vhroda_training_offline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/HYPERNETS/hypernets_training/blob/main/vhroda_training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q--XLoo4Z325"
   },
   "source": [
    "## **VHRODA training session: LANDHYPERNET and RADCALNET over Gobabeb**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4pxZ7fqqZ6jS"
   },
   "source": [
    "### **Introduction**\n",
    "\n",
    "short-url: https://shorturl.at/8HtX1\n",
    "\n",
    "Within this training session, we will run through processing and comparing a bottom-of-atmosphere (BOA) Landsat-9 (L9) matchup and a top-of-atmosphere (TOA) Sentinel-2 (S2) matchup with both HYPERNETS and RadCalNet (both networks providing data for the same overpass) over the Gobabeb site in Namibia. \n",
    "Both these matchups were taken from the list of matchups identified in De Vis et al. (2024; https://doi.org/10.3389/frsen.2024.1323998).\n",
    "\n",
    "We will assume a starting point where the matchup has already been identified and the files have already been downloaded. Both matchups are in June 2022.\n",
    "As part of this notebook, we will read in the RadCalNet and HYPERNETS data, mask poor-quality data, and process the data to be comparible with L9 and S2 (e.g. spectral band integration). The bias will be calculated together with their uncertainties and plotted. These uncertainties will come from the product files themselves, and will be propagated throughout the processing chain using the CoMet toolkit. The CoMet toolkit will also be used to handle flags and propagate uncertainties for the HYPERNETS products. We will run through the BOA L9 example in group. Then, users can run through the TOA S2 example individually following the example provided in this notebook. Further matchup can then be processed by the users themselves if time allows. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MzrTVRTqaNE3"
   },
   "source": [
    "We first install the obsarray package (flag handling and accessing uncertainties), the punpy package (uncertainty propagation) and the matheo package (for band integration)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also import all the relevant python packages we use in this training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from punpy import MeasurementFunction, MCPropagation\n",
    "from matheo.band_integration import band_integration\n",
    "from obsarray.templater.dataset_util import DatasetUtil\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we clone the hypernets_training repository, which contains all the datasets used in this training. Some instructions on downloading LANDHYPERNET and RadCalNet data will be provided at the end of this notebook.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BOA L9 matchup**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will compare some BOA Landsat-9 data to HYPERNETS and RadCalNet. The chosen Landsat-9 product is LC09_L2SP_179076_20220606_20230414_02_T1, which is a tile over Gobabeb from 2022-06-06. Rather than providing the full tile and a reader, we have pre-extracted the region of interest (here taken to be a 200 by 200m box centred on the Gobabeb HYPERNETS and Gobabeb RadCalNet mast, and make this available as a netcdf file on the hypernets_training repository. This data can be read in as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_refl_L9_hyp = xr.load_dataset(\"example_L9_20220606.nc\")     # ROI centred on HYPERNETS\n",
    "ds_refl_L9_rcn = xr.load_dataset(\"example_L9_20220606_rcn.nc\") # ROI centred on RadCalNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From these we can determine the reflectance in each band from the mean over the ROI, and its uncertainty (combination of noise and spatial variability) from the standard deviation between pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bands_L9 = [\"B1\", \"B2\", \"B3\", \"B4\", \"B5\", \"B6\"]\n",
    "wav_L9 = [442.98244284, 482.58889933, 561.33224557, 654.60554515, 864.5708545, 1609.09056245]\n",
    "\n",
    "refl_L9_hyp = np.array([np.mean(ds_refl_L9_hyp[band].values) for band in bands_L9])\n",
    "u_refl_L9_hyp = np.array([np.std(ds_refl_L9_hyp[band].values) for band in bands_L9])\n",
    "\n",
    "refl_L9_rcn = np.array([np.mean(ds_refl_L9_rcn[band].values) for band in bands_L9])\n",
    "u_refl_L9_rcn = np.array([np.std(ds_refl_L9_rcn[band].values) for band in bands_L9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we open the HYPERNETS L2A dataset (surface reflectance without site-specific quality checks). These data were downloaded from the LANDHYPERNET data portal (https://landhypernet.org.uk). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pdv\\.conda\\envs\\hypernets_training\\Lib\\site-packages\\xarray\\namedarray\\core.py:264: UserWarning: Duplicate dimension names present: dimensions {'wavelength'} appear more than once in dims=('wavelength', 'wavelength'). We do not yet support duplicate dimension names, but we do allow initial construction of the object. We recommend you rename the dims immediately to become distinct, as most xarray functionality is likely to fail silently if you do not. To rename the dimensions you will need to set the ``.dims`` attribute of each variable, ``e.g. var.dims=('x0', 'x1')``.\n",
      "  self._dims = self._parse_dimensions(dims)\n"
     ]
    }
   ],
   "source": [
    "ds_HYP = xr.open_dataset(\"HYPERNETS_L_GHNA_L2A_REF_20220606T0900_20231226T1435_v2.0.nc\")  # read digital effects table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, to illustrate the LANDHYPERNET data, we make a spectral plot for a specific viewing geometry (), and a polar polot for a specific wavelength (550nm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pdv\\AppData\\Local\\Temp\\ipykernel_12792\\55870767.py:48: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  refl_2d[i, ii] = np.abs(refl[id_wav, id_series])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_illustrative_hypernets(dataset, wavelength, series =22):\n",
    "        # first plot the spectrum for an example geometry:\n",
    "\n",
    "        fig = plt.figure(figsize=(15,6))\n",
    "        ax = plt.subplot(121)\n",
    "        ax.plot(dataset[\"wavelength\"].values, dataset[\"reflectance\"].values[:,series], \n",
    "                label = \"HYPERNETS spectrum at vza=%s, vaa=%s\"%(dataset[\"viewing_zenith_angle\"].values[series],dataset[\"viewing_azimuth_angle\"].values[series]))\n",
    "        hyp_unc = dataset[\"reflectance\"].values[:,series] /100 * np.sqrt(dataset[\"u_rel_random_reflectance\"].values[:,series]**2+dataset[\"u_rel_systematic_reflectance\"].values[:,series]**2)\n",
    "        ax.fill_between(\n",
    "          dataset[\"wavelength\"].values, dataset[\"reflectance\"].values[:,series] - hyp_unc, dataset[\"reflectance\"].values[:,series] + hyp_unc, alpha=0.2, color=\"b\"\n",
    "        )\n",
    "        ax.legend()\n",
    "        ax.set_ylim(0,0.6)\n",
    "\n",
    "\n",
    "        #next make a polar plot for given wavelength\n",
    "\n",
    "\n",
    "        saa = np.mean(dataset.solar_azimuth_angle.values % 360)\n",
    "        sza = np.mean(dataset.solar_zenith_angle.values)\n",
    "        vaa = dataset.viewing_azimuth_angle.values % 360\n",
    "\n",
    "        vza = dataset.viewing_zenith_angle.values\n",
    "        refl = dataset.reflectance.values\n",
    "\n",
    "        vaa_grid = np.arange(8, 368, 15)\n",
    "        vza_grid = np.array([0, 5, 10, 20, 30, 40, 50, 60])\n",
    "        raa_grid = vaa_grid - saa\n",
    "\n",
    "        id_wav = np.argmin(np.abs(wavelength - dataset.wavelength.values))\n",
    "\n",
    "        vaa_mesh, vza_mesh = np.meshgrid(np.radians(vaa_grid), vza_grid)\n",
    "\n",
    "        refl_2d = np.zeros((len(vaa_grid), len(vza_grid)))\n",
    "        for i in range(len(vaa_grid)):\n",
    "            for ii in range(len(vza_grid)):\n",
    "                id_series = np.where(\n",
    "                    (\n",
    "                        np.abs(vaa - vaa_grid[i])\n",
    "                        < 2\n",
    "                    )\n",
    "                    & (\n",
    "                        np.abs(vza - vza_grid[ii])\n",
