Introduction to the NASA SnowEx Snow Albedo +2023 Dataset
+Anton Surunis
+2024-04-20
+ +
+The NASA SnowEx 2023 Snow Albedo Field Campaign Dataset
+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.
+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.
+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.
+Review of Hyperspectral Data
+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. 
Surface Reflectance vs Albedo
+Hyperspectral data is often captured as either albedo or surface +reflectance.
+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.
+ +
+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.
+ +
+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.
+ +
+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.
+Field Spectrometers
+
+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.
+Data Loading and Description
+Let’s load in the data. This is a BIG file so it may take some +time…
+setwd(dirname(rstudioapi::getSourceEditorContext()$path))
+files <- list.files(pattern = ".csv")
+refl <- read.csv(files)
+as_tibble(refl)## # A tibble: 3,543,003 × 21
+## id date instrument site transect type attachment orientation lat
+## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 2 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 3 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 4 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 5 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 6 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 7 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 8 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 9 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## 10 20230407_… 2023… S2 CARI T2 ssr 8deg down 65.2
+## # ℹ 3,542,993 more rows
+## # ℹ 12 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, wavelength <int>, value <dbl>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:
+id: Unique ID number of measurement date: The date of measurement
+collection
+instrument: Code corresponding to the spectrometer identifier (S1 =
+Spectral Evolution; S2 & S7 = ASD FieldSpec4)
+site: Code of study site (CARI = Caribou-Poker Creek; DEJU = Delta
+Junction, CRMF = Creamer’s Field)
+transect: Code corresponding to transect where the measurement was taken
+(T1 = burned forest; T2 = forested; T3 = open)
+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)
+orientation: Facing of the fiber-optic attachment (down = down-facing,
+up = up-facing)
+lat: Latitude of measurement as recorded by the GPS unit
+(epsg:4269)
+long: Longitude of measurement as recorded by the GPS unit
+(epsg:4269)
+spec_time: Local date and time of measurement as reported by
+spectrometer
+depth: Snow depth in cm
+depth_alt: Altitude as given by the GPS unit
+depth_acc: Accuracy of GPS coordinates as recorded by the GPS unit
+slope: Slope of the ground surface in degrees calculated from USGS 3DEP
+DEM (10m spatial resolution) using GIS software
+aspect: Aspect of the ground surface in degrees calculated from USGS
+3DEP DEM (10m spatial resolution) using GIS software tags: Notes taken
+by notetaker with discrete notes seperated by “#”
+rcr_group: Grouping variable for albedo and irradiance
+calculations
+wavelength: Wavelength measured by spectrometer
+value: Value measured by spectrometer at the given wavelength
+
Data Preparation
+First, we replace -9999 (null) values with NA, set negative values to +0 and convert the date column to the “date” data type.
+refl <- refl %>%
+ mutate(across(where(is.numeric), ~if_else(.x == -9999, NA, .x))) %>%
+ mutate(value = if_else(value < 0, 0, value)) %>%
+ mutate(date = ymd(date))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.
+refl <- refl %>%
+ # Landcover grouping column
+ mutate(landcover = if_else(site == "CRMF","open",
+ if_else(transect == "T1", "burn",
+ if_else(transect == "T2", "forest", "open")))) %>%
+ # Season grouping column
+ mutate(season = if_else(date < ymd(20230415), "early",if_else(
+ date >= ymd(20230415) & date < ymd(20230421), "mid", "late"))) %>%
+ # Factor season so that it is in the right order
+ mutate(season = factor(season, levels = c("early", "mid", "late")))
+as_tibble(refl)## # A tibble: 3,543,003 × 23
+## id date instrument site transect type attachment orientation lat
+## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 2 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 3 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 4 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 5 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 6 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 7 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 8 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 9 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 10 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## # ℹ 3,542,993 more rows
+## # ℹ 14 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, wavelength <int>, value <dbl>,
+## # landcover <chr>, season <fct>Data Exploration: Measurement Locations
+Let’s start exploring the data by mapping our measurement +locations.
+pts <- refl %>%
+ filter(type == "ssr" | type == "albedo") %>%
+ distinct(id, .keep_all = T)
+
+pts.color <- colorFactor(
+ palette = c("ssr" = "Blue", "albedo" = "Orange"),
+ domain = pts$type)
+
+leaflet() %>%
+ addTiles() %>%
+ addCircleMarkers(
+ data = pts,
+ color = ~pts.color(type),
+ label = ~type,
+ radius = 1) %>%
+ addProviderTiles("Esri.WorldImagery")Data Exploration: Snow Surface Reflectance
+Let’s take a look at just the reflectance data.
