From dd432bb7002709f97aef4d5d04d3dbb32a899230 Mon Sep 17 00:00:00 2001 From: Joachim Meyer Date: Wed, 11 Sep 2024 15:56:51 -0600 Subject: [PATCH] Tutorial - Albedo - Add to TOC Add the snow albedo tutorial notebook to the TOC. This also renames and cleans the initial commited notebook. --- book/_toc.yml | 1 + .../albedo/NASA_SnowEx_Snow_Albedo_2023.ipynb | 26782 ---------------- book/tutorials/albedo/snow_albedo.ipynb | 531 + 3 files changed, 532 insertions(+), 26782 deletions(-) delete mode 100644 book/tutorials/albedo/NASA_SnowEx_Snow_Albedo_2023.ipynb create mode 100644 book/tutorials/albedo/snow_albedo.ipynb diff --git a/book/_toc.yml b/book/_toc.yml index 4ded3b8..67266c4 100644 --- a/book/_toc.yml +++ b/book/_toc.yml @@ -54,6 +54,7 @@ parts: title: Albedo sections: - file: tutorials/albedo/aviris-ng-data + - file: tutorials/albedo/snow_albedo - file: tutorials/NN_with_Pytorch/intro title: Neural Networks with Pytorch sections: diff --git a/book/tutorials/albedo/NASA_SnowEx_Snow_Albedo_2023.ipynb b/book/tutorials/albedo/NASA_SnowEx_Snow_Albedo_2023.ipynb deleted file mode 100644 index b959c48..0000000 --- a/book/tutorials/albedo/NASA_SnowEx_Snow_Albedo_2023.ipynb +++ /dev/null @@ -1,26782 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to the NASA SnowEx Snow Albedo 2023 Dataset\n", - "# author: Anton Surunis\n", - "# date: 2024-09-10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Field spectrometer measurements in the SnowEx 2023 Snow Albedo Campaign](./images_for_notebook/field_specs.png)\n", - "\n", - "# The NASA SnowEx 2023 Snow Albedo Field Campaign Dataset\n", - "\n", - "The NASA SnowEx 2023 Snow Albedo Field Campaign took place in burned and unburned boreal forests around Fairbanks, Alaska. The goal of the campaign was to improve understanding of the spatial, temporal, and process-based variability of snow albedo and the uncertainty of snow albedo measurements across scales in boreal forests. The campaign objectives were to capture snow albedo across scales of snow accumulation and snowmelt with coincident snow albedo from ground-based spectrometer measurements, tower-mounted and drone-based radiation measurements, and airborne AVIRIS-NG overflights across boreal forest disturbance history.\n", - "\n", - "Over five weeks from April 1st to May 5th 2023, several teams visited field sites around Fairbanks and collected spectral measurements over 500m-1km transects capturing snow reflectance and snow albedo over gradients of landscape, topography, and forest disturbance variability. During days with favorable weather/clear sky conditions, teams walked transects in teams of three collecting observations of snow spectra using field spectrometers coincident with hyperspectral aerial and satellite observations from above.\n", - "\n", - "The purpose of this tutorial is to provide an introduction to accessing and using the resulting field dataset. First, a review of background information is provided. Then, we cover how to prepare and access the different data points provided in the dataset. Finally, we provide an example of how to calculate derived statistics from the dataset.\n", - "\n", - "# Review of Hyperspectral Data\n", - "\n", - "Incoming solar radiation is either reflected, absorbed, or transmitted (or a combination of all three) depending on the surface material. This spectral response allows us to identify varying surface types (e.g. vegetation, snow, water, etc.) in a remote sensing image. The spectral resolution, or the wavelength interval, determines the amount of detail recorded in the spectral response: finer spectral resolutions have bands with narrow wavelength intervals, while coarser spectral resolutions have bands with larger wavelength intervals, and therefore, less detail in the spectral response (Credit: \"Introduction to AVIRS-NG\", Joachim Meyer, Chelsea Ackroyd, McKenzie Skiles, Phil Dennison, Keely Roth). ![https://www.neonscience.org/resources/learning-hub/tutorials/hyper-spec-intro](./images_for_notebook/em_spectrum.png)\n", - "\n", - "# Surface Reflectance vs Albedo\n", - "\n", - "Hyperspectral data is often captured as either albedo or surface reflectance.\n", - "\n", - "Albedo is the proportion of solar radiation that is reflected by a surface integrated over all incoming solar angles. This is accomplished by taking the ratio of down- and up-facing measurements of hemispherical radiation using a wide (180 degree) lens called a remote cosine receptor (RCR). Albedo is a very important property in calculating land surface energy exchange and snow-mass energy balance.\n", - "\n", - "![](./images_for_notebook/albedo_measure.png)\n", - "\n", - " In contrast, surface reflectance is the proportion of solar radiation reflected over a single or very narrow incoming solar angle (usually 4-8 degrees). Surface reflectance is calculated by taking the ratio of reflected solar radiation from a surface relative and that of a white reference.\n", - "\n", - "![](./images_for_notebook/refl_measure.png)\n", - "\n", - "White references are usually small panels covered in Spectralon - a highly reflective, near-Lambertian substance that reflects and scatters nearly all incoming light equally in all directions.\n", - "\n", - "![](./images_for_notebook/spectralon.jpg)\n", - "\n", - "Surface reflectance allows us to identify varying surface types (e.g. vegetation, snow, water, etc.) as well as specific qualities of those surfaces (e.g., grain size or grain type in a snowpack). While similar measures, the surface reflectance and albedo of a surface can differ considerably, especially at low solar angles where the angle of direct incident light is far off nadir. Further, since snow reflectance is based on reflected light from a white reference, it is essential that the white reference is kept pristine for accurate measurement of surface reflectance.\n", - "\n", - "# Field Spectrometers\n", - "\n", - "![More field spectrometer measurements in the SnowEx 2023 Snow Albedo Campaign](./images_for_notebook/field_specs2.png)\n", - "\n", - "Field spectrometers are remote sensing instruments that are carried into the field by operators and used to measure surface reflectance and albedo. Field spectrometers are manufactured by many different companies and come in many more different models using different spectral ranges, attachments, and processing software. While it is difficult to account for these differences without instrument intercomparison studies, it is an important fact to keep in mind when comparing hyperspectral data from different spectrometers.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Loading and Description" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# Load python packages\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.lines as mlines\n", - "import folium" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# INSERT YOUR PATH HERE\n", - "path = '/Users/brent/Code/AVIRIS/field_albedo/NASA_THP2020_spec_all_v1_20240906_nsidc.csv'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/wv/8sspfkpj0zz8cjf_9l8x34k00000gn/T/ipykernel_1156/724302279.py:2: DtypeWarning: Columns (7,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(path)\n" - ] - }, - { - "data": { - "text/html": [ - "