    "                        < 2\n",
    "                    )\n",
    "                )[0]\n",
    "                if len(id_series) == 1:\n",
    "                    refl_2d[i, ii] = np.abs(refl[id_wav, id_series])\n",
    "                elif len(id_series) > 1:\n",
    "                    print(\n",
    "                        \"There are multiple series that match the same vaa (%s) and vza (%s) \"\n",
    "                        \"within a tolerance of %s and %s degrees respectively.\"\n",
    "                        % (\n",
    "                            vaa_grid[i],\n",
    "                            vza_grid[ii],\n",
    "                            2,\n",
    "                            2,\n",
    "                        )\n",
    "                    )\n",
    "                    refl_2d[i, ii] = np.mean(np.abs(refl[id_wav, id_series]))\n",
    "        refl_2d[refl_2d == 0] = np.nan\n",
    "        ax2 = plt.subplot(122, projection='polar')\n",
    "        ax2.set_theta_direction(-1)\n",
    "        ax2.set_theta_offset(np.pi / 2.0)\n",
    "        im = ax2.pcolormesh(\n",
    "            vaa_mesh,\n",
    "            vza_mesh,\n",
    "            refl_2d.T,\n",
    "            shading=\"auto\",\n",
    "            cmap=plt.get_cmap(\"jet\"),\n",
    "        )\n",
    "\n",
    "        ax2.plot(np.radians(saa), sza, color=\"k\", ls=\"none\", marker=\"o\")\n",
    "\n",
    "        cbar = fig.colorbar(im)\n",
    "        cbar.set_label(\"reflectance at %s nm\" % wavelength, rotation=270, labelpad=15)\n",
    "\n",
    "        plt.show()\n",
    "        plt.close(fig)\n",
    "\n",
    "plot_illustrative_hypernets(ds_HYP, 550, series=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the data available in this dataset. As shown in the dimensions of the dataset, the LANDHYPERNETS products samples at 1551 wavelengths and 44 different geometries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 21MB\n",
       "Dimensions:                          (wavelength: 1551, series: 44)\n",
       "Coordinates:\n",
       "  * wavelength                       (wavelength) float64 12kB 350.3 ... 1.68...\n",
       "Dimensions without coordinates: series\n",
       "Data variables: (12/18)\n",
       "    bandwidth                        (wavelength) float64 12kB ...\n",
       "    viewing_azimuth_angle            (series) float64 352B 263.0 278.0 ... 83.0\n",
       "    pointing_azimuth_angle           (series) float64 352B ...\n",
       "    viewing_zenith_angle             (series) float64 352B 60.0 60.0 ... 60.0\n",
       "    solar_azimuth_angle              (series) float64 352B 33.37 33.32 ... 31.72\n",
       "    solar_zenith_angle               (series) float64 352B 54.1 54.07 ... 53.26\n",
       "    ...                               ...\n",
       "    n_valid_scans                    (series) uint8 44B ...\n",
       "    n_total_scans                    (series) uint8 44B ...\n",
       "    n_valid_scans_SWIR               (series) uint8 44B ...\n",
       "    n_total_scans_SWIR               (series) uint8 44B ...\n",
       "    reflectance                      (wavelength, series) float32 273kB 0.111...\n",
       "    std_reflectance                  (wavelength, series) float32 273kB ...\n",
       "Attributes: (12/47)\n",
       "    type:                             dataset\n",
       "    abstract:                         The HYPERNETS project (Horizon 2020 res...\n",
       "    conventions:                      CFv72, NVS2.0\n",
       "    format_version:                   v2.0\n",
       "    netcdf_version:                   1.6\n",
       "    processor_name:                   hypernets_processor\n",
       "    ...                               ...\n",
       "    creator_name:                     Pieter De Vis\n",
       "    creator_email:                    pieter.de.vis@npl.co.uk\n",
       "    title:                            HYPSTAR Land network dataset of spectra...\n",
       "    instrument_calibration_file_rad:  HYPERNETS_CAL_HYPSTAR_220261_RAD_v2.0.nc\n",
       "    instrument_calibration_file_irr:  HYPERNETS_CAL_HYPSTAR_220261_IRR_v2.0.nc\n",
       "    product_level:                    L_L2A</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af1562ab-7d83-4815-a03a-7769421c7cbf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af1562ab-7d83-4815-a03a-7769421c7cbf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>wavelength</span>: 1551</li><li><span>series</span>: 44</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9813768-8d50-44ad-af77-dcbba4acd531' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9813768-8d50-44ad-af77-dcbba4acd531' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>wavelength</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>350.3 350.7 ... 1.681e+03 1.684e+03</div><input id='attrs-ee87fe0d-469f-44b3-b4d0-6708e28c6499' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee87fe0d-469f-44b3-b4d0-6708e28c6499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7920931d-6bfa-4e5e-9ebd-7d56029baa85' class='xr-var-data-in' type='checkbox'><label for='data-7920931d-6bfa-4e5e-9ebd-7d56029baa85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 350.272939,  350.737361,  351.201847, ..., 1678.76451 , 1681.328189,\n",
       "       1683.886317])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d52c082-2d77-4e28-8185-a03b46c92284' class='xr-section-summary-in' type='checkbox'  ><label for='section-5d52c082-2d77-4e28-8185-a03b46c92284' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bandwidth</span></div><div class='xr-var-dims'>(wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f83d1cb9-7f18-4c9d-a02a-4b7a074ec342' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f83d1cb9-7f18-4c9d-a02a-4b7a074ec342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-915b0fe0-4dd1-42a7-a6cd-66e742dc26a8' class='xr-var-data-in' type='checkbox'><label for='data-915b0fe0-4dd1-42a7-a6cd-66e742dc26a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1551 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_azimuth_angle</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>263.0 278.0 293.0 ... 98.0 83.0</div><input id='attrs-176a1c7a-821f-4ba9-b3d3-1c8987e691d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-176a1c7a-821f-4ba9-b3d3-1c8987e691d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc5f62eb-8b45-43a2-82b9-fc9be71e1ed5' class='xr-var-data-in' type='checkbox'><label for='data-dc5f62eb-8b45-43a2-82b9-fc9be71e1ed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>viewing_azimuth_angle</dd><dt><span>long_name :</span></dt><dd>viewing_azimuth_angle is the horizontal angle between the line of sight from the observation point to the sensor and True North. The angle is measured clockwise positive, starting from the North direction.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>reference :</span></dt><dd>True North</dd><dt><span>preferred_symbol :</span></dt><dd>vaa</dd></dl></div><div class='xr-var-data'><pre>array([262.999 , 277.9975, 293.0015, 293.0015, 277.9975, 262.999 , 262.999 ,\n",
       "       277.9975, 293.0015, 277.9975, 293.0015, 293.0015, 277.9975, 262.999 ,\n",
       "       262.999 , 277.9975, 293.0015, 293.0015, 277.9975, 262.999 , 262.999 ,\n",
       "       277.9975, 293.0015, 112.9975,  97.999 ,  83.0005,  83.0005,  97.999 ,\n",
       "       112.9975, 112.9975,  97.999 ,  83.0005,  83.0005,  97.999 , 112.9975,\n",
       "       112.9975,  97.999 ,  83.0005,  83.0005,  97.999 , 112.9975, 112.9975,\n",