+First, we filter our data by snow surface reflectance (ssr) +measurements only.
+refl_only <- refl %>%
+ filter(type == "ssr")
+
+as_tibble(refl_only)## # A tibble: 2,280,366 × 23
+## id date instrument site transect type attachment orientation lat
+## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 2 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 3 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 4 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 5 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 6 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 7 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 8 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 9 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 10 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## # ℹ 2,280,356 more rows
+## # ℹ 14 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, wavelength <int>, value <dbl>,
+## # landcover <chr>, season <fct>Some of the measurements have exceptionally high reflectance values, +so we filter out measurements where reflectance is too high in the +visible range.
+refl_only <- refl_only %>%
+ group_by(id) %>%
+ filter((!any(wavelength < 750 & value >= 1.2)) %>% replace_na(T))
+
+as_tibble(refl_only)## # A tibble: 2,027,514 × 23
+## id date instrument site transect type attachment orientation lat
+## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 2 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 3 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 4 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 5 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 6 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 7 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 8 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 9 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## 10 2023… 2023-04-07 S2 CARI T2 ssr 8deg down 65.2
+## # ℹ 2,027,504 more rows
+## # ℹ 14 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, wavelength <int>, value <dbl>,
+## # landcover <chr>, season <fct>Finally, we plot all the reflectance data in a faceted plot.
+wvlim <- c(350,2200)
+reflim <- c(1e-5, 1.4)
+
+# Faceted snow surface reflectance plot
+plt <- ggplot(refl_only,
+ aes(x = wavelength, y = value, group = id , color = landcover)) +
+ # Line plot
+ geom_line(size = 0.75) +
+ # Graph colors and axis limits
+ scale_color_discrete(type = c("darkorange", "darkgreen", "dodgerblue3")) +
+ scale_x_continuous(limits = wvlim, expand = c(.02, .1)) +
+ scale_y_continuous(limits = reflim, expand = expansion(mult = c(0, .1))) +
+ # X and Y labels
+ xlab("Wavelength (nm)") +
+ ylab("Snow Surface Reflectance") +
+ # Formatting
+ theme_bw() +
+ theme(
+ axis.text=element_text(size=8,face = "bold"),
+ axis.title=element_text(size=8,face = "bold"),
+ strip.text.x = element_text(
+ size = 8,
+ face = "bold"
+ ),
+ strip.text.y = element_text(
+ size = 8,
+ face = "bold"
+ )
+ ) +
+ # Facet plot by season and site
+ facet_grid(season ~ site)
+
+plt
Data Exploration: Snow Surface Albedo
+Now, we’ll focus on the albedo measurements specifically.
+First, we filter the dataset to include only albedo measurements. +Second, we filter out measurements that were deemed low quality by the +instrument operators.
+albd <- refl %>%
+ filter(type == "albedo" & (is.na(tags) | !str_detect(tags,"#bad#")))
+as_tibble(albd)## # A tibble: 421,596 × 23
+## id date instrument site transect type attachment orientation lat
+## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 2 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 3 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 4 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 5 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 6 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 7 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 8 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 9 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 10 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## # ℹ 421,586 more rows
+## # ℹ 14 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, wavelength <int>, value <dbl>,
+## # landcover <chr>, season <fct>Let’s plot the results (grouped by campaign period, transect, and +landcover type) and see how our albedo measurements look.
+# Graph limits
+wvlim <- c(350,2200)
+reflim <- c(1e-5, 1.5)
+
+# Facetted albedo plot
+plt <- ggplot(albd, aes(x = wavelength, y = value, group = id , color = landcover)) +
+ # Line plot
+ geom_path(linewidth = 0.75) +
+ # Set colors and axis ranges
+ scale_color_discrete(type = c("darkorange", "darkgreen", "dodgerblue3")) +
+ scale_x_continuous(limits = wvlim, expand = c(.02, .1)) +
+ scale_y_continuous(limits = reflim, expand = expansion(mult = c(0, .1))) +
+ # Set axis labels
+ xlab("Wavelength (nm)") +
+ ylab("Snow Surface Albedo") +
+ # Formatting
+ theme_bw() +
+ theme(
+ axis.text=element_text(size=8,face = "bold"),
+ axis.title=element_text(size=8,face = "bold"),
+ strip.text.x = element_text(
+ size = 8,
+ face = "bold"
+ ),
+ strip.text.y = element_text(
+ size = 8,
+ face = "bold"
+ )
+ ) +
+ # Facet plot by season and site
+ facet_grid(season ~ site)
+
+plt
Data Exploration: Snow Surface Albedo Summary
+Now, we can try summarizing the results with mean albedo and +confidence intervals.