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120230407_S2_CARI_T2_102023-04-07S2CARIT2ssr8degdown65.157432-147.501666...86.0266.260000.260000258.31478910.524395129.196701#white reference-99993511.161445
220230407_S2_CARI_T2_102023-04-07S2CARIT2ssr8degdown65.157432-147.501666...86.0266.260000.260000258.31478910.524395129.196701#white reference-99993521.161260
320230407_S2_CARI_T2_102023-04-07S2CARIT2ssr8degdown65.157432-147.501666...86.0266.260000.260000258.31478910.524395129.196701#white reference-99993531.160960
420230407_S2_CARI_T2_102023-04-07S2CARIT2ssr8degdown65.157432-147.501666...86.0266.260000.260000258.31478910.524395129.196701#white reference-99993541.160609
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356450820230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...46.0227.461140.332216222.1240233.128354245.645065#location estimated1324960.111864
356450920230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...46.0227.461140.332216222.1240233.128354245.645065#location estimated1324970.160377
356451020230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...46.0227.461140.332216222.1240233.128354245.645065#location estimated1324980.265144
356451120230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...46.0227.461140.332216222.1240233.128354245.645065#location estimated1324990.380519
356451220230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...46.0227.461140.332216222.1240233.128354245.645065#location estimated132500-0.042500
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" - ], - "text/plain": [ - " id date instrument site transect \\\n", - "0 20230407_S2_CARI_T2_10 2023-04-07 S2 CARI T2 \n", - "1 20230407_S2_CARI_T2_10 2023-04-07 S2 CARI T2 \n", - "2 20230407_S2_CARI_T2_10 2023-04-07 S2 CARI T2 \n", - "3 20230407_S2_CARI_T2_10 2023-04-07 S2 CARI T2 \n", - "4 20230407_S2_CARI_T2_10 2023-04-07 S2 CARI T2 \n", - "... ... ... ... ... ... \n", - "3564508 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564509 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564510 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564511 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564512 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "\n", - " type attachment orientation lat long ... depth \\\n", - "0 ssr 8deg down 65.157432 -147.501666 ... 86.0 \n", - "1 ssr 8deg down 65.157432 -147.501666 ... 86.0 \n", - "2 ssr 8deg down 65.157432 -147.501666 ... 86.0 \n", - "3 ssr 8deg down 65.157432 -147.501666 ... 86.0 \n", - "4 ssr 8deg down 65.157432 -147.501666 ... 86.0 \n", - "... ... ... ... ... ... ... ... \n", - "3564508 albedo_raw rcr up 65.154253 -147.482509 ... 46.0 \n", - "3564509 albedo_raw rcr up 65.154253 -147.482509 ... 46.0 \n", - "3564510 albedo_raw rcr up 65.154253 -147.482509 ... 46.0 \n", - "3564511 albedo_raw rcr up 65.154253 -147.482509 ... 46.0 \n", - "3564512 albedo_raw rcr up 65.154253 -147.482509 ... 46.0 \n", - "\n", - " depth_alt depth_acc elevation slope aspect \\\n", - "0 266.26000 0.260000 258.314789 10.524395 129.196701 \n", - "1 266.26000 0.260000 258.314789 10.524395 129.196701 \n", - "2 266.26000 0.260000 258.314789 10.524395 129.196701 \n", - "3 266.26000 0.260000 258.314789 10.524395 129.196701 \n", - "4 266.26000 0.260000 258.314789 10.524395 129.196701 \n", - "... ... ... ... ... ... \n", - "3564508 227.46114 0.332216 222.124023 3.128354 245.645065 \n", - "3564509 227.46114 0.332216 222.124023 3.128354 245.645065 \n", - "3564510 227.46114 0.332216 222.124023 3.128354 245.645065 \n", - "3564511 227.46114 0.332216 222.124023 3.128354 245.645065 \n", - "3564512 227.46114 0.332216 222.124023 3.128354 245.645065 \n", - "\n", - " tags rcr_group wavelength value \n", - "0 #white reference -9999 350 1.161611 \n", - "1 #white reference -9999 351 1.161445 \n", - "2 #white reference -9999 352 1.161260 \n", - "3 #white reference -9999 353 1.160960 \n", - "4 #white reference -9999 354 1.160609 \n", - "... ... ... ... ... \n", - "3564508 #location estimated 13 2496 0.111864 \n", - "3564509 #location estimated 13 2497 0.160377 \n", - "3564510 #location estimated 13 2498 0.265144 \n", - "3564511 #location estimated 13 2499 0.380519 \n", - "3564512 #location estimated 13 2500 -0.042500 \n", - "\n", - "[3564513 rows x 21 columns]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Read dataframe\n", - "df = pd.read_csv(path)\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "id object\n", - "date object\n", - "instrument object\n", - "site object\n", - "transect object\n", - "type object\n", - "attachment object\n", - "orientation object\n", - "lat float64\n", - "long float64\n", - "spec_time object\n", - "depth float64\n", - "depth_alt float64\n", - "depth_acc float64\n", - "elevation float64\n", - "slope float64\n", - "aspect float64\n", - "tags object\n", - "rcr_group int64\n", - "wavelength int64\n", - "value float64\n", - "dtype: object" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Display types\n", - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset includes field spectrometer measurements of snow reflectance, snow albedo, and up- and down-facing bihemispherical radiance/irradiance measurements along with lots of associated metadata. The column descriptions are as follows:\n", - "\n", - "* __id__: Unique ID number of measurement \n", - "\n", - "* __date__: The date of measurement collection\n", - "\n", - "\n", - "* __instrument__: Code corresponding to the spectrometer identifier (S1 = Spectral Evolution; S2 & S7 = ASD FieldSpec4)\n", - "* __site__: Code of study site (CARI = Caribou-Poker Creek; DEJU = Delta Junction, CRMF = Creamer’s Field)\n", - "* __transect__: Code corresponding to transect where the measurement was taken (T1 = burned forest; T2 = forested; T3 = open)\n", - "* __type__: The type of spectral measurement as recorded by the note taker (ssr = snow surface reflectance, albedo = calculated snow surface albedo, albedo_raw = up and down components of snow surface albedo, irr_raw = irradiance) attachment: The fiber-optic attachment (8deg = 8 degree optic, 4deg = 4 degree optic, rcr = remote cosine receptor)\n", - "* __orientation__: Facing of the fiber-optic attachment (down = down-facing, up = up-facing)\n", - "* __lat__: Latitude of measurement as recorded by the GPS unit (epsg:4269)\n", - "* __long__: Longitude of measurement as recorded by the GPS unit (epsg:4269)\n", - "* __spec_time__: Local date and time of measurement as reported by spectrometer\n", - "* __depth__: Snow depth in cm\n", - "* __depth_alt__: Altitude as given by the GPS unit\n", - "* __depth_acc__: Accuracy of GPS coordinates as recorded by the GPS unit\n", - "* __slope__: Slope of the ground surface in degrees calculated from USGS 3DEP DEM (10m spatial resolution) using GIS software\n", - "* __aspect__: Aspect of the ground surface in degrees calculated from USGS 3DEP DEM (10m spatial resolution) using GIS software \n", - "* __tags__: Notes taken by notetaker with discrete notes* seperated by “#”\n", - "* __rcr_group__: Grouping variable for albedo and irradiance calculations\n", - "* __wavelength__: Wavelength measured by spectrometer\n", - "* __value__: Value measured by spectrometer at the given wavelength" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Preparation\n", - "First, we replace -9999 (null) values with NA, set negative values to 0 and convert the date column to the “date” data type." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# Replace -9999 with np.NaN\n", - "df = df.replace(-9999, np.nan)\n", - "\n", - "# Set negative values in the 'value' column to 0\n", - "df['value'] = df['value'].where(df['value'] >= 0, 0)\n", - "\n", - "# Convert the 'date' column to datetime format\n", - "df['date'] = pd.to_datetime(df['date'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we add some grouping variables to our dataset. These variables are fairly abitrary, but, broadly, transect(s) 1 went through burned forests, transect(s) went through unburned forests, and transect(s) 3 went through open areas. The season variable splits the data into three times spans over the field campaign." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "# Landcover grouping column\n", - "df['landcover'] = np.where(df['site'] == \"CRMF\", \"open\",\n", - " np.where(df['transect'] == \"T1\", \"burn\",\n", - " np.where(df['transect'] == \"T2\", \"forest\", \"open\")))\n", - "\n", - "# Season grouping column\n", - "df['season'] = np.where(df['date'] < pd.to_datetime(\"2023-04-15\"), \"early\",\n", - " np.where((df['date'] >= pd.to_datetime(\"2023-04-15\")) & \n", - " (df['date'] < pd.to_datetime(\"2023-04-21\")), \"mid\", \"late\"))\n", - "\n", - "# Factor season so that it is in the right order\n", - "df['season'] = pd.Categorical(df['season'], categories=[\"early\", \"mid\", \"late\"], ordered=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Exploration: Measurement Locations\n", - "Let’s start exploring the data by mapping our measurement locations." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filter and keep distinct rows\n", - "pts = df[(df['type'] == 'ssr') | (df['type'] == 'albedo')].drop_duplicates(subset='id')\n", - "pts = pts.dropna(subset=['lat', 'long']) # Remove rows with NaN in lat/lon\n", - "\n", - "# Define color mapping\n", - "color_map = {'ssr': 'blue', 'albedo': 'orange'}\n", - "pts['color'] = pts['type'].map(color_map)\n", - "\n", - "# Create a folium map centered around the mean location of your points\n", - "m = folium.Map()\n", - "\n", - "# Get the lat/lon bounds of the data\n", - "bounds = [[pts['lat'].min(), pts['long'].min()], [pts['lat'].max(), pts['long'].max()]]\n", - "\n", - "# Add circle markers\n", - "for _, row in pts.iterrows():\n", - " folium.CircleMarker(\n", - " location=[row['lat'], row['long']],\n", - " color=row['color'],\n", - " radius=5,\n", - " popup=row['type']\n", - " ).add_to(m)\n", - "\n", - "# Fit the map to the bounds of the points\n", - "m.fit_bounds(bounds)\n", - "\n", - "# Add OpenStreetMap tiles with attribution\n", - "folium.TileLayer(\n", - " 'OpenStreetMap',\n", - " name='OpenStreetMap',\n", - " attr='© OpenStreetMap contributors'\n", - ").add_to(m)\n", - "\n", - "# Display the map\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Exploration: Snow Surface Reflectance\n", - "Let’s take a look at just the reflectance data.\n", - "\n", - "First, we filter our data by snow surface reflectance (ssr) measurements only. Some of the measurements have exceptionally high reflectance values, so we filter out measurements where reflectance is too high in the visible range.\n", - "\n", - "For this example, it is looking at __just__ mid-season measurements at the CARI site." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# Only types of snow surface reflectance\n", - "df_ssr = df[df['type'] == 'ssr']\n", - "\n", - "# Grouping, filtering, and then removing any that meet this condition\n", - "df_ssr = (df_ssr.groupby('id')\n", - " .filter(lambda x: not any((x['wavelength'] < 750) & (x['value'] >= 1.2)))\n", - " .reset_index(drop=True))" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a dataset for now just looking at CARI during mid season\n", - "df_ssr_cari = df_ssr[(df_ssr['site'] == 'CARI') & (df_ssr['season'] == 'mid')]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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wavelengthlandcovervalue
meanstd
0350burn1.0468270.062089
1350forest1.0118120.117072
2350open1.1148040.070424
3351burn1.0456770.062203
4351forest1.0100880.117716
...............
64482499forest0.0208900.028921
64492499open0.0331860.030367
64502500burn0.2088810.191107
64512500forest0.0215160.034293
64522500open0.0396140.038573
\n", - "

6453 rows × 4 columns

\n", - "
" - ], - "text/plain": [ - " wavelength landcover value \n", - " mean std\n", - "0 350 burn 1.046827 0.062089\n", - "1 350 forest 1.011812 0.117072\n", - "2 350 open 1.114804 0.070424\n", - "3 351 burn 1.045677 0.062203\n", - "4 351 forest 1.010088 0.117716\n", - "... ... ... ... ...\n", - "6448 2499 forest 0.020890 0.028921\n", - "6449 2499 open 0.033186 0.030367\n", - "6450 2500 burn 0.208881 0.191107\n", - "6451 2500 forest 0.021516 0.034293\n", - "6452 2500 open 0.039614 0.038573\n", - "\n", - "[6453 rows x 4 columns]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Here, we are grouping by wavelength and landcover, and taking the mean and standard deviation for each group\n", - "df_group = df_ssr_cari[['wavelength','value','landcover']].groupby(['wavelength','landcover']).agg(['mean','std']).reset_index()\n", - "df_group" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# These can each be plotted now with the following block\n", - "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5), sharex=True, sharey=True)\n", - "plt.rcParams.update({'font.size': 12})\n", - "\n", - "landcovers = ['burn','forest','open']\n", - "colors = ['black', 'forestgreen', 'steelblue']\n", - "legend_handles = []\n", - "\n", - "for i in range(len(landcovers)):\n", - " df_group_lc = df_group[df_group['landcover'] == landcovers[i]]\n", - " c = colors[i]\n", - " ax.scatter(df_group_lc['wavelength'], df_group_lc['value']['mean'], c=c, s=15, alpha=1.0)\n", - " ax.fill_between(df_group_lc['wavelength'], df_group_lc['value']['mean']-df_group_lc['value']['std'],\n", - " df_group_lc['value']['mean']+df_group_lc['value']['std'], alpha=0.2, color=c)\n", - "\n", - " # Create custom legend handle for scatter points\n", - " legend_handles.append(mlines.Line2D([], [], color=c, marker='o', linestyle='None', markersize=8, label=landcovers[i]))\n", - "\n", - "\n", - "ax.set_ylim(0,1.25)\n", - "ax.legend(handles=legend_handles)\n", - "ax.set_xlabel('Wavelength [nm]')\n", - "ax.set_ylabel('Reflectance')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Exploration: Snow Surface Albedo\n", - "We can repeat this same example but now for snow albedo.\n", - "\n", - "Once again, just looking at mid-season CARI collections." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddateinstrumentsitetransecttypeattachmentorientationlatlong...depth_accelevationslopeaspecttagsrcr_groupwavelengthvaluelandcoverseason
228036620230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03500.809766forestearly
228036720230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03500.809766forestearly
228036820230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03510.810376forestearly
228036920230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03510.810376forestearly
228037020230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03520.811106forestearly
..................................................................