       "        97.999 ,  83.0005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pointing_azimuth_angle</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27eece37-4575-4acc-ac1a-1801268eefff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27eece37-4575-4acc-ac1a-1801268eefff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f26adda-4ba8-4798-af39-4bf44812d4df' class='xr-var-data-in' type='checkbox'><label for='data-8f26adda-4ba8-4798-af39-4bf44812d4df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>pointing_azimuth_angle</dd><dt><span>long_name :</span></dt><dd>pointing_azimuth_angle is the horizontal angle between the direction the sensor is pointing to, and True North. The angle is measured clockwise positive, starting from True North direction. This angle is 180 degrees different from the viewing_azimuth_angle.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>reference :</span></dt><dd>True North</dd><dt><span>preferred_symbol :</span></dt><dd>vaa</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viewing_zenith_angle</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>60.0 60.0 60.0 ... 60.0 60.0 60.0</div><input id='attrs-fc668efe-43f5-41c7-8278-66eb8575869b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc668efe-43f5-41c7-8278-66eb8575869b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1680eaa-0bd0-4371-876f-6e939bd66779' class='xr-var-data-in' type='checkbox'><label for='data-d1680eaa-0bd0-4371-876f-6e939bd66779' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>viewing_zenith_angle</dd><dt><span>long_name :</span></dt><dd>viewing_zenith_angle is the angle between the local zenith and the direction from the location being measuredto the sensor. This angle is measured starting from directly overhead and its range is from zero (directly overhead the observation target, i.e. sensor looking down) to 180 degrees (directly below the observation target, i.e. sensorpointing at local zenith). Local zenith is a line perpendicular to the Earth&#x27;s surface at a given location. &#x27;Observation target&#x27; means a location on the Earth defined by the sensor performing the observations.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>preferred_symbol :</span></dt><dd>vza</dd></dl></div><div class='xr-var-data'><pre>array([60.0012, 60.0012, 60.0012, 49.9996, 49.9996, 49.9996, 40.0008, 40.0008,\n",
       "       40.0008, 29.9992, 29.9992, 20.0004, 20.0004, 20.0004,  9.9988,  9.9988,\n",
       "        9.9988,  5.0008,  5.0008,  5.0008,  0.    ,  0.    ,  0.    ,  5.0008,\n",
       "        5.0008,  5.0008,  9.9988,  9.9988,  9.9988, 20.0004, 20.0004, 20.0004,\n",
       "       29.9992, 29.9992, 29.9992, 40.0008, 40.0008, 40.0008, 49.9996, 49.9996,\n",
       "       49.9996, 60.0012, 60.0012, 60.0012])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_azimuth_angle</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>33.37 33.32 33.29 ... 31.79 31.72</div><input id='attrs-f1a333b0-c418-4329-979c-1a733fb9eb19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1a333b0-c418-4329-979c-1a733fb9eb19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95bccf17-56d3-4fd1-ba4b-c20f0430f7b5' class='xr-var-data-in' type='checkbox'><label for='data-95bccf17-56d3-4fd1-ba4b-c20f0430f7b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>solar_azimuth_angle</dd><dt><span>long_name :</span></dt><dd>Solar azimuth angle is the horizontal angle between the line of sight to the sun and a reference direction which is often due north. The angle is measured clockwise.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>reference :</span></dt><dd>True North</dd><dt><span>preferred_symbol :</span></dt><dd>saa</dd></dl></div><div class='xr-var-data'><pre>array([33.3685, 33.3245, 33.2915, 33.253 , 33.22  , 33.1815, 33.1375, 33.1045,\n",
       "       33.0715, 33.0385, 33.0055, 32.9725, 32.9395, 32.8845, 32.8405, 32.7965,\n",
       "       32.7525, 32.703 , 32.659 , 32.626 , 32.5985, 32.5655, 32.5105, 32.4775,\n",
       "       32.4445, 32.4115, 32.3785, 32.3455, 32.318 , 32.285 , 32.241 , 32.208 ,\n",
       "       32.1695, 32.131 , 32.098 , 32.0705, 32.021 , 31.977 , 31.944 , 31.9055,\n",
       "       31.8725, 31.8285, 31.79  , 31.724 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solar_zenith_angle</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>54.1 54.07 54.05 ... 53.29 53.26</div><input id='attrs-290fc7f3-33bb-40b2-a0f9-340f62d2cf2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-290fc7f3-33bb-40b2-a0f9-340f62d2cf2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a44fd5a4-8448-41cc-adda-28c48f269657' class='xr-var-data-in' type='checkbox'><label for='data-a44fd5a4-8448-41cc-adda-28c48f269657' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>solar_zenith_angle</dd><dt><span>long_name :</span></dt><dd>Solar zenith angle is the the angle between the line of sight to the sun and the local vertical.</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>preferred_symbol :</span></dt><dd>sza</dd></dl></div><div class='xr-var-data'><pre>array([54.096 , 54.0708, 54.054 , 54.0316, 54.0148, 53.9952, 53.9728, 53.956 ,\n",
       "       53.9392, 53.9196, 53.9028, 53.886 , 53.8692, 53.8412, 53.8188, 53.7964,\n",
       "       53.774 , 53.7488, 53.7264, 53.7096, 53.6956, 53.6788, 53.6508, 53.634 ,\n",
       "       53.6172, 53.6004, 53.5836, 53.5668, 53.55  , 53.536 , 53.5136, 53.4996,\n",
       "       53.4772, 53.4604, 53.4436, 53.4268, 53.4044, 53.382 , 53.3652, 53.3456,\n",
       "       53.3316, 53.3092, 53.2896, 53.2588])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acquisition_time</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd3da573-452b-4da7-8863-d9f66c4f0ee2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd3da573-452b-4da7-8863-d9f66c4f0ee2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4a9ae75-95df-46c1-9085-6eb4a49fdc96' class='xr-var-data-in' type='checkbox'><label for='data-e4a9ae75-95df-46c1-9085-6eb4a49fdc96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Acquisition time in seconds since 1970-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>series_id</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a294a58a-067c-4f69-a05f-70fdf75eb48d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a294a58a-067c-4f69-a05f-70fdf75eb48d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7670b316-803f-4340-b62f-cc2aff59397f' class='xr-var-data-in' type='checkbox'><label for='data-7670b316-803f-4340-b62f-cc2aff59397f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>series_id</dd><dt><span>long_name :</span></dt><dd>Series id number</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quality_flag</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85f1b7bf-c115-4f48-8060-2a1dff030d39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85f1b7bf-c115-4f48-8060-2a1dff030d39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11dbacd2-161b-4a67-b627-7521ac8b7868' class='xr-var-data-in' type='checkbox'><label for='data-11dbacd2-161b-4a67-b627-7521ac8b7868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>long_name :</span></dt><dd>A variable with the standard name of quality_flag contains an indication of assessed quality information of another data variable. The linkage between the data variable and the variable or variables with the standard_name of quality_flag is achieved using the ancillary_variables attribute.</dd><dt><span>flag_meanings :</span></dt><dd>lon_default lat_default pt_ref_invalid bad_pointing outliers L0_thresholds L0_discontinuity dark_masked half_of_scans_masked not_enough_dark_scans not_enough_rad_scans not_enough_irr_scans series_missing vza_irradiance no_clear_sky_irradiance variable_irradiance half_of_uncertainties_too_big single_irradiance_used no_clear_sky_sequence discontinuity_VNIR_SWIR</dd><dt><span>flag_masks :</span></dt><dd>1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_rel_random_reflectance</span></div><div class='xr-var-dims'>(wavelength, series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.585 2.172 2.43 ... 0.123 0.1767</div><input id='attrs-8065d10d-4f1d-4017-9b76-8eabfbd3665a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8065d10d-4f1d-4017-9b76-8eabfbd3665a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d47aa12-69ac-4f76-ad54-b0ac50392770' class='xr-var-data-in' type='checkbox'><label for='data-3d47aa12-69ac-4f76-ad54-b0ac50392770' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>u_rel_random_reflectance</dd><dt><span>long_name :</span></dt><dd>Random reflectance relative uncertainty</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>err_corr_1_dim :</span></dt><dd>wavelength</dd><dt><span>err_corr_1_form :</span></dt><dd>random</dd><dt><span>err_corr_1_units :</span></dt><dd>[]</dd><dt><span>err_corr_1_params :</span></dt><dd>[]</dd><dt><span>err_corr_2_dim :</span></dt><dd>series</dd><dt><span>err_corr_2_form :</span></dt><dd>random</dd><dt><span>err_corr_2_units :</span></dt><dd>[]</dd><dt><span>err_corr_2_params :</span></dt><dd>[]</dd><dt><span>pdf_shape :</span></dt><dd>gaussian</dd></dl></div><div class='xr-var-data'><pre>array([[1.585464, 2.172025, 2.430348, ..., 1.282247, 2.720982, 2.635314],\n",