+albd_avg <- albd %>%
+ # Calculate average albedo and upper/lower quartiles
+ group_by(site, season, landcover,wavelength) %>%
+ summarise(albedo_avg = mean(value), margin = qt(0.975,df=(length(value)-1))*(sd(value))/sqrt(length(value)), n = length(unique(id))) %>%
+ ungroup() %>%
+ # Calculate 95% confidence intervals
+ mutate(ymax = albedo_avg + margin, ymin = albedo_avg - margin) %>%
+ # Moving around columns
+ relocate(c(site, season, landcover), .before = wavelength)Aaaand, let’s check out the results.
+# Graph limits
+wvlim <- c(350,2200)
+reflim <- c(1e-5, 1.15)
+
+# Facet plot of average SSA + 95% confidence intervals
+plt <- ggplot(albd_avg, aes(x = wavelength, y = albedo_avg, color = landcover)) +
+ # Line plot with 95% conifidence "ribbons"
+ geom_line(linewidth = 0.75) +
+ geom_ribbon(aes(ymin = ymin, ymax = ymax, fill = landcover),
+ alpha=0.1,
+ linetype="dashed",
+ show.legend = F) +
+ # Set colors
+ scale_color_discrete(type = c("darkorange", "darkgreen", "dodgerblue3")) +
+ # Set axis limits
+ scale_x_continuous(limits = wvlim, expand = c(.02, .1)) +
+ scale_y_continuous(limits = reflim, expand = expansion(mult = c(0, .1))) +
+ # X and Y labels
+ xlab("Wavelength (nm)") +
+ ylab("Snow Surface Albedo") +
+ # Formatting
+ theme_bw() +
+ theme(
+ axis.text=element_text(size=8,face = "bold"),
+ axis.title=element_text(size=8,face = "bold"),
+ strip.text.x = element_text(
+ size = 8,
+ face = "bold"
+ ),
+ strip.text.y = element_text(
+ size = 8,
+ face = "bold"
+ )
+ ) +
+ # Facet plot by season and site
+ facet_grid(season ~ site)
+
+plt
+
Broadband Albedo
+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.
+Data Exploration: Broadband Albedo
+Let’s clean up irradiance measurements, apply corrections, and +calculate irradiance weights
+irr <- refl %>%
+ # Only use "up"-facing RCR measurements
+ filter(orientation == "up") %>%
+ # Remove measurements deemed "bad" by the field team
+ filter((is.na(tags) | !str_detect(tags,"#bad#"))) %>%
+ # Filter out noisy NIR and SWIR wavelengths
+ filter(!(dplyr::between(wavelength, 1350,1420))) %>%
+ filter(!(dplyr::between(wavelength, 1820,1930))) %>%
+ filter(wavelength <= 2400) %>%
+ # Calculate the average irradiance on each day on each transect
+ group_by(date, instrument, site, transect, wavelength) %>%
+ summarise(value = mean(value, na.rm = T)) %>%
+ group_by(date, instrument, site, transect) %>%
+ # Calculate irradiance weights
+ summarise(weight = value/sum(value), wavelength) %>%
+ # Cleaning up
+ relocate(wavelength, .before = weight)
+
+as_tibble(irr)## # A tibble: 37,380 × 6
+## date instrument site transect wavelength weight
+## <date> <chr> <chr> <chr> <int> <dbl>
+## 1 2023-04-07 S2 CARI T2 350 0.000302
+## 2 2023-04-07 S2 CARI T2 351 0.000313
+## 3 2023-04-07 S2 CARI T2 352 0.000323
+## 4 2023-04-07 S2 CARI T2 353 0.000330
+## 5 2023-04-07 S2 CARI T2 354 0.000336
+## 6 2023-04-07 S2 CARI T2 355 0.000341
+## 7 2023-04-07 S2 CARI T2 356 0.000344
+## 8 2023-04-07 S2 CARI T2 357 0.000347
+## 9 2023-04-07 S2 CARI T2 358 0.000351
+## 10 2023-04-07 S2 CARI T2 359 0.000357
+## # ℹ 37,370 more rowsNow we calculate shortwave BBA and clean up the new dataset.