271486320230504_S4_CARI_T3_132023-05-04S4CARIT3albedorcrNaN65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024980.000000openlate
271486420230504_S4_CARI_T3_132023-05-04S4CARIT3albedorcrNaN65.154253-147.482509...0.332216222.1240233.128354245.645065#snow#location estimated13.024990.000000openlate
271486520230504_S4_CARI_T3_132023-05-04S4CARIT3albedorcrNaN65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024990.000000openlate
271486620230504_S4_CARI_T3_132023-05-04S4CARIT3albedorcrNaN65.154253-147.482509...0.332216222.1240233.128354245.645065#snow#location estimated13.025002.101184openlate
271486720230504_S4_CARI_T3_132023-05-04S4CARIT3albedorcrNaN65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.025002.101184openlate
\n", - "

432351 rows × 23 columns

\n", - "
" - ], - "text/plain": [ - " id date instrument site transect type \\\n", - "2280366 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 albedo \n", - "2280367 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 albedo \n", - "2280368 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 albedo \n", - "2280369 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 albedo \n", - "2280370 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 albedo \n", - "... ... ... ... ... ... ... \n", - "2714863 20230504_S4_CARI_T3_13 2023-05-04 S4 CARI T3 albedo \n", - "2714864 20230504_S4_CARI_T3_13 2023-05-04 S4 CARI T3 albedo \n", - "2714865 20230504_S4_CARI_T3_13 2023-05-04 S4 CARI T3 albedo \n", - "2714866 20230504_S4_CARI_T3_13 2023-05-04 S4 CARI T3 albedo \n", - "2714867 20230504_S4_CARI_T3_13 2023-05-04 S4 CARI T3 albedo \n", - "\n", - " attachment orientation lat long ... depth_acc \\\n", - "2280366 rcr NaN 65.157432 -147.501666 ... 0.260000 \n", - "2280367 rcr NaN 65.157432 -147.501666 ... 0.260000 \n", - "2280368 rcr NaN 65.157432 -147.501666 ... 0.260000 \n", - "2280369 rcr NaN 65.157432 -147.501666 ... 0.260000 \n", - "2280370 rcr NaN 65.157432 -147.501666 ... 0.260000 \n", - "... ... ... ... ... ... ... \n", - "2714863 rcr NaN 65.154253 -147.482509 ... 0.332216 \n", - "2714864 rcr NaN 65.154253 -147.482509 ... 0.332216 \n", - "2714865 rcr NaN 65.154253 -147.482509 ... 0.332216 \n", - "2714866 rcr NaN 65.154253 -147.482509 ... 0.332216 \n", - "2714867 rcr NaN 65.154253 -147.482509 ... 0.332216 \n", - "\n", - " elevation slope aspect tags \\\n", - "2280366 258.314789 10.524395 129.196701 NaN \n", - "2280367 258.314789 10.524395 129.196701 NaN \n", - "2280368 258.314789 10.524395 129.196701 NaN \n", - "2280369 258.314789 10.524395 129.196701 NaN \n", - "2280370 258.314789 10.524395 129.196701 NaN \n", - "... ... ... ... ... \n", - "2714863 222.124023 3.128354 245.645065 #location estimated \n", - "2714864 222.124023 3.128354 245.645065 #snow#location estimated \n", - "2714865 222.124023 3.128354 245.645065 #location estimated \n", - "2714866 222.124023 3.128354 245.645065 #snow#location estimated \n", - "2714867 222.124023 3.128354 245.645065 #location estimated \n", - "\n", - " rcr_group wavelength value landcover season \n", - "2280366 3.0 350 0.809766 forest early \n", - "2280367 3.0 350 0.809766 forest early \n", - "2280368 3.0 351 0.810376 forest early \n", - "2280369 3.0 351 0.810376 forest early \n", - "2280370 3.0 352 0.811106 forest early \n", - "... ... ... ... ... ... \n", - "2714863 13.0 2498 0.000000 open late \n", - "2714864 13.0 2499 0.000000 open late \n", - "2714865 13.0 2499 0.000000 open late \n", - "2714866 13.0 2500 2.101184 open late \n", - "2714867 13.0 2500 2.101184 open late \n", - "\n", - "[432351 rows x 23 columns]" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Remove tags with bad and only include albedo\n", - "df_alb = df[(df['type'] == 'albedo') & (df['tags'].isna() | ~df['tags'].str.contains('bad', na=False))]\n", - "\n", - "df_alb" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "similar to the reflectance example, we will just grab CARI mid-season for now" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Creating a dataset for now just looking at CARI during mid season\n", - "df_alb_cari = df_alb[(df_alb['site'] == 'CARI') & (df_alb['season'] == 'mid')]\n", - "\n", - "# Here, we are grouping by wavelength and landcover, and taking the mean and standard deviation for each group\n", - "df_alb_group = df_alb_cari[['wavelength','value','landcover']].groupby(['wavelength','landcover']).agg(['mean','std']).reset_index()\n", - "\n", - "# And plotting\n", - "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5), sharex=True, sharey=True)\n", - "plt.rcParams.update({'font.size': 12})\n", - "\n", - "landcovers = ['burn','forest','open']\n", - "colors = ['black', 'forestgreen', 'steelblue']\n", - "legend_handles = []\n", - "\n", - "for i in range(len(landcovers)):\n", - " df_group_lc = df_alb_group[df_alb_group['landcover'] == landcovers[i]]\n", - " c = colors[i]\n", - " ax.scatter(df_group_lc['wavelength'], df_group_lc['value']['mean'], c=c, s=15, alpha=1.0)\n", - " ax.fill_between(df_group_lc['wavelength'], df_group_lc['value']['mean']-df_group_lc['value']['std'],\n", - " df_group_lc['value']['mean']+df_group_lc['value']['std'], alpha=0.2, color=c)\n", - "\n", - " # Create custom legend handle for scatter points\n", - " legend_handles.append(mlines.Line2D([], [], color=c, marker='o', linestyle='None', markersize=8, label=landcovers[i]))\n", - "\n", - "\n", - "ax.set_ylim(0,1.25)\n", - "ax.legend(handles=legend_handles)\n", - "ax.set_xlabel('Wavelength [nm]')\n", - "ax.set_ylabel('Albedo')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Broadband Albedo\n", - "Broadband albedo (BBA) is the ratio of upward and downward bi-hemispherical reflectance over a specific wavelength range. To calculate BBA, we weight the albedo at each band by the amount of incoming solar radiation (called irradiance) at that band and sum all results over the wavelength range. While albedo is calculated individually over each measurement band, calculations of BBA produce a single value of albedo over a given spectral range. Some common spectral ranges are shortwave BBA (0.25 μm to 5.0 μm), ultraviolet BBA (0.4 μm to 0.7 μm), and visible BBA (0.4 μm to 0.7 μm). Broadband albedo is important for calculating impurities in snowpack, especially ones that absorb light at all short wavelengths such as black carbon.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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iddateinstrumentsitetransecttypeattachmentorientationlatlong...depth_accelevationslopeaspecttagsrcr_groupwavelengthvaluelandcoverseason
