notebooks/SnowEx_ASO_MODIS_Snow/Snow-tutorial_rendered.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Snow Depth and Snow Cover Data Exploration \n",
    "\n",
    "This tutorial demonstrates how to access and compare coincident snow data across in-situ, airborne, and satellite platforms from NASA's SnowEx, ASO, and MODIS data sets, respectively. All data are available from the NASA National Snow and Ice Data Center Distributed Active Archive Center, or NSIDC DAAC. \n",
    "\n",
    "### Here are the steps you will learn in this snow data notebook:\n",
    "\n",
    "1. Explore the coverage and structure of select NSIDC DAAC snow data products, as well as available resources to search and access data.\n",
    "2. Search and download spatiotemporally coincident data across in-situ, airborne, and satellite observations.\n",
    "3. Subset and reformat MODIS data using the NSIDC DAAC API.\n",
    "4. Read CSV and GeoTIFF formatted data using geopandas and rasterio libraries.\n",
    "5. Subset data based on buffered area.\n",
    "5. Extract and visualize raster values at point locations.\n",
    "6. Save output as shapefile for further GIS analysis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## Explore snow products and resources\n",
    "\n",
    "\n",
    "### NSIDC introduction\n",
    "\n",
    "[The National Snow and Ice Data Center](https://nsidc.org) provides over 1100 data sets covering the Earth's cryosphere and more, all of which are available to the public free of charge. Beyond providing these data, NSIDC creates tools for data access, supports data users, performs scientific research, and educates the public about the cryosphere. \n",
    "\n",
    "#### Select Data Resources\n",
    "\n",
    "* [NSIDC Data Search](https://nsidc.org/data/search/#keywords=snow)\n",
    "    * Search NSIDC snow data\n",
    "* [NSIDC Data Update Announcements](https://nsidc.org/the-drift/data-update/) \n",
    "    * News and tips for data users\n",
    "* [NASA Earthdata Search](http://search.earthdata.nasa.gov/)\n",
    "    * Search and access data across the NASA Earthdata\n",
    "* [NASA Worldview](https://worldview.earthdata.nasa.gov/)\n",
    "    * Interactive interface for browsing full-resolution, global, daily satellite images\n",
    "    \n",
    "    \n",
    "#### Snow Today\n",
    "\n",
    "[Snow Today](https://nsidc.org/snow-today), a collaboration with the University of Colorado's Institute of Alpine and Arctic Research (INSTAAR), provides near-real-time snow analysis for the western United States and regular reports on conditions during the winter season. Snow Today is funded by NASA Hydrological Sciences Program and utilizes data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument and snow station data from the Snow Telemetry (SNOTEL) network by the Natural Resources Conservation Service (NRCS), United States Department of Agriculture (USDA) and the California Department of Water Resources: www.wcc.nrcs.usda.gov/snow.\n",
    "\n",
    "### Snow-related missions and data sets used in the following steps:\n",
    "\n",
    "* [SnowEx](https://nsidc.org/data/snowex)\n",
    "    * SnowEx17 Ground Penetrating Radar, Version 2: https://doi.org/10.5067/G21LGCNLFSC5\n",
    "* [ASO](https://nsidc.org/data/aso)\n",
    "    * ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1: https://doi.org/10.5067/KIE9QNVG7HP0\n",
    "* [MODIS](https://nsidc.org/data/modis)\n",
    "    * MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6: https://doi.org/10.5067/MODIS/MOD10A1.006\n",
    "\n",
    "\n",
    "#### Other relevant snow products:\n",
    "\n",
    "* [VIIRS Snow Data](http://nsidc.org/data/search/#sortKeys=score,,desc/facetFilters=%257B%2522facet_sensor%2522%253A%255B%2522Visible-Infrared%2520Imager-Radiometer%2520Suite%2520%257C%2520VIIRS%2522%255D%252C%2522facet_parameter%2522%253A%255B%2522SNOW%2520COVER%2522%252C%2522Snow%2520Cover%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
    "\n",
    "* [AMSR-E and AMSR-E/AMSR2 Unified Snow Data](http://nsidc.org/data/search/#sortKeys=score,,desc/facetFilters=%257B%2522facet_sensor%2522%253A%255B%2522Advanced%2520Microwave%2520Scanning%2520Radiometer-EOS%2520%257C%2520AMSR-E%2522%252C%2522Advanced%2520Microwave%2520Scanning%2520Radiometer%25202%2520%257C%2520AMSR2%2522%255D%252C%2522facet_parameter%2522%253A%255B%2522SNOW%2520WATER%2520EQUIVALENT%2522%252C%2522Snow%2520Water%2520Equivalent%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
    "\n",
    "* [MEaSUREs Snow Data](http://nsidc.org/data/search/#keywords=measures/sortKeys=score,,desc/facetFilters=%257B%2522facet_parameter%2522%253A%255B%2522SNOW%2520COVER%2522%255D%252C%2522facet_sponsored_program%2522%253A%255B%2522NASA%2520National%2520Snow%2520and%2520Ice%2520Data%2520Center%2520Distributed%2520Active%2520Archive%2520Center%2520%257C%2520NASA%2520NSIDC%2520DAAC%2522%255D%252C%2522facet_format%2522%253A%255B%2522NetCDF%2522%255D%252C%2522facet_temporal_duration%2522%253A%255B%252210%252B%2520years%2522%255D%257D/pageNumber=1/itemsPerPage=25)\n",
    "  \n",
    "* Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent (NISE), Version 5: https://doi.org/10.5067/3KB2JPLFPK3R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "### Import Packages\n",
    "\n",
    "Get started by importing packages needed to run the following code blocks, including the `tutorial_helper_functions` module provided within this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Polygon, mapping\n",
    "from shapely.geometry.polygon import orient\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio\n",
    "from rasterio.plot import show\n",
    "import numpy as np\n",
    "import pyresample as prs\n",
    "import requests\n",
    "import json\n",
    "import pprint\n",
    "from rasterio.mask import mask\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "\n",
    "# This is our functions module. We created several helper functions to discover, access, and harmonize the data below.\n",
    "import tutorial_helper_functions as fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "\n",
    "## Data Discovery\n",
    "\n",
    "Start by identifying your study area and exploring coincident data over the same time and area. \n",
    "\n",
    "NASA Earthdata Search can be used to visualize file coverage over mulitple data sets and to access the same data you will be working with below: \n",
    "https://search.earthdata.nasa.gov/projects?projectId=5366449248\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify area and time of interest\n",
    "\n",
    "Since our focus is on the Grand Mesa study site of the NASA SnowEx campaign, we'll use that area to search for coincident data across other data products. From the [SnowEx17 Ground Penetrating Radar Version 2](https://doi.org/10.5067/G21LGCNLFSC5) landing page, you can find the rectangular spatial coverage under the Overview tab, or you can draw a polygon over your area of interest in the map under the Download Data tab and export the shape as a geojson file using the Export Polygon icon shown below. An example polygon geojson file is provided in the /Data folder of this repository.   \n",
    "\n",
    "<img align=\"left\" src=\"Data-download-polygon-export.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create polygon coordinate string\n",
    "\n",