       "       [2.315224, 2.209267, 2.257052, ..., 1.801559, 3.5128  , 3.258078],\n",
       "       [2.054942, 2.721922, 1.988944, ..., 1.826591, 4.94715 , 3.667572],\n",
       "       ...,\n",
       "       [0.153205, 0.265068, 0.135733, ..., 0.119976, 0.142856, 0.167338],\n",
       "       [0.169896, 0.204654, 0.176897, ..., 0.110046, 0.182621, 0.143648],\n",
       "       [0.204326, 0.243267, 0.159306, ..., 0.147931, 0.123001, 0.176716]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_rel_systematic_reflectance</span></div><div class='xr-var-dims'>(wavelength, series)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.145 3.263 3.37 ... 2.97 2.895</div><input id='attrs-688d5fe1-8fad-4792-8265-4379c58f109b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-688d5fe1-8fad-4792-8265-4379c58f109b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3379bde9-9a40-429a-b9ed-0af363a544bc' class='xr-var-data-in' type='checkbox'><label for='data-3379bde9-9a40-429a-b9ed-0af363a544bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>u_rel_systematic_reflectance</dd><dt><span>long_name :</span></dt><dd>Systematic reflectance relative uncertainty</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>err_corr_1_dim :</span></dt><dd>wavelength</dd><dt><span>err_corr_1_form :</span></dt><dd>err_corr_matrix</dd><dt><span>err_corr_1_units :</span></dt><dd>[]</dd><dt><span>err_corr_1_params :</span></dt><dd>err_corr_systematic_reflectance</dd><dt><span>err_corr_2_dim :</span></dt><dd>series</dd><dt><span>err_corr_2_form :</span></dt><dd>systematic</dd><dt><span>err_corr_2_units :</span></dt><dd>[]</dd><dt><span>err_corr_2_params :</span></dt><dd>[]</dd><dt><span>pdf_shape :</span></dt><dd>gaussian</dd></dl></div><div class='xr-var-data'><pre>array([[3.145317, 3.26342 , 3.369721, ..., 3.352831, 3.363646, 3.181394],\n",
       "       [3.034877, 2.816083, 3.21381 , ..., 3.248977, 3.471454, 2.740887],\n",
       "       [3.445619, 3.15755 , 3.733618, ..., 3.496878, 3.553479, 3.248603],\n",
       "       ...,\n",
       "       [2.422641, 2.676366, 3.115834, ..., 2.596653, 2.877841, 2.776424],\n",
       "       [2.503367, 2.73488 , 3.094458, ..., 2.633078, 2.833504, 2.799853],\n",
       "       [2.547203, 2.626583, 3.179456, ..., 2.694985, 2.970439, 2.894754]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>err_corr_systematic_reflectance</span></div><div class='xr-var-dims'>(wavelength, wavelength)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04f11df0-118c-4b51-b571-88a661573dae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04f11df0-118c-4b51-b571-88a661573dae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-267cc29d-c192-4d2b-b3b9-70c6fdcfdc69' class='xr-var-data-in' type='checkbox'><label for='data-267cc29d-c192-4d2b-b3b9-70c6fdcfdc69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>err_corr_systematic_reflectance</dd><dt><span>long_name :</span></dt><dd>Correlation matrix of systematic reflectance uncertainty</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[2405601 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_valid_scans</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-257a717a-cdfd-4cde-bb0d-93bfddff8fbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-257a717a-cdfd-4cde-bb0d-93bfddff8fbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3392aba-cb19-4378-b0e5-481c6af86790' class='xr-var-data-in' type='checkbox'><label for='data-f3392aba-cb19-4378-b0e5-481c6af86790' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>number of valid scans used in average of VNIR</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_total_scans</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1d8a9ae-c1ff-4ac8-8f33-cc6cf13ee6f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d8a9ae-c1ff-4ac8-8f33-cc6cf13ee6f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b0d0fb-f0cd-4236-99ab-d775147cd3c4' class='xr-var-data-in' type='checkbox'><label for='data-e8b0d0fb-f0cd-4236-99ab-d775147cd3c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>number of total radiance scans acquired for VNIR</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_valid_scans_SWIR</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b77644ab-523f-4c25-8fff-c12f5763cbce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b77644ab-523f-4c25-8fff-c12f5763cbce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f552349-ac1c-46d9-800a-4cbb0127662a' class='xr-var-data-in' type='checkbox'><label for='data-4f552349-ac1c-46d9-800a-4cbb0127662a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>number of valid scans used in average of SWIR</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_total_scans_SWIR</span></div><div class='xr-var-dims'>(series)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ecee4d5-e2be-4677-ba0d-0e29e9496ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ecee4d5-e2be-4677-ba0d-0e29e9496ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9061d228-e65f-4b1b-9ea1-164896b1596d' class='xr-var-data-in' type='checkbox'><label for='data-9061d228-e65f-4b1b-9ea1-164896b1596d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>number of total radiance scans acquired for SWIR</dd><dt><span>long_name :</span></dt><dd></dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[44 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reflectance</span></div><div class='xr-var-dims'>(wavelength, series)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1113 0.1094 ... 0.5204 0.5611</div><input id='attrs-8dfe491e-bd2c-4040-ab63-46ce1a378d96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dfe491e-bd2c-4040-ab63-46ce1a378d96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2962932e-d6d8-40f8-baf5-691268678397' class='xr-var-data-in' type='checkbox'><label for='data-2962932e-d6d8-40f8-baf5-691268678397' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>reflectance</dd><dt><span>long_name :</span></dt><dd>The surface called surface means the lower boundary of the atmosphere. Bidirectional_reflectance depends onthe angles of incident and measuredradiation.Reflectance is the ratio of the energy of the reflected to the incident radiation. A coordinate variable of radiation_wavelength or radiation_frequency can be used to specify the wavelength or frequency, respectively, of the radiation.</dd><dt><span>units :</span></dt><dd>-</dd><dt><span>unc_comps :</span></dt><dd>[&#x27;u_rel_random_reflectance&#x27;, &#x27;u_rel_systematic_reflectance&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([[0.111346, 0.109448, 0.10843 , ..., 0.114247, 0.11591 , 0.118011],\n",
       "       [0.108654, 0.107639, 0.106455, ..., 0.112556, 0.112782, 0.118171],\n",
       "       [0.110181, 0.107906, 0.106557, ..., 0.112109, 0.113783, 0.116939],\n",
       "       ...,\n",
       "       [0.515468, 0.49595 , 0.51111 , ..., 0.512316, 0.52068 , 0.560899],\n",
       "       [0.514862, 0.494416, 0.510639, ..., 0.513002, 0.521172, 0.561584],\n",
       "       [0.513782, 0.492361, 0.509783, ..., 0.510407, 0.520393, 0.561072]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>std_reflectance</span></div><div class='xr-var-dims'>(wavelength, series)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ac7ef7a-4687-4b62-af6d-7617f6ec636f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ac7ef7a-4687-4b62-af6d-7617f6ec636f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e712a4d5-ed2c-49a6-8ba4-bd5e96c56e42' class='xr-var-data-in' type='checkbox'><label for='data-e712a4d5-ed2c-49a6-8ba4-bd5e96c56e42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>standard deviation reflectance</dd><dt><span>long_name :</span></dt><dd>standard deviation on surface reflectance that is due to the variability in radiance (i.e. not accounting for variability in darks or in irradiance)</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><pre>[68244 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0476805-6414-4b5e-969f-6a6725c89d2d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d0476805-6414-4b5e-969f-6a6725c89d2d' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>wavelength</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5052c4a6-170a-4bf6-9f64-e18558fef8d6' class='xr-index-data-in' type='checkbox'/><label for='index-5052c4a6-170a-4bf6-9f64-e18558fef8d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([350.27293897134416, 350.73736059086343, 351.20184670719345,\n",