228036620230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03500.809766forestearly
228036720230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03500.809766forestearly
228036820230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03510.810376forestearly
228036920230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03510.810376forestearly
228037020230407_S2_CARI_T2_32023-04-07S2CARIT2albedorcrNaN65.157432-147.501666...0.260000258.31478910.524395129.196701NaN3.03520.811106forestearly
..................................................................
356450820230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024960.111864openlate
356450920230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024970.160377openlate
356451020230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024980.265144openlate
356451120230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.024990.380519openlate
356451220230504_S4_CARI_T3_652023-05-04S4CARIT3albedo_rawrcrup65.154253-147.482509...0.332216222.1240233.128354245.645065#location estimated13.025000.000000openlate
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952893 rows × 23 columns

\n", - "
" - ], - "text/plain": [ - " id date instrument site transect \\\n", - "2280366 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 \n", - "2280367 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 \n", - "2280368 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 \n", - "2280369 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 \n", - "2280370 20230407_S2_CARI_T2_3 2023-04-07 S2 CARI T2 \n", - "... ... ... ... ... ... \n", - "3564508 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564509 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564510 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564511 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "3564512 20230504_S4_CARI_T3_65 2023-05-04 S4 CARI T3 \n", - "\n", - " type attachment orientation lat long ... \\\n", - "2280366 albedo rcr NaN 65.157432 -147.501666 ... \n", - "2280367 albedo rcr NaN 65.157432 -147.501666 ... \n", - "2280368 albedo rcr NaN 65.157432 -147.501666 ... \n", - "2280369 albedo rcr NaN 65.157432 -147.501666 ... \n", - "2280370 albedo rcr NaN 65.157432 -147.501666 ... \n", - "... ... ... ... ... ... ... \n", - "3564508 albedo_raw rcr up 65.154253 -147.482509 ... \n", - "3564509 albedo_raw rcr up 65.154253 -147.482509 ... \n", - "3564510 albedo_raw rcr up 65.154253 -147.482509 ... \n", - "3564511 albedo_raw rcr up 65.154253 -147.482509 ... \n", - "3564512 albedo_raw rcr up 65.154253 -147.482509 ... \n", - "\n", - " depth_acc elevation slope aspect tags \\\n", - "2280366 0.260000 258.314789 10.524395 129.196701 NaN \n", - "2280367 0.260000 258.314789 10.524395 129.196701 NaN \n", - "2280368 0.260000 258.314789 10.524395 129.196701 NaN \n", - "2280369 0.260000 258.314789 10.524395 129.196701 NaN \n", - "2280370 0.260000 258.314789 10.524395 129.196701 NaN \n", - "... ... ... ... ... ... \n", - "3564508 0.332216 222.124023 3.128354 245.645065 #location estimated \n", - "3564509 0.332216 222.124023 3.128354 245.645065 #location estimated \n", - "3564510 0.332216 222.124023 3.128354 245.645065 #location estimated \n", - "3564511 0.332216 222.124023 3.128354 245.645065 #location estimated \n", - "3564512 0.332216 222.124023 3.128354 245.645065 #location estimated \n", - "\n", - " rcr_group wavelength value landcover season \n", - "2280366 3.0 350 0.809766 forest early \n", - "2280367 3.0 350 0.809766 forest early \n", - "2280368 3.0 351 0.810376 forest early \n", - "2280369 3.0 351 0.810376 forest early \n", - "2280370 3.0 352 0.811106 forest early \n", - "... ... ... ... ... ... \n", - "3564508 13.0 2496 0.111864 open late \n", - "3564509 13.0 2497 0.160377 open late \n", - "3564510 13.0 2498 0.265144 open late \n", - "3564511 13.0 2499 0.380519 open late \n", - "3564512 13.0 2500 0.000000 open late \n", - "\n", - "[952893 rows x 23 columns]" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Remove tags with bad and only include albedo and upward radiation\n", - "df_bba = df[(df['type'] == 'albedo') | (df['orientation'] == 'up')]\n", - "df_bba = df_bba[(df_bba['tags'].isna() | ~df_bba['tags'].str.contains('bad', na=False))]\n", - "\n", - "df_bba\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To be brief in this document, we will show the irradiance and broadband albedo for one paired measurement (taken about the same time)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "df_pair = df_bba[(df_bba['date'] == '2023-04-20') & (df_bba['rcr_group'] == 6) & (df_bba['instrument'] == 'S1') ]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting this, we can see that the signal is a bit messy in the longer wavelengths due to clouds and atmosphere." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# Get the data and save to a simpler dataframe\n", - "df_bba_example_albedo = df_pair[df_pair['type'] == 'albedo']\n", - "df_bba_example_up = df_pair[df_pair['orientation'] == 'up']\n", - "df_test = pd.DataFrame(data=df_bba_example_albedo.wavelength.values, columns=['wavelength'])\n", - "df_test['albedo'] = df_bba_example_albedo.value.values\n", - "df_test['irrad'] = df_bba_example_up.value.values\n", - "\n", - "# Plot albedo\n", - "plt.scatter(df_test.wavelength, df_test.albedo)\n", - "\n", - "plt.ylim(0,1)\n", - "plt.ylabel('Albedo')\n", - "plt.xlabel('Wavelength [nm]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And so we must remove these bands before integrating for BBA." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "# In this code we are removing noise from these windows of bands\n", - "df_test = df_test[~df_test.iloc[:, 0].between(300, 400, inclusive='neither')]\n", - "df_test = df_test[~df_test.iloc[:, 0].between(1300, 1450, inclusive='neither')]\n", - "df_test = df_test[~df_test.iloc[:, 0].between(1750, 2000, inclusive='neither')]\n", - "df_test = df_test[~df_test.iloc[:, 0].between(2200, 2600, inclusive='neither')]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Now plotting again to show the difference,\n", - "# Plot albedo\n", - "plt.scatter(df_test.wavelength, df_test.albedo)\n", - "plt.ylim(0,1)\n", - "plt.ylabel('Albedo')\n", - "plt.xlabel('Wavelength [nm]')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can use numerical integration for all of the valid bands to solve for BBA " - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BBA: 0.725\n" - ] - } - ], - "source": [ - "# Integrating broadband albedo\n", - "broadband = np.trapz(df_test.albedo * df_test.irrad, dx=1) / np.trapz(df_test.irrad, dx=1)\n", - "\n", - "print(f'BBA: {round(broadband,3)}')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "goshawk", - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/book/tutorials/albedo/snow_albedo.ipynb b/book/tutorials/albedo/snow_albedo.ipynb new file mode 100644 index 0000000..d514597 --- /dev/null +++ b/book/tutorials/albedo/snow_albedo.