    "Read in the geojson file as a GeoDataFrame object and simplify and reorder using the shapely package. This will be converted back to a dictionary to be applied as our polygon search parameter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polygon coordinates to be used in search: -108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x102328e48>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polygon_filepath = str(os.getcwd() + '/Data/nsidc-polygon.json') # Note: A shapefile or other vector-based spatial data format could be substituted here.\n",
    "\n",
    "gdf = gpd.read_file(polygon_filepath) #Return a GeoDataFrame object\n",
    "\n",
    "# Simplify polygon for complex shapes in order to pass a reasonable request length to CMR. The larger the tolerance value, the more simplified the polygon.\n",
    "# Orient counter-clockwise: CMR polygon points need to be provided in counter-clockwise order. The last point should match the first point to close the polygon.\n",
    "poly = orient(gdf.simplify(0.05, preserve_topology=False).loc[0],sign=1.0)\n",
    "\n",
    "#Format dictionary to polygon coordinate pairs for CMR polygon filtering\n",
    "polygon = ','.join([str(c) for xy in zip(*poly.exterior.coords.xy) for c in xy])\n",
    "print('Polygon coordinates to be used in search:', polygon)\n",
    "poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set time range\n",
    "\n",
    "We are interested in accessing files within each data set over the same time range, so we'll start by searching all of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "temporal = '2017-01-01T00:00:00Z,2017-12-31T23:59:59Z' # Set temporal range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create data dictionary \n",
    "\n",
    "Create a nested dictionary with each data set shortname and version, as well as shared temporal range and polygonal area of interest. Data set shortnames, or IDs, as well as version numbers, are located at the top of every NSIDC landing page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dict = { 'snowex': {'short_name': 'SNEX17_GPR','version': '2','polygon': polygon,'temporal':temporal},\n",
    "                'aso': {'short_name': 'ASO_3M_SD','version': '1','polygon': polygon,'temporal':temporal},\n",
    "             'modis': {'short_name': 'MOD10A1','version': '6','polygon': polygon,'temporal':temporal}\n",
    "            }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine how many files exist over this time and area of interest, as well as the average size and total volume of those files\n",
    "\n",
    "We will use the `granule_info` function to query metadata about each data set and associated files using the [Common Metadata Repository (CMR)](https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html), which is a high-performance, high-quality, continuously evolving metadata system that catalogs Earth Science data and associated service metadata records. Note that not all NSIDC data can be searched at the file level using CMR, particularly those outside of the NASA DAAC program. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 files of SNEX17_GPR version 2 over my area and time of interest.\n",
      "The average size of each file is 69.73 MB and the total size of all 3 granules is 209.20 MB\n",
      "There are 5 files of ASO_3M_SD version 1 over my area and time of interest.\n",
      "The average size of each file is 1689.92 MB and the total size of all 5 granules is 8449.60 MB\n",
      "There are 364 files of MOD10A1 version 6 over my area and time of interest.\n",
      "The average size of each file is 8.23 MB and the total size of all 364 granules is 2995.34 MB\n"
     ]
    }
   ],
   "source": [
    "for k, v in data_dict.items(): fn.granule_info(data_dict[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find coincident data\n",
    "\n",
    "The function above tells us the size of data available for each data set over our time and area of interest, but we want to go a step further and determine what time ranges are coincident based on our bounding box. This `time_overlap` helper function returns a dataframe with file names, dataset_id, start date, and end date for all files that overlap in temporal range across all data sets of interest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19  total files returned\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>short_name</th>\n",
       "      <th>version</th>\n",
       "      <th>producer_granule_id</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SnowEx17 Ground Penetrating Radar V002</td>\n",
       "      <td>SNEX17_GPR</td>\n",
       "      <td>002</td>\n",
       "      <td>SnowEx17_GPR_Version2_Week1.csv</td>\n",
       "      <td>2017-02-08T00:00:00.000Z</td>\n",
       "      <td>2017-02-10T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SnowEx17 Ground Penetrating Radar V002</td>\n",
       "      <td>SNEX17_GPR</td>\n",
       "      <td>002</td>\n",
       "      <td>SnowEx17_GPR_Version2_Week2.csv</td>\n",
       "      <td>2017-02-14T00:00:00.000Z</td>\n",
       "      <td>2017-02-17T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SnowEx17 Ground Penetrating Radar V002</td>\n",
       "      <td>SNEX17_GPR</td>\n",
       "      <td>002</td>\n",
       "      <td>SnowEx17_GPR_Version2_Week3.csv</td>\n",
       "      <td>2017-02-21T00:00:00.000Z</td>\n",
       "      <td>2017-02-25T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASO L4 Lidar Snow Depth 3m UTM Grid V001</td>\n",
       "      <td>ASO_3M_SD</td>\n",
       "      <td>001</td>\n",
       "      <td>ASO_3M_SD_USCOGM_20170208</td>\n",
       "      <td>2017-02-08T00:00:00.000Z</td>\n",
       "      <td>2017-02-08T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASO L4 Lidar Snow Depth 3m UTM Grid V001</td>\n",
       "      <td>ASO_3M_SD</td>\n",
       "      <td>001</td>\n",
       "      <td>ASO_3M_SD_USCOGM_20170216</td>\n",
       "      <td>2017-02-16T00:00:00.000Z</td>\n",
       "      <td>2017-02-16T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ASO L4 Lidar Snow Depth 3m UTM Grid V001</td>\n",
       "      <td>ASO_3M_SD</td>\n",
       "      <td>001</td>\n",
       "      <td>ASO_3M_SD_USCOGM_20170221</td>\n",
       "      <td>2017-02-21T00:00:00.000Z</td>\n",
       "      <td>2017-02-21T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ASO L4 Lidar Snow Depth 3m UTM Grid V001</td>\n",
       "      <td>ASO_3M_SD</td>\n",
       "      <td>001</td>\n",
       "      <td>ASO_3M_SD_USCOGM_20170225</td>\n",
       "      <td>2017-02-25T00:00:00.000Z</td>\n",
       "      <td>2017-02-25T23:59:59.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017039.h09v05.006.2017041102600.hdf</td>\n",
       "      <td>2017-02-08T16:20:00.000Z</td>\n",
       "      <td>2017-02-08T19:40:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017040.h09v05.006.2017042102640.hdf</td>\n",
       "      <td>2017-02-09T17:05:00.000Z</td>\n",
       "      <td>2017-02-09T18:50:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017041.h09v05.006.2017043095629.hdf</td>\n",
       "      <td>2017-02-10T16:10:00.000Z</td>\n",
       "      <td>2017-02-10T19:30:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017045.h09v05.006.2017047103323.hdf</td>\n",
       "      <td>2017-02-14T17:20:00.000Z</td>\n",
       "      <td>2017-02-14T19:05:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017046.h09v05.006.2017052213130.hdf</td>\n",