       "       351.66639728894694,  352.1310123046434, 352.59569172270903,\n",
       "       353.06043551147684,  353.5252436391864,  353.9901160739842,\n",
       "        354.4550527839234,\n",
       "       ...\n",
       "       1660.6641746001692, 1663.2664270212792,  1665.863199829345,\n",
       "       1668.4544827412567, 1671.0402654806248, 1673.6205377777787,\n",
       "       1676.1952893697671, 1678.7645100003588, 1681.3281894200404,\n",
       "         1683.88631738602],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;wavelength&#x27;, length=1551))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb533d49-715a-4f38-a3f6-f0d59df85aa8' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb533d49-715a-4f38-a3f6-f0d59df85aa8' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>type :</span></dt><dd>dataset</dd><dt><span>abstract :</span></dt><dd>The HYPERNETS project (Horizon 2020 research and innovation, grant agreement No 775983) has the overall aim to provide high quality in situ measurements tosupport the (visible/SWIR) optical Copernicus products. Therefore a new multi-head hyperspectral spectroradiometer dedicated to land and water surface reflectance validation with instrument pointing capabilities and embedded calibration device has been established. The instrument has been deployed at 24 sites covering a range of water and land types and a range of climatic andlogistic conditions (www.hypernets.eu).</dd><dt><span>conventions :</span></dt><dd>CFv72, NVS2.0</dd><dt><span>format_version :</span></dt><dd>v2.0</dd><dt><span>netcdf_version :</span></dt><dd>1.6</dd><dt><span>processor_name :</span></dt><dd>hypernets_processor</dd><dt><span>processor_version :</span></dt><dd>2.0</dd><dt><span>processor_atbd :</span></dt><dd>https://hypernets-processor.readthedocs.io/en/latest/</dd><dt><span>product_name :</span></dt><dd>HYPERNETS_L_GHNA_L2A_REF_20220606T0900_20231226T1435_v2.0</dd><dt><span>data_created :</span></dt><dd>12/26/2023, 14:37:42</dd><dt><span>references :</span></dt><dd>https://hypernets-processor.readthedocs.io/en/latest/</dd><dt><span>source :</span></dt><dd>surface observation</dd><dt><span>comment :</span></dt><dd>Any free-format text is appropriate.</dd><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>acknowledgement :</span></dt><dd>HYPERNETS project is funded by Horizon 2020 research and innovation program, Grand Agreement No 775993. Consortium of project  of the Hypernets test sites, .... are greatly acknowledged.</dd><dt><span>project_name :</span></dt><dd>H2020 HYPERNETS GN 775993</dd><dt><span>language :</span></dt><dd>English</dd><dt><span>operational_status :</span></dt><dd>operational</dd><dt><span>limitations :</span></dt><dd>no limitations to public access</dd><dt><span>licence :</span></dt><dd>Attribution-NonCommercial-NoDerivs CC BY-NC-ND</dd><dt><span>conformity :</span></dt><dd>notEvaluated</dd><dt><span>lineage :</span></dt><dd>Quality assured following www.hypernets.eu/docs/QC/</dd><dt><span>instrument_configuration_file :</span></dt><dd>TBD</dd><dt><span>history :</span></dt><dd>TBD</dd><dt><span>processor_configuration_file :</span></dt><dd>TBD</dd><dt><span>sequence_id :</span></dt><dd>SEQ20220606T090044</dd><dt><span>system_id :</span></dt><dd>HYPSTAR_220261</dd><dt><span>site_id :</span></dt><dd>GHNA</dd><dt><span>site_latitude :</span></dt><dd>-23.60153</dd><dt><span>site_longitude :</span></dt><dd>15.12589</dd><dt><span>source_file :</span></dt><dd>SEQ20220606T090044</dd><dt><span>system_temperature :</span></dt><dd>NaN</dd><dt><span>system_pressure :</span></dt><dd>NaN</dd><dt><span>illuminance :</span></dt><dd>NaN</dd><dt><span>system_relative_humidity :</span></dt><dd>NaN</dd><dt><span>instrument_id :</span></dt><dd>220261</dd><dt><span>topic_category :</span></dt><dd>land, environment, geoscientific information</dd><dt><span>keyword :</span></dt><dd>Environmental monitoring Facilities (INSPIRE Spatial Data Theme), reflectance (http://aims.fao.org/aos/agrovoc/c_28538), optical properties(http://aims.fao.org/aos/agrovoc/c_5371), vegetation (http://www.eionet.europa.eu/gemet/concept/8922)</dd><dt><span>locator :</span></dt><dd>https://github.com/HYPERNETS/hypernets_processor/</dd><dt><span>responsible_party :</span></dt><dd>National Physical Laboratory (NPL), UK</dd><dt><span>creator_name :</span></dt><dd>Pieter De Vis</dd><dt><span>creator_email :</span></dt><dd>pieter.de.vis@npl.co.uk</dd><dt><span>title :</span></dt><dd>HYPSTAR Land network dataset of spectral surface reflectance</dd><dt><span>instrument_calibration_file_rad :</span></dt><dd>HYPERNETS_CAL_HYPSTAR_220261_RAD_v2.0.nc</dd><dt><span>instrument_calibration_file_irr :</span></dt><dd>HYPERNETS_CAL_HYPSTAR_220261_IRR_v2.0.nc</dd><dt><span>product_level :</span></dt><dd>L_L2A</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 21MB\n",
       "Dimensions:                          (wavelength: 1551, series: 44)\n",
       "Coordinates:\n",
       "  * wavelength                       (wavelength) float64 12kB 350.3 ... 1.68...\n",
       "Dimensions without coordinates: series\n",
       "Data variables: (12/18)\n",
       "    bandwidth                        (wavelength) float64 12kB ...\n",
       "    viewing_azimuth_angle            (series) float64 352B 263.0 278.0 ... 83.0\n",
       "    pointing_azimuth_angle           (series) float64 352B ...\n",
       "    viewing_zenith_angle             (series) float64 352B 60.0 60.0 ... 60.0\n",
       "    solar_azimuth_angle              (series) float64 352B 33.37 33.32 ... 31.72\n",
       "    solar_zenith_angle               (series) float64 352B 54.1 54.07 ... 53.26\n",
       "    ...                               ...\n",
       "    n_valid_scans                    (series) uint8 44B ...\n",
       "    n_total_scans                    (series) uint8 44B ...\n",
       "    n_valid_scans_SWIR               (series) uint8 44B ...\n",
       "    n_total_scans_SWIR               (series) uint8 44B ...\n",
       "    reflectance                      (wavelength, series) float32 273kB 0.111...\n",
       "    std_reflectance                  (wavelength, series) float32 273kB ...\n",
       "Attributes: (12/47)\n",
       "    type:                             dataset\n",
       "    abstract:                         The HYPERNETS project (Horizon 2020 res...\n",
       "    conventions:                      CFv72, NVS2.0\n",
       "    format_version:                   v2.0\n",
       "    netcdf_version:                   1.6\n",
       "    processor_name:                   hypernets_processor\n",
       "    ...                               ...\n",
       "    creator_name:                     Pieter De Vis\n",
       "    creator_email:                    pieter.de.vis@npl.co.uk\n",
       "    title:                            HYPSTAR Land network dataset of spectra...\n",
       "    instrument_calibration_file_rad:  HYPERNETS_CAL_HYPSTAR_220261_RAD_v2.0.nc\n",
       "    instrument_calibration_file_irr:  HYPERNETS_CAL_HYPSTAR_220261_IRR_v2.0.nc\n",
       "    product_level:                    L_L2A"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_HYP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the L2A dataset, there is a quality flag variable, which contains quality flags indicating warnings about the data quality. For further information on how to handle these flags, we refer to the following jupyter notebook: https://colab.research.google.com/github/comet-toolkit/comet_training/blob/main/hypernets_surface_reflectance.ipynb . Below, we list the quality flags set in the current dataset (one list for each geometry)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['outliers', 'series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['outliers', 'series_missing'], ['series_missing'], ['outliers', 'series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['series_missing'], ['outliers', 'series_missing'], ['series_missing']]\n"