+bba <- albd %>%
+ # Join the irradiance weights to the albedo dataset
+ left_join(irr, by = join_by(date,instrument,site, transect, wavelength)) %>%
+ # Apply irradiance weights to albedo measurements
+ mutate(value = value * weight, .keep = "unused") %>%
+ # Create a new ID column for later
+ mutate(id = paste(date, instrument, site, transect, rcr_group, sep = "_")) %>%
+ # Group and sum across spectrum to calculate BBA
+ group_by(date, instrument, site, transect, rcr_group) %>%
+ mutate(bba = sum(value, na.rm = T),
+ .keep = "unused") %>%
+ # Drop BBA results that are greater than 1
+ filter(bba < 1) %>%
+ # Drop the wavelength column as it is no longer needed
+ select(-wavelength) %>%
+ # Remove any duplicate measurements based on the ID we created earlier
+ distinct(id, .keep_all = T)
+
+as_tibble(bba)## # A tibble: 107 × 22
+## id date instrument site transect type attachment orientation lat
+## <chr> <date> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
+## 1 2023… 2023-04-07 S2 CARI T2 albe… rcr <NA> 65.2
+## 2 2023… 2023-04-07 S4 CARI T1 albe… rcr <NA> 65.2
+## 3 2023… 2023-04-07 S4 CARI T1 albe… rcr <NA> NA
+## 4 2023… 2023-04-07 S4 CARI T1 albe… rcr <NA> NA
+## 5 2023… 2023-04-07 S4 CARI T1 albe… rcr <NA> NA
+## 6 2023… 2023-04-07 S4 CARI T1 albe… rcr <NA> NA
+## 7 2023… 2023-04-13 S2 DEJU T1 albe… rcr <NA> 63.9
+## 8 2023… 2023-04-13 S2 DEJU T1 albe… rcr <NA> 63.9
+## 9 2023… 2023-04-13 S2 DEJU T1 albe… rcr <NA> 63.9
+## 10 2023… 2023-04-13 S2 DEJU T1 albe… rcr <NA> 63.9
+## # ℹ 97 more rows
+## # ℹ 13 more variables: long <dbl>, spec_time <chr>, depth <dbl>,
+## # depth_alt <dbl>, depth_acc <dbl>, elevation <dbl>, slope <dbl>,
+## # aspect <dbl>, tags <chr>, rcr_group <int>, landcover <chr>, season <fct>,
+## # bba <dbl>Let’s make a summary plot.
+# Graph limits
+bbalim <- c(0, 1)
+
+# Faceted boxplot (BBA)
+plt <- ggplot(bba, aes(x = transect, y = bba, fill = landcover)) +
+ # Boxplot
+ geom_boxplot() +
+ # Choose colors
+ scale_fill_manual(values = c("darkorange", "darkgreen", "dodgerblue3")) +
+ # Set graph limits
+ scale_y_continuous(limits = bbalim, expand = expansion(mult = c(0, .1))) +
+ # X and Y labels
+ xlab("Transect") +
+ ylab("Broadband Albedo (350-2400 nm)") +
+ # Formatting
+ theme_bw() +
+ theme(
+ axis.text=element_text(size=8,face = "bold"),
+ axis.title=element_text(size=8,face = "bold"),
+ strip.text.x = element_text(
+ size = 8,
+ face = "bold"
+ ),
+ strip.text.y = element_text(
+ size = 8,
+ face = "bold"
+ )
+ ) +
+ # Facet plot by season and site
+ facet_grid(factor(season, levels = c("early", "mid", "late")) ~ site)
+
+plt
Wrapping it up
+What we learned:
+-Basic intro on how to access and prepare the SnowEx 2023 Snow Albedo
+dataset
+-How to map points and filter the dataset for specific types of
+data
+-How to plot and summarize snow surface reflectance and snow
+albedo
+-How to calculate and summarize broadband albedo
+