       "      <td>2017-02-15T16:30:00.000Z</td>\n",
       "      <td>2017-02-15T18:10:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017047.h09v05.006.2017053103120.hdf</td>\n",
       "      <td>2017-02-16T17:10:00.000Z</td>\n",
       "      <td>2017-02-16T18:55:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017048.h09v05.006.2017050103600.hdf</td>\n",
       "      <td>2017-02-17T16:15:00.000Z</td>\n",
       "      <td>2017-02-17T19:35:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017052.h09v05.006.2017054100801.hdf</td>\n",
       "      <td>2017-02-21T17:30:00.000Z</td>\n",
       "      <td>2017-02-21T19:10:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017053.h09v05.006.2017055094801.hdf</td>\n",
       "      <td>2017-02-22T16:35:00.000Z</td>\n",
       "      <td>2017-02-22T18:20:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017054.h09v05.006.2017059063600.hdf</td>\n",
       "      <td>2017-02-23T17:15:00.000Z</td>\n",
       "      <td>2017-02-23T19:00:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017055.h09v05.006.2017057092149.hdf</td>\n",
       "      <td>2017-02-24T16:20:00.000Z</td>\n",
       "      <td>2017-02-24T19:40:00.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>MODIS/Terra Snow Cover Daily L3 Global 500m SI...</td>\n",
       "      <td>MOD10A1</td>\n",
       "      <td>006</td>\n",
       "      <td>MOD10A1.A2017056.h09v05.006.2017058092815.hdf</td>\n",
       "      <td>2017-02-25T17:05:00.000Z</td>\n",
       "      <td>2017-02-25T18:50:00.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           dataset_id  short_name version  \\\n",
       "0              SnowEx17 Ground Penetrating Radar V002  SNEX17_GPR     002   \n",
       "1              SnowEx17 Ground Penetrating Radar V002  SNEX17_GPR     002   \n",
       "2              SnowEx17 Ground Penetrating Radar V002  SNEX17_GPR     002   \n",
       "3            ASO L4 Lidar Snow Depth 3m UTM Grid V001   ASO_3M_SD     001   \n",
       "4            ASO L4 Lidar Snow Depth 3m UTM Grid V001   ASO_3M_SD     001   \n",
       "6            ASO L4 Lidar Snow Depth 3m UTM Grid V001   ASO_3M_SD     001   \n",
       "7            ASO L4 Lidar Snow Depth 3m UTM Grid V001   ASO_3M_SD     001   \n",
       "46  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "47  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "48  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "52  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "53  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "54  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "55  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "59  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "60  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "61  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "62  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "63  MODIS/Terra Snow Cover Daily L3 Global 500m SI...     MOD10A1     006   \n",
       "\n",
       "                              producer_granule_id                start_date  \\\n",
       "0                 SnowEx17_GPR_Version2_Week1.csv  2017-02-08T00:00:00.000Z   \n",
       "1                 SnowEx17_GPR_Version2_Week2.csv  2017-02-14T00:00:00.000Z   \n",
       "2                 SnowEx17_GPR_Version2_Week3.csv  2017-02-21T00:00:00.000Z   \n",
       "3                       ASO_3M_SD_USCOGM_20170208  2017-02-08T00:00:00.000Z   \n",
       "4                       ASO_3M_SD_USCOGM_20170216  2017-02-16T00:00:00.000Z   \n",
       "6                       ASO_3M_SD_USCOGM_20170221  2017-02-21T00:00:00.000Z   \n",
       "7                       ASO_3M_SD_USCOGM_20170225  2017-02-25T00:00:00.000Z   \n",
       "46  MOD10A1.A2017039.h09v05.006.2017041102600.hdf  2017-02-08T16:20:00.000Z   \n",
       "47  MOD10A1.A2017040.h09v05.006.2017042102640.hdf  2017-02-09T17:05:00.000Z   \n",
       "48  MOD10A1.A2017041.h09v05.006.2017043095629.hdf  2017-02-10T16:10:00.000Z   \n",
       "52  MOD10A1.A2017045.h09v05.006.2017047103323.hdf  2017-02-14T17:20:00.000Z   \n",
       "53  MOD10A1.A2017046.h09v05.006.2017052213130.hdf  2017-02-15T16:30:00.000Z   \n",
       "54  MOD10A1.A2017047.h09v05.006.2017053103120.hdf  2017-02-16T17:10:00.000Z   \n",
       "55  MOD10A1.A2017048.h09v05.006.2017050103600.hdf  2017-02-17T16:15:00.000Z   \n",
       "59  MOD10A1.A2017052.h09v05.006.2017054100801.hdf  2017-02-21T17:30:00.000Z   \n",
       "60  MOD10A1.A2017053.h09v05.006.2017055094801.hdf  2017-02-22T16:35:00.000Z   \n",
       "61  MOD10A1.A2017054.h09v05.006.2017059063600.hdf  2017-02-23T17:15:00.000Z   \n",
       "62  MOD10A1.A2017055.h09v05.006.2017057092149.hdf  2017-02-24T16:20:00.000Z   \n",
       "63  MOD10A1.A2017056.h09v05.006.2017058092815.hdf  2017-02-25T17:05:00.000Z   \n",
       "\n",
       "                    end_date  \n",
       "0   2017-02-10T23:59:59.000Z  \n",
       "1   2017-02-17T23:59:59.000Z  \n",
       "2   2017-02-25T23:59:59.000Z  \n",
       "3   2017-02-08T23:59:59.000Z  \n",
       "4   2017-02-16T23:59:59.000Z  \n",
       "6   2017-02-21T23:59:59.000Z  \n",
       "7   2017-02-25T23:59:59.000Z  \n",
       "46  2017-02-08T19:40:00.000Z  \n",
       "47  2017-02-09T18:50:00.000Z  \n",
       "48  2017-02-10T19:30:00.000Z  \n",
       "52  2017-02-14T19:05:00.000Z  \n",
       "53  2017-02-15T18:10:00.000Z  \n",
       "54  2017-02-16T18:55:00.000Z  \n",
       "55  2017-02-17T19:35:00.000Z  \n",
       "59  2017-02-21T19:10:00.000Z  \n",
       "60  2017-02-22T18:20:00.000Z  \n",
       "61  2017-02-23T19:00:00.000Z  \n",
       "62  2017-02-24T19:40:00.000Z  \n",
       "63  2017-02-25T18:50:00.000Z  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_df = fn.time_overlap(data_dict)\n",
    "print(len(search_df), ' total files returned')\n",
    "search_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "## Data Access\n",
    "\n",
    "The number of files has been greatly reduced to only those needed to compare data across these data sets. This CMR query will collect the data file URLs, including the associated quality and metadata files if available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.08/SnowEx17_GPR_Version2_Week1.csv',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.08/SnowEx17_GPR_Version2_Week1.csv.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.14/SnowEx17_GPR_Version2_Week2.csv',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.14/SnowEx17_GPR_Version2_Week2.csv.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.21/SnowEx17_GPR_Version2_Week3.csv',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/SNOWEX/SNEX17_GPR.002/2017.02.21/SnowEx17_GPR_Version2_Week3.csv.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_QF_USCOGM_20170208.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_SD_USCOGM_20170208.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.08/ASO_3M_SD_USCOGM_20170208.tif.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_QF_USCOGM_20170216.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_SD_USCOGM_20170216.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.16/ASO_3M_SD_USCOGM_20170216.tif.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_QF_USCOGM_20170221.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_SD_USCOGM_20170221.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.21/ASO_3M_SD_USCOGM_20170221.tif.