     ]
    }
   ],
   "source": [
    "print([DatasetUtil.get_set_flags(flag) for flag in ds_HYP[\"quality_flag\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data does have a few warnings (especially the missing series - which indicates at least one geometry from the standard sequence is not present in the data), but none of these are an issue for the current usecase. Some other products do contain more significant quality flags, for which we recommend not using this data for Cal/Val if the flag is raised as highlighted below. We next remove all data that has these \"bad\" quality flags set. In the near future, when the data is released publicly, the L2B data will be released, which will already have all these data with \"bad\" quality flags omitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "bad_flags = [\n",
    "    \"pt_ref_invalid\",\n",
    "    \"half_of_scans_masked\",\n",
    "    \"not_enough_dark_scans\",\n",
    "    \"not_enough_rad_scans\",\n",
    "    \"not_enough_irr_scans\",\n",
    "    \"no_clear_sky_irradiance\",\n",
    "    \"variable_irradiance\",\n",
    "    \"half_of_uncertainties_too_big\",\n",
    "    \"discontinuity_VNIR_SWIR\",\n",
    "    \"single_irradiance_used\",\n",
    "]\n",
    "flagged = DatasetUtil.get_flags_mask_or(ds_HYP[\"quality_flag\"], bad_flags)\n",
    "id_series_valid = np.where(~flagged)[0]\n",
    "ds_HYP = ds_HYP.isel(series=id_series_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that for the dataset being used here, this does not remove any data. \n",
    "Once we have removed the flagged data, we select the geometry (i.e. series dimension) which most closely matches the satellite viewing angles (which were taken from the satellite data at the region of interest)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vza_L9 = 0.7\n",
    "#vaa_L9 = 38.1\n",
    "vzadiff = ds_HYP[\"viewing_zenith_angle\"].values - vza_L9\n",
    "#vaadiff = np.abs(ds_HYP[\"viewing_azimuth_angle\"].values - vaa_L9 % 360)\n",
    "angledif_series = np.sqrt(vzadiff**2) # + vaadiff**2)\n",
    "id_series = np.where(angledif_series < 5)[0]\n",
    "ds_HYP = ds_HYP.isel(series=id_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not select on viewing azimuth angle here, as for nadir measurements there is only a single measurement.\n",
    "We do here give a word of caution around using the LANDHYPERNET nadir measurements as these are prone to shadowing. \n",
    "Measurements affected by shadowing will be removed in the L2B files, but are still present in L2A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most relevant variables from the xarray dataset are extracted as numpy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_HYP_full = ds_HYP[\"wavelength\"].values\n",
    "refl_HYP_full = np.mean(ds_HYP[\"reflectance\"].values,axis=1)\n",
    "u_ran_refl_HYP_full = refl_HYP_full * np.mean(ds_HYP[\"u_rel_random_reflectance\"].values,axis=1) / 100\n",
    "u_sys_refl_HYP_full = refl_HYP_full * np.mean(ds_HYP[\"u_rel_systematic_reflectance\"].values,axis=1) / 100\n",
    "u_refl_HYP_full = np.sqrt(u_ran_refl_HYP_full**2+u_sys_refl_HYP_full**2)  # add random and systematic uncertainties for later plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The LANDHYPERNET data has a Gaussian spectral response function (SRF) with a width of 3nm for the VNIR sensor (<1000nm) and 10nm for the SWIR sensor (>1000nm). In order to be comparable with the satellite data, we need to spectrally integrate this data to the SRF of the L9 OLI sensor. We here provide a function that performs this band integration using the matheo tool, and returns the first six L9 bands, which we are including in this comparison. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def band_integrate_L9(reflectance, wavelength):\n",
    "    \"\"\"\n",
    "    Function to perform L9 band integration on reflectance\n",
    "\n",
    "    :param reflectance: reflectance spectrum\n",
    "    :param wavelength: wavelengths\n",
    "    \"\"\"\n",
    "    refl_band, band_centres = band_integration.spectral_band_int_sensor(\n",
    "        d=reflectance,\n",
    "        wl=wavelength,\n",
    "        platform_name=\"Landsat-8\",  # the pyspectral library does not contain the L9 SRF, so we use L8 instead which is very similar.\n",
    "        sensor_name=\"OLI\",\n",
    "        u_d=None,\n",
    "    )\n",
    "    return refl_band[:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use this function to perform the band integration, and also propagate the uncertainties through this process. The punpy tool from the CoMet toolkit is used for the uncertainty propagation. The random and systematic uncertainty are propagated separately, as it is important to correctly take into account the spectral error correlation when propagting these uncertainties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pdv\\.conda\\envs\\hypernets_training\\Lib\\site-packages\\matheo\\band_integration\\band_integration.py:369: UserWarning: The x_r coordinate range (1516.0-1697.0) exceeds the range of the x coordinate range (350.27293897134416-1683.88631738602), and the band integration can thus not be applied.\n",
      "  warnings.warn(\n",
      "C:\\Users\\pdv\\.conda\\envs\\hypernets_training\\Lib\\site-packages\\comet_maths\\linear_algebra\\matrix_calculation.py:274: UserWarning: One of the correlation matrices is not positive definite. It has been slightly changed (maximum difference of 0.044887128380837416) to accomodate our method.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "refl_HYP = band_integrate_L9(refl_HYP_full,wav_HYP_full)\n",
    "\n",
    "prop = MCPropagation(50, parallel_cores=1)\n",
    "u_ran_refl_HYP = prop.propagate_standard(band_integrate_L9,[refl_HYP_full,wav_HYP_full],[u_ran_refl_HYP_full,None],[\"rand\",None])\n",
    "u_sys_refl_HYP = prop.propagate_standard(band_integrate_L9,[refl_HYP_full,wav_HYP_full],[u_sys_refl_HYP_full,None],[ds_HYP[\"err_corr_systematic_reflectance\"].values,None])\n",
    "u_refl_HYP = np.sqrt(u_ran_refl_HYP**2+u_sys_refl_HYP**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that spectrally, the systematic component is not 100% correlated (but it is fully correlated between repeated measurements/different geometries), but instead an spectral error correlation matrix is provided with the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ds_HYP[\"err_corr_systematic_reflectance\"].values)\n",
    "plt.show()\n",
    "plt.close() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare these band integrated uncertainties to the full resolution uncertainties, and see how they are affected differently by the process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wav_HYP_full,u_ran_refl_HYP_full,\"red\",label=\"random uncertainty full resolution\")\n",
    "plt.plot(wav_HYP_full,u_sys_refl_HYP_full,\"blue\",label=\"systematic uncertainty full resolution\")\n",
    "#plt.plot(wav_rcn,u_refl_rcn,\"green\",label=\"random uncertainty RCN\")\n",
    "plt.plot(wav_L9,u_ran_refl_HYP,\"ro\",label=\"random uncertainty band integrated\")\n",
    "plt.plot(wav_L9,u_sys_refl_HYP,\"bo\",label=\"systematic uncertainty band integrated\")\n",
    "#plt.plot(wav_L9,u_refl_rcn_band,\"go\",label=\"random uncertainty band integrated RCN\")\n",
    "plt.yscale(\"log\") \n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.close()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we read in the RadCalNet data. The BOA data is provided in the .input files that can be downloaded from the RadCalNet portal (https://www.radcalnet.org/). These are ascii files which provide the relevant reflectance and atmospheric data and uncertainties (NetCDF files are also availble). We here extract the reflectance and it's uncertainty for 9:00 UTC (which is column with index 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "rcn_data = np.genfromtxt(\"GONA01_2022_157_v00.09.input\", dtype=\"str\", delimiter=\"\\t\", skip_header=4)\n",
