",
    "short_name = 'ATL08'\n",
    "\n",
    "rgts = ['0698']\n",
    "spatial_extent = [-94, 46.5, -84, 49.5]\n",
    "date_range = ['2021-11-07', '2021-11-07']\n",
    "\n",
    "region = ipx.Query(short_name, spatial_extent, date_range, tracks=rgts)\n",
    "#region.avail_granules(ids=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3582a2f-0383-4f21-a18e-c45918f81c64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of data order requests is  1  for  1  granules.\n",
      "Data request  1  of  1  is submitting to NSIDC\n",
      "order ID:  5000002860924\n",
      "Initial status of your order request at NSIDC is:  processing\n",
      "Your order status is still  processing  at NSIDC. Please continue waiting... this may take a few moments.\n",
      "Your order is: complete\n"
     ]
    }
   ],
   "source": [
    "uid = 'zhfair'\n",
    "email = 'zhfair@umich.edu'\n",
    "region.earthdata_login(uid, email)\n",
    "\n",
    "region.order_granules()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fb8c104-e301-47b0-bf80-ff9d7ed25387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning download of zipped output...\n",
      "Data request 5000002860924 of  1  order(s) is downloaded.\n",
      "Download complete\n"
     ]
    }
   ],
   "source": [
    "path = 'strong-beams/'\n",
    "region.download_granules(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2163a08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the ATL08 file, and extract the strong beam data\n",
    "f = h5py.File('strong-beams/processed_ATL08_20211107040925_06981306_005_01.h5', 'r')\n",
    "\n",
    "strong_left,strong_central,strong_right = subsetBeamsv2(f, 'strong')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22017fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2709925b64bb4290a727766540a8fa05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[47.995556, -89.35192], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Subset the data to the Michigan UP\n",
    "strong_left = strong_left[(strong_left['lat']>47) & (strong_left['lat'].values<49)]\n",
    "strong_central = strong_central[(strong_central['lat']>47) & (strong_central['lat']<49)]\n",
    "strong_right = strong_right[(strong_right['lat']>47) & (strong_right['lat']<49)]\n",
    "\n",
    "\n",
    "# Plot a map of the region\n",
    "# Set up a map with the flight tracks as overlays\n",
    "from ipyleaflet import Map, basemaps, basemap_to_tiles, Polyline\n",
    "\n",
    "m = Map(\n",
    "    basemap=basemap_to_tiles(basemaps.Esri.WorldImagery),\n",
    "    center=(strong_central['lat'].mean(), strong_central['lon'].mean()),\n",
    "    zoom=8\n",
    ")\n",
    "\n",
    "left_line = Polyline(\n",
    "    locations=[\n",
    "        [strong_left['lat'].min(), strong_left['lon'].min()],\n",
    "        [strong_left['lat'].max(), strong_left['lon'].max()]\n",
    "    ],\n",
    "    color=\"green\" ,\n",
    "    fill=False\n",
    ")\n",
    "m.add_layer(left_line)\n",
    "\n",
    "central_line = Polyline(\n",
    "    locations=[\n",
    "        [strong_central['lat'].min(), strong_central['lon'].min()],\n",
    "        [strong_central['lat'].max(), strong_central['lon'].max()]\n",
    "    ],\n",
    "    color=\"green\" ,\n",
    "    fill=False\n",
    ")\n",
    "m.add_layer(central_line)\n",
    "\n",
    "right_line = Polyline(\n",
    "    locations=[\n",
    "        [strong_right['lat'].min(), strong_right['lon'].min()],\n",
    "        [strong_right['lat'].max(), strong_right['lon'].max()]\n",
    "    ],\n",
    "    color=\"green\" ,\n",
    "    fill=False\n",
    ")\n",
    "m.add_layer(right_line)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fa57a47c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot canopy height for each strong beam\n",
    "import matplotlib.pyplot as plt\n",
    "fig,axs = plt.subplots(1,3)\n",
    "axs[0].plot(strong_left['lat'][strong_left['ch']<1e38], strong_left['ch'][strong_left['ch']<1e38], '.', markersize=1)\n",
    "axs[0].set_title('Left strong beam')\n",
    "axs[1].plot(strong_central['lat'][strong_central['ch']<1e38], strong_central['ch'][strong_central['ch']<1e38], '.', markersize=1)\n",
    "axs[1].set_title('Central strong beam')\n",
    "axs[2].plot(strong_right['lat'][strong_right['ch']<1e38], strong_right['ch'][strong_right['ch']<1e38], '.', markersize=1)\n",
    "axs[2].set_title('Right strong beam')\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.label_outer()\n",
    "plt.tight_layout()\n",
    "plt.show()"
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   "id": "db0574b6-cc3d-4c85-83f5-b60ee4a920ed",
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   "outputs": [],
   "source": []
  }
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