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_QF_USCOGM_20170225.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_SD_USCOGM_20170225.tif',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP1/ASO/ASO_3M_SD.001/2017.02.25/ASO_3M_SD_USCOGM_20170225.tif.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.08/MOD10A1.A2017039.h09v05.006.2017041102600.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.08/MOD10A1.A2017039.h09v05.006.2017041102600.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.09/MOD10A1.A2017040.h09v05.006.2017042102640.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.09/MOD10A1.A2017040.h09v05.006.2017042102640.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.10/MOD10A1.A2017041.h09v05.006.2017043095629.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.10/MOD10A1.A2017041.h09v05.006.2017043095629.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.14/MOD10A1.A2017045.h09v05.006.2017047103323.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.14/MOD10A1.A2017045.h09v05.006.2017047103323.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.15/MOD10A1.A2017046.h09v05.006.2017052213130.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.15/MOD10A1.A2017046.h09v05.006.2017052213130.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.16/MOD10A1.A2017047.h09v05.006.2017053103120.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.16/MOD10A1.A2017047.h09v05.006.2017053103120.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.17/MOD10A1.A2017048.h09v05.006.2017050103600.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.17/MOD10A1.A2017048.h09v05.006.2017050103600.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.21/MOD10A1.A2017052.h09v05.006.2017054100801.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.21/MOD10A1.A2017052.h09v05.006.2017054100801.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.22/MOD10A1.A2017053.h09v05.006.2017055094801.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.22/MOD10A1.A2017053.h09v05.006.2017055094801.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.23/MOD10A1.A2017054.h09v05.006.2017059063600.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.23/MOD10A1.A2017054.h09v05.006.2017059063600.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.24/MOD10A1.A2017055.h09v05.006.2017057092149.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.24/MOD10A1.A2017055.h09v05.006.2017057092149.hdf.xml',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.25/MOD10A1.A2017056.h09v05.006.2017058092815.hdf',\n",
       " 'https://n5eil01u.ecs.nsidc.org/DP4/MOST/MOD10A1.006/2017.02.25/MOD10A1.A2017056.h09v05.006.2017058092815.hdf.xml']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dictionary with fields needed for CMR url search\n",
    "\n",
    "url_df = search_df.drop(columns=['start_date', 'end_date','version','dataset_id'])\n",
    "url_dict = url_df.to_dict('records')\n",
    "\n",
    "# CMR search variables\n",
    "granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n",
    "headers= {'Accept': 'application/json'}\n",
    "\n",
    "# Create URL list from each df row\n",
    "urls = []\n",
    "for i in range(len(url_dict)):\n",
    "    response = requests.get(granule_search_url, params=url_dict[i], headers=headers)\n",
    "    results = json.loads(response.content)\n",
    "    urls.append(fn.cmr_filter_urls(results))\n",
    "# flatten url list\n",
    "urls = list(np.concatenate(urls))\n",
    "urls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional data access and subsetting services\n",
    "\n",
    "#### API Access\n",
    "Data can be accessed directly from our HTTPS file system through the URLs collected above, or through our Application Programming Interface (API). Our API offers you the ability to order data using specific temporal and spatial filters, as well as subset, reformat, and reproject select data sets. The same subsetting, reformatting, and reprojection services available on select data sets through NASA Earthdata Search can also be applied using this API. These options can be requested in a single access command without the need to script against our data directory structure. See our [programmatic access guide](https://nsidc.org/support/how/how-do-i-programmatically-request-data-services) for more information on API options. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add service request options for MODIS data\n",
    "\n",
    "According to https://nsidc.org/support/faq/what-data-subsetting-reformatting-and-reprojection-services-are-available-for-MODIS-data, we can see that spatial subsetting and GeoTIFF reformatting are available for MOD10A1 so those options are requested below. The area subset must be described as a bounding box, which can be created based on the polygon bounds above. We will also add GeoTIFF reformatting to the MOD10A1 data dictionary and the temporal range will be set based on the range of MOD10A1 files in the dataframe above. These new parameters will be added to the API request below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'short_name': 'MOD10A1', 'version': '6', 'polygon': '-108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165', 'temporal': '2017-02-08T16:20:00.000Z,2017-02-25T18:50:00.000Z', 'page_size': 2000, 'page_num': 1, 'bbox': '-108.2352445938561,38.978765032966244,-107.85284607930835,39.11294532581687', 'format': 'GeoTIFF'}\n"
     ]
    }
   ],
   "source": [
    "bounds = poly.bounds # Get polygon bounds to be used as bounding box input\n",
    "data_dict['modis']['bbox'] = ','.join(map(str, list(bounds))) # Add bounding box subsetting to MODIS dictionary\n",
    "data_dict['modis']['format'] = 'GeoTIFF' # Add geotiff reformatting to MODIS dictionary\n",
    "\n",
    "# Set new temporal range based on dataframe above. Note that this will request all MOD10A1 data falling within this time range.\n",
    "modis_start = min(search_df.loc[search_df['short_name'] == 'MOD10A1', 'start_date'])\n",
    "modis_end = max(search_df.loc[search_df['short_name'] == 'MOD10A1', 'end_date'])\n",
    "data_dict['modis']['temporal'] = ','.join([modis_start,modis_end])\n",
    "print(data_dict['modis'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the data request API endpoint\n",
    "Programmatic API requests are formatted as HTTPS URLs that contain key-value-pairs specifying the service operations that we specified above. We will first create a string of key-value-pairs from our data dictionary and we'll feed those into our API endpoint. This API endpoint can be executed via command line, a web browser, or in Python below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://n5eil02u.ecs.nsidc.org/egi/request?short_name=MOD10A1&version=6&polygon=-108.2352445938561,38.98556907427165,-107.85284607930835,38.978765032966244,-107.85494925720668,39.10596902171742,-108.22772795408136,39.11294532581687,-108.2352445938561,38.98556907427165&temporal=2017-02-08T16:20:00.000Z,2017-02-25T18:50:00.000Z&page_size=2000&page_num=1&bbox=-108.2352445938561,38.978765032966244,-107.85284607930835,39.11294532581687&format=GeoTIFF\n"
     ]
    }
   ],
   "source": [
    "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request' # Set NSIDC data access base URL\n",
    "#data_dict['modis']['request_mode'] = 'stream' # Set the request mode to asynchronous\n",
    "\n",