    "if np.all(rcn_data[:, -1] == \"\"):\n",
    "    rcn_data = rcn_data[:, :-1]\n",
    "wav_rcn = rcn_data[12:223, 0].astype(float)\n",
    "times_rcn = rcn_data[2, 1:]\n",
    "\n",
    "refl_rcn = rcn_data[12:223, 3].astype(float)\n",
    "u_refl_rcn = np.abs(rcn_data[229:440, 3].astype(float))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RadCalNet data has a triangular SRF with 10nm width. These also have to be band integrated to the L9 OLI SRF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "refl_rcn_band = band_integrate_L9(refl_rcn,wav_rcn)\n",
    "u_refl_rcn_band = prop.propagate_standard(band_integrate_L9,[refl_rcn,wav_rcn],[u_refl_rcn,None],[\"rand\",None])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1:** For which of the networks does the uncertainty in the random uncertainty component reduce most by the band integration? (either look at the data, add RadCalNet to the plot showing the HYPERNETS uncertainties, or think about this theoretically.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The HYPERNETS and RadCalNet data are now at the same spectral scale as the L9 data. All the data is also near simultaneous, in a similar viewing geometry and over a homogeneous surface. So the data is now sufficiently consistent to perform the comparison. We calculate the bias between the satellite and reference as follows, and also calculate the uncertainties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bias_hyp = ((refl_L9_hyp / refl_HYP) - 1) * 100\n",
    "u_bias_hyp = np.sqrt((u_refl_L9_hyp / refl_L9_hyp) ** 2 + (u_refl_HYP / refl_HYP) ** 2) * 100\n",
    "\n",
    "bias_rcn = ((refl_L9_rcn / refl_rcn_band) - 1) * 100\n",
    "u_bias_rcn = (\n",
    "    np.sqrt((u_refl_L9_rcn / refl_L9_rcn) ** 2 + (u_refl_rcn_band / refl_rcn_band) ** 2)\n",
    "    * 100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot these biases and uncertainties, as well as the reflectances themselves. We first define a function to make this plot, and then run this for the BOA matchup between Landsat-9 and LANDHYPERNET & RadCalNet on the 6th of June 2022:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(\n",
    "    sat: str,\n",
    "    date: str,\n",
    "    sat_wav: np.ndarray,\n",
    "    sat_refl: np.ndarray,\n",
    "    sat_unc: np.ndarray,\n",
    "    hyp_wav: np.ndarray,\n",
    "    hyp_refl: np.ndarray,\n",
    "    hyp_unc: np.ndarray,\n",
    "    rcn_wav: np.ndarray,\n",
    "    rcn_refl: np.ndarray,\n",
    "    rcn_unc: np.ndarray,\n",
    "    bias: np.ndarray,\n",
    "    bias_unc: np.ndarray,\n",
    "    wavs_band: np.ndarray,\n",
    "    reflectance_band: np.ndarray,\n",
    "    reflectance_band_unc: np.ndarray,\n",
    "    vza: float,\n",
    "    bias_rcn: np.ndarray,\n",
    "    bias_rcn_unc: np.ndarray,\n",
    "):\n",
    "    \"\"\"\n",
    "    Function to plot matchup results\n",
    "    \n",
    "    :param sat: satellite name\n",
    "    :param date: date of matchup\n",
    "    :param sat_wav: satellite wavelength\n",
    "    :param sat_refl: satellite reflectance \n",
    "    :param sat_unc: satellite reflectance uncertainty\n",
    "    :param hyp_wav: hypernets (full resolution) wavelength\n",
    "    :param hyp_refl: hypernets reflectance\n",
    "    :param hyp_unc: hypernets reflectance uncertainty\n",
    "    :param rcn_wav: RadCalNet wavelength\n",
    "    :param rcn_refl: RadCalNet reflectance\n",
    "    :param rcn_unc:  RadCalNet reflectance uncertainty\n",
    "    :param bias: hypernets bias (for satellite bands)\n",
    "    :param bias_unc: hypernets bias uncertainty (for satellite bands)\n",
    "    :param wavs_band: band-integrated hypernets wavelength\n",
    "    :param reflectance_band: band-integrated hypernets reflectance\n",
    "    :param reflectance_band_unc: band-integrated hypernets reflectance uncertainty\n",
    "    :param vza: satellite viewing zenith angle\n",
    "    :param bias_rcn: RadCalNet bias (for satellite bands)\n",
    "    :param bias_rcn_unc: RadCalNet bias uncertainty (for satellite bands)\n",
    "    :return: \n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(20, 12))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.errorbar(\n",
    "        wavs_band,\n",
    "        reflectance_band,\n",
    "        yerr=reflectance_band_unc,\n",
    "        fmt=\"o\",\n",
    "        ls=\"none\",\n",
    "        ms=10,\n",
    "        color=\"m\",\n",
    "        label=\"HYPERNETS for satellite bands\",\n",
    "    )\n",
    "    plt.errorbar(\n",
    "        sat_wav, sat_refl, yerr=sat_unc, fmt=\"o\", ls=\"none\", ms=10, color=\"g\", label=sat\n",
    "    )\n",
    "    plt.fill_between(\n",
    "        hyp_wav, hyp_refl - hyp_unc, hyp_refl + hyp_unc, alpha=0.3, color=\"b\"\n",
    "    )\n",
    "    plt.errorbar(rcn_wav, rcn_refl, yerr=rcn_unc, label=\"RadCalNet\", color=\"orange\")\n",
    "    plt.plot(hyp_wav, hyp_refl, \"-b\", label=\"HYPERNETS full-resolution model\")\n",
    "    if sat == \"Landsat-8\" or sat == \"Landsat-9\":\n",
    "        plt.title(\n",
    "            \"Landsat-8/9 (vza=%.1f) vs HYPERNETS TOA Comparison at %s\" % (vza, date),\n",
    "            fontsize=20,\n",
    "        )\n",
    "    else:\n",
    "        plt.title(\n",
    "            \"%s (vza=%.1f) vs HYPERNETS Comparison at %s\" % (sat, vza, date),\n",
    "            fontsize=20,\n",
    "        )\n",
    "    plt.ylabel(\"Reflectance\", fontsize=20)\n",
    "    plt.xlim(380, 1700)\n",
    "    plt.ylim(0.0, 0.6)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.legend(loc=2, numpoints=1, scatterpoints=1, facecolor=\"white\")\n",
    "\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.errorbar(\n",
    "        sat_wav,\n",
    "        bias,\n",
    "        yerr=bias_unc,\n",
    "        fmt=\"o\",\n",
    "        mfc=\"blue\",\n",
    "        ls=\"none\",\n",
    "        ms=15,\n",
    "        capsize=3,\n",
    "        label=\"HYPERNETS-%s bias\" % sat,\n",
    "    )\n",
    "    plt.errorbar(\n",
    "        sat_wav,\n",
    "        bias_rcn,\n",
    "        yerr=bias_rcn_unc,\n",
    "        fmt=\"o\",\n",
    "        mfc=\"orange\",\n",
    "        ls=\"none\",\n",
    "        ms=15,\n",
    "        capsize=3,\n",
    "        alpha=0.5,\n",
    "        label=\"RadCalNet-%s bias\" % sat,\n",
    "    )\n",
    "    plt.axhline(y=0, color=\"r\", linestyle=\"--\")\n",
    "    plt.ylabel(\"Relative Difference (%)\", fontsize=20)\n",
    "    plt.xlabel(\"Wavelength (nm)\", fontsize=20)\n",
    "    plt.xlim(380, 1700)\n",
    "    plt.ylim(-10, 10)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.legend()\n",
    "    # plt.legend(loc=2, numpoints=1, scatterpoints=1, facecolor='white')\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "plot(\"LANDSAT 9\", \"2022-06-06\", wav_L9, refl_L9_hyp, u_refl_L9_hyp, wav_HYP_full, refl_HYP_full, u_refl_HYP_full, wav_rcn,\n",
    "     refl_rcn, u_refl_rcn, bias_hyp, u_bias_hyp, wav_L9, refl_HYP, u_refl_HYP, vza_L9, bias_rcn, u_bias_rcn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2:** Within the shown examples, we have propagated some uncertainty components, but there are many others missing. Could you think of a few uncertainty components that are missing when comparing the in-situ data to satellites?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results show the biases are within 5% for both RadCalNet and HYPERNETS and thus quite good agreement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TOA matchup**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we perform a comparison for a matchup between Sentinel-2 and HYPERNETS and RadCalNet on the 8th of June 2022. \n",
    "This time, the comparison is made at TOA. Specifically, the Sentinel-2 product used is SEQ20220608T090043_vs_S2B_MSIL1C_20220608T084559_N0400_R107_T33KWP_20220608T113756.SAFE. \n",
    "We have again extracted a 200 by 200 m region or interest around the HYPERNETS and RadCalNet site, and made it available as a NetCDF file:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ds_refl_S2_hyp = xr.load_dataset(\"example_S2_20220628.nc\")\n",