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to the NASA SnowEx Snow Albedo 2023 Dataset\n", + "# author: Anton Surunis\n", + "# date: 2024-09-10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Field spectrometer measurements in the SnowEx 2023 Snow Albedo Campaign](./images_for_notebook/field_specs.png)\n", + "\n", + "# The NASA SnowEx 2023 Snow Albedo Field Campaign Dataset\n", + "\n", + "The NASA SnowEx 2023 Snow Albedo Field Campaign took place in burned and unburned boreal forests around Fairbanks, Alaska. The goal of the campaign was to improve understanding of the spatial, temporal, and process-based variability of snow albedo and the uncertainty of snow albedo measurements across scales in boreal forests. The campaign objectives were to capture snow albedo across scales of snow accumulation and snowmelt with coincident snow albedo from ground-based spectrometer measurements, tower-mounted and drone-based radiation measurements, and airborne AVIRIS-NG overflights across boreal forest disturbance history.\n", + "\n", + "Over five weeks from April 1st to May 5th 2023, several teams visited field sites around Fairbanks and collected spectral measurements over 500m-1km transects capturing snow reflectance and snow albedo over gradients of landscape, topography, and forest disturbance variability. During days with favorable weather/clear sky conditions, teams walked transects in teams of three collecting observations of snow spectra using field spectrometers coincident with hyperspectral aerial and satellite observations from above.\n", + "\n", + "The purpose of this tutorial is to provide an introduction to accessing and using the resulting field dataset. First, a review of background information is provided. Then, we cover how to prepare and access the different data points provided in the dataset. Finally, we provide an example of how to calculate derived statistics from the dataset.\n", + "\n", + "# Review of Hyperspectral Data\n", + "\n", + "Incoming solar radiation is either reflected, absorbed, or transmitted (or a combination of all three) depending on the surface material. This spectral response allows us to identify varying surface types (e.g. vegetation, snow, water, etc.) in a remote sensing image. The spectral resolution, or the wavelength interval, determines the amount of detail recorded in the spectral response: finer spectral resolutions have bands with narrow wavelength intervals, while coarser spectral resolutions have bands with larger wavelength intervals, and therefore, less detail in the spectral response (Credit: \"Introduction to AVIRS-NG\", Joachim Meyer, Chelsea Ackroyd, McKenzie Skiles, Phil Dennison, Keely Roth). ![https://www.neonscience.org/resources/learning-hub/tutorials/hyper-spec-intro](./images_for_notebook/em_spectrum.png)\n", + "\n", + "# Surface Reflectance vs Albedo\n", + "\n", + "Hyperspectral data is often captured as either albedo or surface reflectance.\n", + "\n", + "Albedo is the proportion of solar radiation that is reflected by a surface integrated over all incoming solar angles. This is accomplished by taking the ratio of down- and up-facing measurements of hemispherical radiation using a wide (180 degree) lens called a remote cosine receptor (RCR). Albedo is a very important property in calculating land surface energy exchange and snow-mass energy balance.\n", + "\n", + "![](./images_for_notebook/albedo_measure.png)\n", + "\n", + " In contrast, surface reflectance is the proportion of solar radiation reflected over a single or very narrow incoming solar angle (usually 4-8 degrees). Surface reflectance is calculated by taking the ratio of reflected solar radiation from a surface relative and that of a white reference.\n", + "\n", + "![](./images_for_notebook/refl_measure.png)\n", + "\n", + "White references are usually small panels covered in Spectralon - a highly reflective, near-Lambertian substance that reflects and scatters nearly all incoming light equally in all directions.\n", + "\n", + "![](./images_for_notebook/spectralon.jpg)\n", + "\n", + "Surface reflectance allows us to identify varying surface types (e.g. vegetation, snow, water, etc.) as well as specific qualities of those surfaces (e.g., grain size or grain type in a snowpack). While similar measures, the surface reflectance and albedo of a surface can differ considerably, especially at low solar angles where the angle of direct incident light is far off nadir. Further, since snow reflectance is based on reflected light from a white reference, it is essential that the white reference is kept pristine for accurate measurement of surface reflectance.\n", + "\n", + "# Field Spectrometers\n", + "\n", + "![More field spectrometer measurements in the SnowEx 2023 Snow Albedo Campaign](./images_for_notebook/field_specs2.png)\n", + "\n", + "Field spectrometers are remote sensing instruments that are carried into the field by operators and used to measure surface reflectance and albedo. Field spectrometers are manufactured by many different companies and come in many more different models using different spectral ranges, attachments, and processing software. While it is difficult to account for these differences without instrument intercomparison studies, it is an important fact to keep in mind when comparing hyperspectral data from different spectrometers.