    "param_string = '&'.join(\"{!s}={!r}\".format(k,v) for (k,v) in data_dict['modis'].items()) # Convert param_dict to string\n",
    "param_string = param_string.replace(\"'\",\"\") # Remove quotes\n",
    "\n",
    "api_request = [f'{base_url}?{param_string}']\n",
    "print(api_request[0]) # Print API base URL + request parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download options\n",
    "\n",
    "The following functions will return the file URLs and the MOD10A1 API request. For demonstration purposes, these functions have been commented out, and instead the data utilized in the following steps will be accessed from a staged directory. ***Note that if you are running this notebook in Binder, the memory may not be sufficient to download these files. Please use the Docker or local Conda options provided in the README if you are interested in downloading all files.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://snowex-aso-modis-tutorial-data/MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif to ./MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif\n",
      "download: s3://snowex-aso-modis-tutorial-data/SnowEx17_GPR_Version2_Week1.csv to ./SnowEx17_GPR_Version2_Week1.csv\n",
      "download: s3://snowex-aso-modis-tutorial-data/ASO_3M_SD_USCOGM_20170208.tif to ./ASO_3M_SD_USCOGM_20170208.tif\n"
     ]
    }
   ],
   "source": [
    "path = str(os.getcwd() + '/Data')\n",
    "if not os.path.exists(path):\n",
    "    os.mkdir(path)\n",
    "os.chdir(path)\n",
    "#fn.cmr_download(urls)\n",
    "#fn.cmr_download(api_request)\n",
    "\n",
    "\n",
    "# pull data from staged bucket for demonstration\n",
    "!aws --no-sign-request s3 cp s3://snowex-aso-modis-tutorial-data/ ./ --recursive #access data in staged directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Read in SnowEx data and buffer points around Snotel location\n",
    "\n",
    "This SnowEx data set is provided in CSV. A [Pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html) is used to easily read in data. For these next steps, just one day's worth of data will be selected from this file and the coincident ASO and MODIS data will be selected.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>collection</th>\n",
       "      <th>trace</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>elev</th>\n",
       "      <th>twtt</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>SWE</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>UTM_Zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2581</td>\n",
       "      <td>-108.066856</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.2</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.692</td>\n",
       "      <td>225</td>\n",
       "      <td>753854.880092</td>\n",
       "      <td>4.325659e+06</td>\n",
       "      <td>12 S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2582</td>\n",
       "      <td>-108.066856</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.2</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.692</td>\n",
       "      <td>225</td>\n",
       "      <td>753854.899385</td>\n",
       "      <td>4.325660e+06</td>\n",
       "      <td>12 S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2583</td>\n",
       "      <td>-108.066856</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.2</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.690</td>\n",
       "      <td>224</td>\n",
       "      <td>753854.918686</td>\n",
       "      <td>4.325660e+06</td>\n",
       "      <td>12 S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2584</td>\n",
       "      <td>-108.066855</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.2</td>\n",
       "      <td>5.86</td>\n",
       "      <td>0.689</td>\n",
       "      <td>224</td>\n",
       "      <td>753854.937987</td>\n",
       "      <td>4.325660e+06</td>\n",
       "      <td>12 S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2585</td>\n",
       "      <td>-108.066855</td>\n",
       "      <td>39.043147</td>\n",
       "      <td>3240.2</td>\n",
       "      <td>5.84</td>\n",
       "      <td>0.686</td>\n",
       "      <td>223</td>\n",
       "      <td>753854.957280</td>\n",
       "      <td>4.325660e+06</td>\n",
       "      <td>12 S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        collection  trace        long        lat    elev  twtt  Thickness  \\\n",
       "0  GPR_0042_020817   2581 -108.066856  39.043146  3240.2  5.89      0.692   \n",
       "1  GPR_0042_020817   2582 -108.066856  39.043146  3240.2  5.89      0.692   \n",
       "2  GPR_0042_020817   2583 -108.066856  39.043146  3240.2  5.87      0.690   \n",
       "3  GPR_0042_020817   2584 -108.066855  39.043146  3240.2  5.86      0.689   \n",
       "4  GPR_0042_020817   2585 -108.066855  39.043147  3240.2  5.84      0.686   \n",
       "\n",
       "   SWE              x             y UTM_Zone  \n",
       "0  225  753854.880092  4.325659e+06     12 S  \n",
       "1  225  753854.899385  4.325660e+06     12 S  \n",
       "2  224  753854.918686  4.325660e+06     12 S  \n",
       "3  224  753854.937987  4.325660e+06     12 S  \n",
       "4  223  753854.957280  4.325660e+06     12 S  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snowex_path = './SnowEx17_GPR_Version2_Week1.csv' # Define local filepath\n",
    "df = pd.read_csv(snowex_path, sep='\\t') \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to time values and extract a single day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The collection date needs to be extracted from the `collection` value and a new dataframe will be generated as a subset of the original based on a single day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>collection</th>\n",
       "      <th>trace</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>elev</th>\n",
       "      <th>twtt</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>SWE</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>UTM_Zone</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2581</td>\n",
       "      <td>-108.066856</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.20</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.692</td>\n",
       "      <td>225</td>\n",
       "      <td>753854.880092</td>\n",
       "      <td>4.325659e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109172</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6360</td>\n",
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       "      <td>39.049202</td>\n",
       "      <td>3248.49</td>\n",
       "      <td>11.49</td>\n",
       "      <td>1.350</td>\n",
       "      <td>439</td>\n",
       "      <td>754148.853700</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109173</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6361</td>\n",
       "      <td>-108.063209</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.56</td>\n",
       "      <td>1.358</td>\n",
       "      <td>441</td>\n",
       "      <td>754148.882549</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109174</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6362</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.62</td>\n",
       "      <td>1.365</td>\n",
       "      <td>444</td>\n",
       "      <td>754148.911407</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109175</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6363</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.64</td>\n",