    "ds_refl_S2_rcn = xr.load_dataset(\"example_S2_20220628_rcn.nc\")\n",
    "\n",
    "bands_S2=[\"B01\", \"B02\", \"B03\", \"B04\", \"B05\", \"B06\", \"B07\", \"B08\", \"B09\", \"B10\", \"B11\", \"B8A\"]\n",
    "wav_S2 = [442.69504835,  492.43657768,  559.84905824,  664.62175142,\n",
    "        704.11493669,  740.49182383,  782.75291928,  832.79041366,\n",
    "        945.05446558, 1373.46188735, 1613.65941706,  864.71079209]\n",
    "vza_S2=5.6\n",
    "\n",
    "refl_S2_hyp = np.array([np.mean(ds_refl_S2_hyp[band].values) for band in bands_S2])\n",
    "u_refl_S2_hyp = np.array([np.std(ds_refl_S2_hyp[band].values) for band in bands_S2])\n",
    "refl_S2_rcn = np.array([np.mean(ds_refl_S2_rcn[band].values) for band in bands_S2])\n",
    "u_refl_S2_rcn = np.array([np.std(ds_refl_S2_rcn[band].values) for band in bands_S2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HYPERNETS does not (currently) provide TOA reference data. Instead the user would need to propagate the HYPERNETS to TOA themselves using Radiative Transfer (RT) modelling. This was done in De Vis et al (2024a). We here provide the TOA reflectances manually. These were based on propagating the BOA reflectances from the HYPERNETS L2A products to TOA using LibRadtran. Atmospheric properties from the RadCalNet network were used, and uncertainties from both atmospheric properties as well as on the reflectance were also propagated through the RT forward model. For further details we refer to De Vis et al (2024a). We here provide data at full resolution and band integrated (with the S2 SRF) data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav_TOA_HYP_full, refl_TOA_HYP_full, u_refl_TOA_HYP_full = np.load(\"hypernets_TOA_example_20220608_full.npy\")\n",
    "wav_TOA_HYP_band, refl_TOA_HYP_band, u_refl_TOA_HYP_band = np.load(\"hypernets_TOA_example_20220608_band.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TOA RadCalNet can be read in the same way as the BOA data. The TOA data is provided in the RadCalNet .output files that can be downloaded from the RadCalNet portal (https://www.radcalnet.org/). We here extract the reflectance and it's uncertainty for 9:00 UTC (which is column with index 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcn_data = np.genfromtxt(\"GONA01_2022_159_v04.09.output\", dtype=\"str\", delimiter=\"\\t\", skip_header=4)\n",
    "if np.all(rcn_data[:, -1] == \"\"):\n",
    "    rcn_data = rcn_data[:, :-1]\n",
    "wav_TOA_rcn = rcn_data[15:226, 0].astype(float)\n",
    "times_rcn = rcn_data[2, 1:]\n",
    "\n",
    "refl_TOA_rcn = rcn_data[15:226, 3].astype(float)\n",
    "u_refl_TOA_rcn = np.abs(rcn_data[232:443, 3].astype(float))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We once more band integrate the RadCalNet data. This time the Sentinel-2 SRF are applied. Band B12 is removed because it is outside the LANDHYPERNET wavelength range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def band_integrate_S2B(reflectance, wavelength):\n",
    "    \"\"\"\n",
    "    Function to perform L9 band integration on reflectance\n",
    "\n",
    "    :param reflectance: reflectance spectrum\n",
    "    :param wavelength: wavelengths\n",
    "    \"\"\"\n",
    "    refl_band, band_centres = band_integration.spectral_band_int_sensor(\n",
    "        d=reflectance,\n",
    "        wl=wavelength,\n",
    "        platform_name=\"Sentinel-2B\",\n",
    "        sensor_name=\"MSI\",\n",
    "        u_d=None,\n",
    "    )\n",
    "    return np.delete(refl_band, 11)\n",
    "\n",
    "refl_TOA_rcn_band = band_integrate_S2B(refl_TOA_rcn,wav_TOA_rcn)\n",
    "u_refl_TOA_rcn_band = prop.propagate_standard(band_integrate_S2B,[refl_TOA_rcn,wav_TOA_rcn],[u_refl_TOA_rcn,None],[\"rand\",None])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then again calculate the bias and its uncertainty for both the LANDHYPERNET and RadCalNet data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bias_TOA_hyp = ((refl_S2_hyp / refl_TOA_HYP_band) - 1) * 100\n",
    "u_bias_TOA_hyp = (\n",
    "    np.sqrt((u_refl_S2_hyp / refl_S2_hyp) ** 2 + (u_refl_TOA_HYP_band / refl_TOA_HYP_band) ** 2)\n",
    "    * 100\n",
    ")\n",
    "\n",
    "bias_TOA_rcn = ((refl_S2_rcn / refl_TOA_rcn_band) - 1) * 100\n",
    "u_bias_TOA_rcn = (\n",
    "    np.sqrt(\n",
    "        (u_refl_S2_rcn / refl_S2_rcn) ** 2\n",
    "        + (u_refl_TOA_rcn_band / refl_TOA_rcn_band) ** 2\n",
    "    )\n",
    "    * 100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we produce similar plots as for the BOA results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot(\"Sentinel-2\", \"2022-06-08\", wav_S2, refl_S2_hyp, u_refl_S2_hyp, wav_TOA_HYP_full, refl_TOA_HYP_full, u_refl_TOA_HYP_full, wav_TOA_rcn,\n",
    "     refl_TOA_rcn, u_refl_TOA_rcn, bias_TOA_hyp, u_bias_TOA_hyp, wav_TOA_HYP_band, refl_TOA_HYP_band, u_refl_TOA_HYP_band, vza_S2, bias_TOA_rcn, u_bias_TOA_rcn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to above, these demonstrate quite good agreement between the satellite and reference data. For further examples we refer to De Vis et al. (2024, https://doi.org/10.3389/frsen.2024.1323998)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3:** Why is B10 (at 1375 nm) not visible in the bias plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4:** We have not corrected for the 15 min time difference between the Sentinel-2 and network observation. Will this make much of a difference, and if so why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Choose a matchup yourself**\n",
    "\n",
    "There is a more simple and a more advanved option for the next exercise.\n",
    "\n",
    "- more advanced (probably not enough time during this limited time-slot, but feel free to start exploring and continue afterwards): Perform a comparison for an overpass over Gobabeb by one of your own satellites. Download RadCalNet or LANDHYPERNET data from the respective data portals (or if you don't have a login, ask someone within the training team to make some specific data available within the hypernets_training repository). \n",
    "\n",
    "- Or alternatively, choose one (or more/all) of the following BOA L9 files over Gobabeb during October 2024 (all available for use within the hypernets_training repository that was previously cloned) and perform a comparison with the provided LANDHYPERNET and RadCalNet files:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L9_hypernets_paths = ['example_L9_20241025.nc', 'example_L9_20241017.nc', 'example_L9_20241009.nc', 'example_L9_20241001.nc']\n",
    "\n",
    "hypernet_paths = ['HYPERNETS_L_GHNA_L2A_REF_20241025T0900_20241114T0243_v2.1.nc', 'HYPERNETS_L_GHNA_L2A_REF_20241017T0900_20241114T0343_v2.1.nc', 'HYPERNETS_L_GHNA_L2A_REF_20241009T0901_20241016T1731_v2.1.nc', 'HYPERNETS_L_GHNA_L2A_REF_20241001T0900_20241001T1525_v2.1.nc']\n",
    "\n",
    "\n",
    "\n",
    "L9_rcn_paths = ['example_L9_20241025_rcn.nc', 'example_L9_20241017_rcn.nc', 'example_L9_20241009_rcn.nc', 'example_L9_20241001_rcn.nc']\n",
    "\n",
    "rcn_paths = ['GONA01_2024_299_v00.09.input','GONA01_2024_291_v00.09.input','GONA01_2024_283_v00.09.input','GONA01_2024_275_v00.09.input']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the nadir measurements from the HYPERNETS sequences at this time of year and this time of day are affected by shadows. \n",
    "We thus recommend to instead use a viewing zenith angle of 5 or 10 degrees (averaging over different azimuths). \n",
    "In the L2B files, measurements affected by shadows will be removed. In the future, HYPERNETS products might be produced where the measurements affected by shadows are replaced by interpolated values (using BRDF model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 5:** Did you manage to generate more matchups?\n",
    "- No, not enough time\n",
    "- No, I wasn't sure how to do it\n",
    "- Yes, a single one\n",
    "- Yes, multiple\n",
    "- Yes, I used my own satellite data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the link for the wooclap quiz: https://app.wooclap.com/VHRODA2?from=banner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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