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Loading and Description" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load python packages\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.lines as mlines\n", + "import folium" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# INSERT YOUR PATH HERE\n", + "path = '/Users/brent/Code/AVIRIS/field_albedo/NASA_THP2020_spec_all_v1_20240906_nsidc.csv'\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read dataframe\n", + "df = pd.read_csv(path)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Display types\n", + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset includes field spectrometer measurements of snow reflectance, snow albedo, and up- and down-facing bihemispherical radiance/irradiance measurements along with lots of associated metadata. The column descriptions are as follows:\n", + "\n", + "* __id__: Unique ID number of measurement \n", + "\n", + "* __date__: The date of measurement collection\n", + "\n", + "\n", + "* __instrument__: Code corresponding to the spectrometer identifier (S1 = Spectral Evolution; S2 & S7 = ASD FieldSpec4)\n", + "* __site__: Code of study site (CARI = Caribou-Poker Creek; DEJU = Delta Junction, CRMF = Creamer’s Field)\n", + "* __transect__: Code corresponding to transect where the measurement was taken (T1 = burned forest; T2 = forested; T3 = open)\n", + "* __type__: The type of spectral measurement as recorded by the note taker (ssr = snow surface reflectance, albedo = calculated snow surface albedo, albedo_raw = up and down components of snow surface albedo, irr_raw = irradiance) attachment: The fiber-optic attachment (8deg = 8 degree optic, 4deg = 4 degree optic, rcr = remote cosine receptor)\n", + "* __orientation__: Facing of the fiber-optic attachment (down = down-facing, up = up-facing)\n", + "* __lat__: Latitude of measurement as recorded by the GPS unit (epsg:4269)\n", + "* __long__: Longitude of measurement as recorded by the GPS unit (epsg:4269)\n", + "* __spec_time__: Local date and time of measurement as reported by spectrometer\n", + "* __depth__: Snow depth in cm\n", + "* __depth_alt__: Altitude as given by the GPS unit\n", + "* __depth_acc__: Accuracy of GPS coordinates as recorded by the GPS unit\n", + "* __slope__: Slope of the ground surface in degrees calculated from USGS 3DEP DEM (10m spatial resolution) using GIS software\n", + "* __aspect__: Aspect of the ground surface in degrees calculated from USGS 3DEP DEM (10m spatial resolution) using GIS software \n", + "* __tags__: Notes taken by notetaker with discrete notes* seperated by “#”\n", + "* __rcr_group__: Grouping variable for albedo and irradiance calculations\n", + "* __wavelength__: Wavelength measured by spectrometer\n", + "* __value__: Value measured by spectrometer at the given wavelength" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Preparation\n", + "First, we replace -9999 (null) values with NA, set negative values to 0 and convert the date column to the “date” data type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Replace -9999 with np.NaN\n", + "df = df.replace(-9999, np.nan)\n", + "\n", + "# Set negative values in the 'value' column to 0\n", + "df['value'] = df['value'].where(df['value'] >= 0, 0)\n", + "\n", + "# Convert the 'date' column to datetime format\n", + "df['date'] = pd.to_datetime(df['date'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we add some grouping variables to our dataset. These variables are fairly abitrary, but, broadly, transect(s) 1 went through burned forests, transect(s) went through unburned forests, and transect(s) 3 went through open areas. The season variable splits the data into three times spans over the field campaign." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Landcover grouping column\n", + "df['landcover'] = np.where(df['site'] == \"CRMF\", \"open\",\n", + " np.where(df['transect'] == \"T1\", \"burn\",\n", + " np.where(df['transect'] == \"T2\", \"forest\", \"open\")))\n", + "\n", + "# Season grouping column\n", + "df['season'] = np.where(df['date'] < pd.to_datetime(\"2023-04-15\"), \"early\",\n", + " np.where((df['date'] >= pd.to_datetime(\"2023-04-15\")) & \n", + " (df['date'] < pd.to_datetime(\"2023-04-21\")), \"mid\", \"late\"))\n", + "\n", + "# Factor season so that it is in the right order\n", + "df['season'] = pd.Categorical(df['season'], categories=[\"early\", \"mid\", \"late\"], ordered=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration: Measurement Locations\n", + "Let’s start exploring the data by mapping our measurement locations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter and keep distinct rows\n", + "pts = df[(df['type'] == 'ssr') | (df['type'] == 'albedo')].drop_duplicates(subset='id')\n", + "pts = pts.dropna(subset=['lat', 'long']) # Remove rows with NaN in lat/lon\n", + "\n", + "# Define color mapping\n", + "color_map = {'ssr': 'blue', 'albedo': 'orange'}\n", + "pts['color'] = pts['type'].map(color_map)\n", + "\n", + "# Create a folium map centered around the mean location of your points\n", + "m = folium.Map()\n", + "\n", + "# Get the lat/lon bounds of the data\n", + "bounds = [[pts['lat'].min(), pts['long'].min()], [pts['lat'].max(), pts['long'].max()]]\n", + "\n", + "# Add circle markers\n", + "for _, row in pts.iterrows():\n", + " folium.CircleMarker(\n", + " location=[row['lat'], row['long']],\n", + " color=row['color'],\n", + " radius=5,\n", + " popup=row['type']\n", + " ).add_to(m)\n", + "\n", + "# Fit the map to the bounds of the points\n", + "m.fit_bounds(bounds)\n", + "\n", + "# Add OpenStreetMap tiles with attribution\n", + "folium.TileLayer(\n", + " 'OpenStreetMap',\n", + " name='OpenStreetMap',\n", + " attr='© OpenStreetMap contributors'\n", + ").add_to(m)\n", + "\n", + "# Display the map\n", + "m" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration: Snow Surface Reflectance\n", + "Let’s take a look at just the reflectance data.\n", + "\n", + "First, we filter our data by snow surface reflectance (ssr) measurements only. Some of the measurements have exceptionally high reflectance values, so we filter out measurements where reflectance is too high in the visible range.\n", + "\n", + "For this example, it is looking at __just__ mid-season measurements at the CARI site." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Only types of snow surface reflectance\n", + "df_ssr = df[df['type'] == 'ssr']\n", + "\n", + "# Grouping, filtering, and then removing any that meet this condition\n", + "df_ssr = (df_ssr.groupby('id')\n", + " .filter(lambda x: not any((x['wavelength'] < 750) & (x['value'] >= 1.2)))\n", + " .reset_index(drop=True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a dataset for now just looking at CARI during mid season\n", + "df_ssr_cari = df_ssr[(df_ssr['site'] == 'CARI') & (df_ssr['season'] == 'mid')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Here, we are grouping by wavelength and landcover, and taking the mean and standard deviation for each group\n", + "df_group = df_ssr_cari[['wavelength','value','landcover']].groupby(['wavelength','landcover']).agg(['mean','std']).reset_index()\n", + "df_group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# These can each be plotted now with the following block\n", + "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5), sharex=True, sharey=True)\n", + "plt.rcParams.update({'font.size': 12})\n", + "\n", + "landcovers = ['burn','forest','open']\n", + "colors = ['black', 'forestgreen', 'steelblue']\n", + "legend_handles = []\n", + "\n", + "for i in range(len(landcovers)):\n", + " df_group_lc = df_group[df_group['landcover'] == landcovers[i]]\n", + " c = colors[i]\n", + " ax.scatter(df_group_lc['wavelength'], df_group_lc['value']['mean'], c=c, s=15, alpha=1.0)\n", + " ax.fill_between(df_group_lc['wavelength'], df_group_lc['value']['mean']-df_group_lc['value']['std'],\n", + " df_group_lc['value']['mean']+df_group_lc['value']['std'], alpha=0.2, color=c)\n", + "\n", + " # Create custom legend handle for scatter points\n", + " legend_handles.append(mlines.Line2D([], [], color=c, marker='o', linestyle='None', markersize=8, label=landcovers[i]))\n", + "\n", + "\n", + "ax.set_ylim(0,1.25)\n", + "ax.legend(handles=legend_handles)\n", + "ax.set_xlabel('Wavelength [nm]')\n", + "ax.set_ylabel('Reflectance')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Exploration: Snow Surface Albedo\n", + "We can repeat this same example but now for snow albedo.\n", + "\n", + "Once again, just looking at mid-season CARI collections." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove tags with bad and only include albedo\n", + "df_alb = df[(df['type'] == 'albedo') & (df['tags'].isna() | ~df['tags'].str.contains('bad', na=False))]\n", + "\n", + "df_alb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "similar to the reflectance example, we will just grab CARI mid-season for now" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating a dataset for now just looking at CARI during mid season\n", + "df_alb_cari = df_alb[(df_alb['site'] == 'CARI') & (df_alb['season'] == 'mid')]\n", + "\n", + "# Here, we are grouping by wavelength and landcover, and taking the mean and standard deviation for each group\n", + "df_alb_group = df_alb_cari[['wavelength','value','landcover']].groupby(['wavelength','landcover']).agg(['mean','std']).reset_index()\n", + "\n", + "# And plotting\n", + "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5), sharex=True, sharey=True)\n", + "plt.rcParams.update({'font.size': 12})\n", + "\n", + "landcovers = ['burn','forest','open']\n", + "colors = ['black', 'forestgreen', 'steelblue']\n", + "legend_handles = []\n", + "\n", + "for i in range(len(landcovers)):\n", + " df_group_lc = df_alb_group[df_alb_group['landcover'] == landcovers[i]]\n", + " c = colors[i]\n", + " ax.scatter(df_group_lc['wavelength'], df_group_lc['value']['mean'], c=c, s=15, alpha=1.0)\n", + " ax.fill_between(df_group_lc['wavelength'], df_group_lc['value']['mean']-df_group_lc['value']['std'],\n", + " df_group_lc['value']['mean']+df_group_lc['value']['std'], alpha=0.2, color=c)\n", + "\n", + " # Create custom legend handle for scatter points\n", + " legend_handles.append(mlines.Line2D([], [], color=c, marker='o', linestyle='None', markersize=8, label=landcovers[i]))\n", + "\n", + "\n", + "ax.set_ylim(0,1.25)\n", + "ax.legend(handles=legend_handles)\n", + "ax.set_xlabel('Wavelength [nm]')\n", + "ax.set_ylabel('Albedo')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Broadband Albedo\n", + "Broadband albedo (BBA) is the ratio of upward and downward bi-hemispherical reflectance over a specific wavelength range. To calculate BBA, we weight the albedo at each band by the amount of incoming solar radiation (called irradiance) at that band and sum all results over the wavelength range. While albedo is calculated individually over each measurement band, calculations of BBA produce a single value of albedo over a given spectral range. Some common spectral ranges are shortwave BBA (0.25 μm to 5.0 μm), ultraviolet BBA (0.4 μm to 0.7 μm), and visible BBA (0.4 μm to 0.7 μm). Broadband albedo is important for calculating impurities in snowpack, especially ones that absorb light at all short wavelengths such as black carbon.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Remove tags with bad and only include albedo and upward radiation\n", + "df_bba = df[(df['type'] == 'albedo') | (df['orientation'] == 'up')]\n", + "df_bba = df_bba[(df_bba['tags'].isna() | ~df_bba['tags'].str.contains('bad', na=False))]\n", + "\n", + "df_bba\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To be brief in this document, we will show the irradiance and broadband albedo for one paired measurement (taken about the same time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_pair = df_bba[(df_bba['date'] == '2023-04-20') & (df_bba['rcr_group'] == 6) & (df_bba['instrument'] == 'S1') ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting this, we can see that the signal is a bit messy in the longer wavelengths due to clouds and atmosphere." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Get the data and save to a simpler dataframe\n", + "df_bba_example_albedo = df_pair[df_pair['type'] == 'albedo']\n", + "df_bba_example_up = df_pair[df_pair['orientation'] == 'up']\n", + "df_test = pd.DataFrame(data=df_bba_example_albedo.wavelength.values, columns=['wavelength'])\n", + "df_test['albedo'] = df_bba_example_albedo.value.values\n", + "df_test['irrad'] = df_bba_example_up.value.values\n", + "\n", + "# Plot albedo\n", + "plt.scatter(df_test.wavelength, df_test.albedo)\n", + "\n", + "plt.ylim(0,1)\n", + "plt.ylabel('Albedo')\n", + "plt.xlabel('Wavelength [nm]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And so we must remove these bands before integrating for BBA." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In this code we are removing noise from these windows of bands\n", + "df_test = df_test[~df_test.iloc[:, 0].between(300, 400, inclusive='neither')]\n", + "df_test = df_test[~df_test.iloc[:, 0].between(1300, 1450, inclusive='neither')]\n", + "df_test = df_test[~df_test.iloc[:, 0].between(1750, 2000, inclusive='neither')]\n", + "df_test = df_test[~df_test.iloc[:, 0].between(2200, 2600, inclusive='neither')]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now plotting again to show the difference,\n", + "# Plot albedo\n", + "plt.scatter(df_test.wavelength, df_test.albedo)\n", + "plt.ylim(0,1)\n", + "plt.ylabel('Albedo')\n", + "plt.xlabel('Wavelength [nm]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can use numerical integration for all of the valid bands to solve for BBA " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Integrating broadband albedo\n", + "broadband = np.trapz(df_test.albedo * df_test.irrad, dx=1) / np.trapz(df_test.irrad, dx=1)\n", + "\n", + "print(f'BBA: {round(broadband,3)}')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "goshawk", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}