       "      <td>1.368</td>\n",
       "      <td>445</td>\n",
       "      <td>754148.947466</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             collection  trace        long        lat     elev   twtt  \\\n",
       "0       GPR_0042_020817   2581 -108.066856  39.043146  3240.20   5.89   \n",
       "109172  GPR_0043_020817   6360 -108.063209  39.049202  3248.49  11.49   \n",
       "109173  GPR_0043_020817   6361 -108.063209  39.049202  3248.50  11.56   \n",
       "109174  GPR_0043_020817   6362 -108.063208  39.049202  3248.50  11.62   \n",
       "109175  GPR_0043_020817   6363 -108.063208  39.049202  3248.50  11.64   \n",
       "\n",
       "        Thickness  SWE              x             y UTM_Zone       date  \n",
       "0           0.692  225  753854.880092  4.325659e+06     12 S 2017-02-08  \n",
       "109172      1.350  439  754148.853700  4.326342e+06     12 S 2017-02-08  \n",
       "109173      1.358  441  754148.882549  4.326342e+06     12 S 2017-02-08  \n",
       "109174      1.365  444  754148.911407  4.326342e+06     12 S 2017-02-08  \n",
       "109175      1.368  445  754148.947466  4.326342e+06     12 S 2017-02-08  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = df.collection.str.rsplit('_').str[-1].astype(str)\n",
    "df.date = pd.to_datetime(df.date, format=\"%m%d%y\")\n",
    "df = df.sort_values(['date'])\n",
    "df_subset = df[df['date'] == '2017-02-08'] # subset original dataframe and only select this date\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert to Geopandas dataframe to provide point geometry\n",
    "\n",
    "According to the SnowEx documentation, the data are available in UTM Zone 12N so we'll set to this projection so that we can buffer in meters in the next step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collection</th>\n",
       "      <th>trace</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>elev</th>\n",
       "      <th>twtt</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>SWE</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>UTM_Zone</th>\n",
       "      <th>date</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GPR_0042_020817</td>\n",
       "      <td>2581</td>\n",
       "      <td>-108.066856</td>\n",
       "      <td>39.043146</td>\n",
       "      <td>3240.20</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.692</td>\n",
       "      <td>225</td>\n",
       "      <td>753854.880092</td>\n",
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       "      <td>2017-02-08</td>\n",
       "      <td>POINT (753854.880 4325659.484)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109172</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6360</td>\n",
       "      <td>-108.063209</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.49</td>\n",
       "      <td>11.49</td>\n",
       "      <td>1.350</td>\n",
       "      <td>439</td>\n",
       "      <td>754148.853700</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (754148.854 4326341.915)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109173</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6361</td>\n",
       "      <td>-108.063209</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.56</td>\n",
       "      <td>1.358</td>\n",
       "      <td>441</td>\n",
       "      <td>754148.882549</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (754148.883 4326341.916)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109174</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6362</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.62</td>\n",
       "      <td>1.365</td>\n",
       "      <td>444</td>\n",
       "      <td>754148.911407</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (754148.911 4326341.917)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109175</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6363</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.64</td>\n",
       "      <td>1.368</td>\n",
       "      <td>445</td>\n",
       "      <td>754148.947466</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (754148.947 4326341.918)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             collection  trace        long        lat     elev   twtt  \\\n",
       "0       GPR_0042_020817   2581 -108.066856  39.043146  3240.20   5.89   \n",
       "109172  GPR_0043_020817   6360 -108.063209  39.049202  3248.49  11.49   \n",
       "109173  GPR_0043_020817   6361 -108.063209  39.049202  3248.50  11.56   \n",
       "109174  GPR_0043_020817   6362 -108.063208  39.049202  3248.50  11.62   \n",
       "109175  GPR_0043_020817   6363 -108.063208  39.049202  3248.50  11.64   \n",
       "\n",
       "        Thickness  SWE              x             y UTM_Zone       date  \\\n",
       "0           0.692  225  753854.880092  4.325659e+06     12 S 2017-02-08   \n",
       "109172      1.350  439  754148.853700  4.326342e+06     12 S 2017-02-08   \n",
       "109173      1.358  441  754148.882549  4.326342e+06     12 S 2017-02-08   \n",
       "109174      1.365  444  754148.911407  4.326342e+06     12 S 2017-02-08   \n",
       "109175      1.368  445  754148.947466  4.326342e+06     12 S 2017-02-08   \n",
       "\n",
       "                              geometry  \n",
       "0       POINT (753854.880 4325659.484)  \n",
       "109172  POINT (754148.854 4326341.915)  \n",
       "109173  POINT (754148.883 4326341.916)  \n",
       "109174  POINT (754148.911 4326341.917)  \n",
       "109175  POINT (754148.947 4326341.918)  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_utm= gpd.GeoDataFrame(df_subset, geometry=gpd.points_from_xy(df_subset.x, df_subset.y), crs='EPSG:32612')\n",
    "gdf_utm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Buffer data around SNOTEL site"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further subset the SnowEx snow depth data to get within a 500 m radius of the [SNOTEL Mesa Lakes](https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=622&state=co) site.\n",
    "\n",
    "First we'll create a new geodataframe with the SNOTEL site location, set to our SnowEx UTM coordinate reference system, and create a 500 meter buffer around this point. Then we'll subset the SnowEx points to the buffer and convert back to the WGS84 CRS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create another geodataframe (gdfsel) with the center point for the selection\n",
    "df_snotel = pd.DataFrame(\n",
    "    {'SNOTEL Site': ['Mesa Lakes'],\n",
    "     'Latitude': [39.05],\n",
    "     'Longitude': [-108.067]})\n",
    "gdf_snotel = gpd.GeoDataFrame(df_snotel, geometry=gpd.points_from_xy(df_snotel.Longitude, df_snotel.Latitude), crs='EPSG:4326')\n",
    "\n",
    "gdf_snotel.to_crs('EPSG:32612', inplace=True) # set CRS to UTM 12 N\n",
    "\n",
    "buffer = gdf_snotel.buffer(500) #create 500 m buffer\n",
    "\n",
    "gdf_buffer = gdf_utm.loc[gdf_utm.geometry.within(buffer.unary_union)] # subset dataframe to buffer region\n",
    "gdf_buffer = gdf_buffer.to_crs('EPSG:4326')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Read in Airborne Snow Observatory data and clip to SNOTEL buffer\n",
    "\n",
    "Snow depth data from the ASO L4 Lidar Snow Depth 3m UTM Grid data set were calculated from surface elevation measured by the Riegl LMS-Q1560 airborne laser scanner (ALS). The data are provided in GeoTIFF format, so we'll use the [Rasterio](https://rasterio.readthedocs.io/en/latest/) library to read in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "aso_path = './ASO_3M_SD_USCOGM_20170208.tif' # Define local filepath\n",
    "\n",
    "aso = rasterio.open(aso_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clip data to SNOTEL buffer\n",
    "\n",
    "In order to reduce the data volume to the buffered region of interest, we can subset this GeoTIFF to the same SNOTEL buffer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "buffer = buffer.to_crs(crs=aso.crs) # convert buffer to CRS of ASO rasterio object\n",
    "out_img, out_transform = mask(aso, buffer, crop=True)\n",
    "out_meta = aso.meta.copy()\n",
    "epsg_code = int(aso.crs.data['init'][5:])\n",
    "out_meta.update({\"driver\": \"GTiff\", \"height\": out_img.shape[1], \"width\": out_img.shape[2], \"transform\": out_transform, \"crs\": '+proj=utm +zone=13 +datum=WGS84 +units=m +no_defs'})\n",
    "out_tif = 'clipped_ASO_3M_SD_USCOGM_20170208.tif'\n",
    "\n",
    "with rasterio.open(out_tif, 'w', **out_meta) as dest:\n",
    "    dest.write(out_img)\n",
    "    \n",
    "clipped_aso = rasterio.open(out_tif)\n",
    "aso_array = clipped_aso.read(1, masked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___ \n",
    "## Read in MODIS Snow Cover data \n",
    "\n",
    "We are interested in the Normalized Difference Snow Index (NDSI) snow cover value from the MOD10A1 data set, which is an index that is related to the presence of snow in a pixel. According to the [MOD10A1 FAQ](https://nsidc.org/support/faq/what-ndsi-snow-cover-and-how-does-it-compare-fsc), snow cover is detected using the NDSI ratio of the difference in visible reflectance (VIS) and shortwave infrared reflectance (SWIR), where NDSI = ((band 4-band 6) / (band 4 + band 6)).\n",
    "\n",
    "Note that you may need to change this filename output below if you download the data outside of the staged bucket, as the output names may vary per request. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "modis_path = './MOD10A1_A2017039_h09v05_006_2017041102600_MOD_Grid_Snow_500m_NDSI_Snow_Cover_99f6ee91_subsetted.tif' # Define local filepath\n",
    "modis = rasterio.open(modis_path)\n",
    "modis_array = modis.read(1, masked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Add ASO and MODIS data to GeoPandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to add data from these ASO and MODIS gridded data sets, we need to define the geometry parameters for theses, as well as the SnowEx data. The SnowEx geometry is set using the longitude and latitude values of the geodataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "snowex geometry:  Shape: (26516,)\n",
      "Lons: [-108.063209   -108.06320867 -108.06320833 ... -108.06127167 -108.061267\n",
      " -108.06214585]\n",
      "Lats: [39.04920167 39.04920167 39.04920167 ... 39.04973833 39.04973658\n",
      " 39.05015724]\n"
     ]
    }
   ],
   "source": [
    "snowex_geometry = prs.geometry.SwathDefinition(lons=gdf_buffer['long'], lats=gdf_buffer['lat'])\n",
    "print('snowex geometry: ', snowex_geometry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With ASO and MODIS data on regular grids, we can create area definitions for these using projection and extent metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'count': 1,\n",
      " 'crs': CRS.from_epsg(32613),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'float32',\n",
      " 'height': 334,\n",
      " 'interleave': 'band',\n",
      " 'nodata': -9999.0,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(3.0, 0.0, 234081.0,\n",
      "       0.0, -3.0, 4327305.0),\n",
      " 'width': 335}\n",
      "\n",
      "BoundingBox(left=234081.0, bottom=4326303.0, right=235086.0, top=4327305.0)\n",
      "{'compress': 'deflate',\n",
      " 'count': 1,\n",
      " 'crs': CRS.from_wkt('PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,0,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]'),\n",
      " 'driver': 'GTiff',\n",
      " 'dtype': 'uint8',\n",
      " 'height': 34,\n",
      " 'interleave': 'band',\n",
      " 'nodata': None,\n",
      " 'tiled': False,\n",
      " 'transform': Affine(463.3127165279165, 0.0, -9356136.99756175,\n",
      "       0.0, -463.3127165279165, 4349579.782763082),\n",
      " 'width': 110}\n",
      "\n",
      "BoundingBox(left=-9356136.99756175, bottom=4333827.150401132, right=-9305172.598743679, top=4349579.782763082)\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(clipped_aso.profile)\n",
    "print('')\n",
    "print(clipped_aso.bounds)\n",
    "\n",
    "\n",
    "pprint.pprint(modis.profile)\n",
    "print('')\n",
    "print(modis.bounds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create area definition for ASO\n",
    "area_id = 'UTM_13N' # area_id: ID of area\n",
    "description = 'WGS 84 / UTM zone 13N' # description: Description\n",
    "proj_id = 'UTM_13N' # proj_id: ID of projection (being deprecated)\n",
    "projection = 'EPSG:32613' # projection: Proj4 parameters as a dict or string\n",
    "width = clipped_aso.width # width: Number of grid columns\n",
    "height = clipped_aso.height # height: Number of grid rows\n",
    "area_extent = (234081.0, 4326303.0, 235086.0, 4327305.0)\n",
    "aso_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)\n",
    "\n",
    "# Create area definition for MODIS\n",
    "area_id = 'Sinusoidal' # area_id: ID of area\n",
    "description = 'Sinusoidal Modis Spheroid' # description: Description\n",
    "proj_id = 'Sinusoidal' # proj_id: ID of projection (being deprecated)\n",
    "projection = 'PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]' # projection: Proj4 parameters as a dict or string\n",
    "width = modis.width # width: Number of grid columns\n",
    "height = modis.height # height: Number of grid rows\n",
    "area_extent = (-9332971.361735353, 4341240.1538655795, -9331118.110869242, 4343093.404731691)\n",
    "modis_geometry = prs.geometry.AreaDefinition(area_id, description, proj_id, projection, width, height, area_extent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate ASO and MODIS values onto SnowEx points\n",
    "\n",
    "To interpolate ASO snow depth and MODIS snow cover data to SnowEx snow depth points, we can use the `pyresample` library. The `radius_of_influence` parameter determines maximum radius to look for nearest neighbor interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>collection</th>\n",
       "      <th>trace</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>elev</th>\n",
       "      <th>twtt</th>\n",
       "      <th>Thickness</th>\n",
       "      <th>SWE</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>UTM_Zone</th>\n",
       "      <th>date</th>\n",
       "      <th>geometry</th>\n",
       "      <th>aso_snow_depth</th>\n",
       "      <th>modis_ndsi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109172</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6360</td>\n",
       "      <td>-108.063209</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.49</td>\n",
       "      <td>11.49</td>\n",
       "      <td>1.350</td>\n",
       "      <td>439</td>\n",
       "      <td>754148.853700</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (-108.06321 39.04920)</td>\n",
       "      <td>1.302658</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109173</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6361</td>\n",
       "      <td>-108.063209</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.56</td>\n",
       "      <td>1.358</td>\n",
       "      <td>441</td>\n",
       "      <td>754148.882549</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (-108.06321 39.04920)</td>\n",
       "      <td>1.302658</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109174</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6362</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.62</td>\n",
       "      <td>1.365</td>\n",
       "      <td>444</td>\n",
       "      <td>754148.911407</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (-108.06321 39.04920)</td>\n",
       "      <td>1.302658</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109175</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6363</td>\n",
       "      <td>-108.063208</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.64</td>\n",
       "      <td>1.368</td>\n",
       "      <td>445</td>\n",
       "      <td>754148.947466</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (-108.06321 39.04920)</td>\n",
       "      <td>1.302658</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109176</th>\n",
       "      <td>GPR_0043_020817</td>\n",
       "      <td>6364</td>\n",
       "      <td>-108.063207</td>\n",
       "      <td>39.049202</td>\n",
       "      <td>3248.50</td>\n",
       "      <td>11.68</td>\n",
       "      <td>1.372</td>\n",
       "      <td>446</td>\n",
       "      <td>754148.983533</td>\n",
       "      <td>4.326342e+06</td>\n",
       "      <td>12 S</td>\n",
       "      <td>2017-02-08</td>\n",
       "      <td>POINT (-108.06321 39.04920)</td>\n",
       "      <td>1.302658</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             collection  trace        long        lat     elev   twtt  \\\n",
       "109172  GPR_0043_020817   6360 -108.063209  39.049202  3248.49  11.49   \n",
       "109173  GPR_0043_020817   6361 -108.063209  39.049202  3248.50  11.56   \n",
       "109174  GPR_0043_020817   6362 -108.063208  39.049202  3248.50  11.62   \n",
       "109175  GPR_0043_020817   6363 -108.063208  39.049202  3248.50  11.64   \n",
       "109176  GPR_0043_020817   6364 -108.063207  39.049202  3248.50  11.68   \n",
       "\n",
       "        Thickness  SWE              x             y UTM_Zone       date  \\\n",
       "109172      1.350  439  754148.853700  4.326342e+06     12 S 2017-02-08   \n",
       "109173      1.358  441  754148.882549  4.326342e+06     12 S 2017-02-08   \n",
       "109174      1.365  444  754148.911407  4.326342e+06     12 S 2017-02-08   \n",
       "109175      1.368  445  754148.947466  4.326342e+06     12 S 2017-02-08   \n",
       "109176      1.372  446  754148.983533  4.326342e+06     12 S 2017-02-08   \n",
       "\n",
       "                           geometry  aso_snow_depth  modis_ndsi  \n",
       "109172  POINT (-108.06321 39.04920)        1.302658          71  \n",
       "109173  POINT (-108.06321 39.04920)        1.302658          71  \n",
       "109174  POINT (-108.06321 39.04920)        1.302658          71  \n",
       "109175  POINT (-108.06321 39.04920)        1.302658          71  \n",
       "109176  POINT (-108.06321 39.04920)        1.302658          71  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add ASO values to geodataframe\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # ignore warning when resampling to a different projection\n",
    "gdf_buffer['aso_snow_depth'] = prs.kd_tree.resample_nearest(aso_geometry, aso_array, snowex_geometry, radius_of_influence=3)\n",
    "\n",
    "# add MODIS values to geodataframe\n",
    "gdf_buffer['modis_ndsi'] = prs.kd_tree.resample_nearest(modis_geometry, modis_array, snowex_geometry, radius_of_influence=500)\n",
    "\n",
    "gdf_buffer.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___ \n",
    "## Visualize data and export for further GIS analysis\n",
    "\n",
    "The rasterio plot module allows you to directly plot GeoTIFFs objects. The SnowEx `Thickness` values are plotted against the clipped ASO snow depth raster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf_buffer_aso_crs = gdf_buffer.to_crs('EPSG:32613') \n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "show(clipped_aso, ax=ax)\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
    "gdf_buffer_aso_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
    "                        {'label': \"Snow Depth (m)\",});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same for MOD10A1: This was subsetted to the entire Grand Mesa region defined by the SnowEx data set coverage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set dataframe to MOD10A1 Sinusoidal projection\n",
    "gdf_buffer_modis_crs = gdf_buffer.to_crs('PROJCS[\"Sinusoidal Modis Spheroid\",GEOGCS[\"Unknown datum based upon the Authalic Sphere\",DATUM[\"Not_specified_based_on_Authalic_Sphere\",SPHEROID[\"Sphere\",6371007.181,887203.3395236016,AUTHORITY[\"EPSG\",\"7035\"]],AUTHORITY[\"EPSG\",\"6035\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433],AUTHORITY[\"EPSG\",\"4035\"]],PROJECTION[\"Sinusoidal\"],PARAMETER[\"longitude_of_center\",0],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]]]')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "show(modis, ax=ax)\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # fit legend to height of plot\n",
    "gdf_buffer_modis_crs.plot(column='Thickness', ax=ax, cmap='OrRd', legend=True, cax=cax, legend_kwds=\n",
    "                        {'label': \"Snow Depth (m)\",});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional data imagery services\n",
    "\n",
    "#### NASA Worldview and the Global Browse Imagery Service\n",
    "\n",
    "NASA’s EOSDIS Worldview mapping application provides the capability to interactively browse over 900 global, full-resolution satellite imagery layers and then download the underlying data. Many of the available imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks “right now.\"\n",
    "\n",
    "According to the [MOD10A1 landing page](https://nsidc.org/data/mod10a1), snow cover imagery layers from this data set are available through NASA Worldview. This layer can be downloaded as various image files including GeoTIFF using the snapshot feature at the top right of the page. This link presents the MOD10A1 NDSI layer over our time and area of interest: https://go.nasa.gov/35CgYMd. \n",
    "\n",
    "Additionally, the NASA Global Browse Imagery Service provides up to date, full resolution imagery for select NSIDC DAAC data sets as web services including WMTS, WMS, KML, and more. These layers can be accessed in GIS applications following guidance on the [GIBS documentation pages](https://wiki.earthdata.nasa.gov/display/GIBS/Geographic+Information+System+%28GIS%29+Usage). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export dataframe to Shapefile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the dataframe can be exported as an Esri shapefile for further analysis in GIS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_buffer = gdf_buffer.drop(columns=['date'])\n",
    "gdf_buffer.to_file('snow-data-20170208.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}