vignettes/IceSat-2_Mission_Orbits_HTML.Rmd
IceSat-2_Mission_Orbits_HTML.RmdThis first vignette demonstrates how to download and process time specific orbits. We’ll use one of the Reference Ground Track (RGT) cycles and merge it with other data sources with the purpose to visualize specific areas.
We’ll load one of the latest which is “RGT_cycle_14” (from December 22, 2021 to March 23, 2022). The documentation of the “RGT_cycle_14” data includes more details on how a user can come to the same data format for any of the RGT Cycles.
pkgs = c('IceSat2R', 'magrittr', 'mapview', 'sf', 'rnaturalearth',
'data.table', 'DT', 'stargazer')
load_pkgs = lapply(pkgs, require, character.only = TRUE) # load required R packages
sf::sf_use_s2(use_s2 = FALSE) # disable 's2' in this vignette
mapview::mapviewOptions(leafletHeight = '600px',
leafletWidth = '700px') # applies to all leaflet maps
#.............................
# load the 'RGT_cycle_14' data
#.............................
data(RGT_cycle_14)
res_rgt_many = sf::st_as_sf(x = RGT_cycle_14, coords = c('longitude', 'latitude'), crs = 4326)
res_rgt_many## Simple feature collection with 131765 features and 6 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -179.9986 ymin: -87.66742 xmax: 179.9984 ymax: 87.3305
## Geodetic CRS: WGS 84
## First 10 features:
## day_of_year Date hour minute second RGT geometry
## 1 356 2021-12-22 7 57 49 1 POINT (-0.1318472 0.02795893)
## 2 356 2021-12-22 7 58 49 1 POINT (-0.5162124 3.868758)
## 3 356 2021-12-22 7 59 49 1 POINT (-0.901809 7.709809)
## 4 356 2021-12-22 8 0 49 1 POINT (-1.289879 11.55065)
## 5 356 2021-12-22 8 1 49 1 POINT (-1.681755 15.39082)
## 6 356 2021-12-22 8 2 49 1 POINT (-2.078916 19.2299)
## 7 356 2021-12-22 8 3 49 1 POINT (-2.483051 23.06748)
## 8 356 2021-12-22 8 4 49 1 POINT (-2.896146 26.90316)
## 9 356 2021-12-22 8 5 49 1 POINT (-3.3206 30.73662)
## 10 356 2021-12-22 8 6 49 1 POINT (-3.759374 34.56754)We’ll proceed to merge the orbit geometry points with the countries data of the rnaturalearth R package (1:110 million scales) and for this purpose, we keep only the “sovereignt” and “sov_a3” columns,
cntr = rnaturalearth::ne_countries(scale = 110, type = 'countries', returnclass = 'sf')
cntr = cntr[, c('sovereignt', 'sov_a3')]
cntr## Simple feature collection with 177 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
## Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 10 features:
## sovereignt sov_a3 geometry
## 0 Fiji FJI MULTIPOLYGON (((180 -16.067...
## 1 United Republic of Tanzania TZA MULTIPOLYGON (((33.90371 -0...
## 2 Western Sahara SAH MULTIPOLYGON (((-8.66559 27...
## 3 Canada CAN MULTIPOLYGON (((-122.84 49,...
## 4 United States of America US1 MULTIPOLYGON (((-122.84 49,...
## 5 Kazakhstan KAZ MULTIPOLYGON (((87.35997 49...
## 6 Uzbekistan UZB MULTIPOLYGON (((55.96819 41...
## 7 Papua New Guinea PNG MULTIPOLYGON (((141.0002 -2...
## 8 Indonesia IDN MULTIPOLYGON (((141.0002 -2...
## 9 Argentina ARG MULTIPOLYGON (((-68.63401 -...We then merge the orbit points with the country geometries and specify also “left = TRUE” to keep also observations that do not intersect with the rnaturalearth countries data,
dat_both = suppressMessages(sf::st_join(x = res_rgt_many,
y = cntr,
join = sf::st_intersects,
left = TRUE))
dat_both## Simple feature collection with 131765 features and 8 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -179.9986 ymin: -87.66742 xmax: 179.9984 ymax: 87.3305
## Geodetic CRS: WGS 84
## First 10 features:
## day_of_year Date hour minute second RGT sovereignt sov_a3
## 1 356 2021-12-22 7 57 49 1 <NA> <NA>
## 2 356 2021-12-22 7 58 49 1 <NA> <NA>
## 3 356 2021-12-22 7 59 49 1 Ghana GHA
## 4 356 2021-12-22 8 0 49 1 Burkina Faso BFA
## 5 356 2021-12-22 8 1 49 1 Mali MLI
## 6 356 2021-12-22 8 2 49 1 Mali MLI
## 7 356 2021-12-22 8 3 49 1 Mali MLI
## 8 356 2021-12-22 8 4 49 1 Algeria DZA
## 9 356 2021-12-22 8 5 49 1 Algeria DZA
## 10 356 2021-12-22 8 6 49 1 Morocco MAR
## geometry
## 1 POINT (-0.1318472 0.02795893)
## 2 POINT (-0.5162124 3.868758)
## 3 POINT (-0.901809 7.709809)
## 4 POINT (-1.289879 11.55065)
## 5 POINT (-1.681755 15.39082)
## 6 POINT (-2.078916 19.2299)
## 7 POINT (-2.483051 23.06748)
## 8 POINT (-2.896146 26.90316)
## 9 POINT (-3.3206 30.73662)
## 10 POINT (-3.759374 34.56754)The unique number of RGT’s for “RGT_cycle_14” are
## [1] 1387We observe that from December 22, 2021 to March 23, 2022,
df_tbl = data.frame(table(dat_both$sovereignt), stringsAsFactors = F)
colnames(df_tbl) = c('country', 'Num_IceSat2_points')
df_subs = dat_both[, c('RGT', 'sovereignt')]
df_subs$geometry = NULL
df_subs = data.table::data.table(df_subs, stringsAsFactors = F)
colnames(df_subs) = c('RGT', 'country')
df_subs = split(df_subs, by = 'country')
df_subs = lapply(df_subs, function(x) {
unq_rgt = sort(unique(x$RGT))
items = ifelse(length(unq_rgt) < 5, length(unq_rgt), 5)
concat = paste(unq_rgt[1:items], collapse = '-')
iter_dat = data.table::setDT(list(country = unique(x$country),
Num_RGTs = length(unq_rgt),
first_5_RGTs = concat))
iter_dat
})
df_subs = data.table::rbindlist(df_subs)
df_tbl = merge(df_tbl, df_subs, by = 'country')
df_tbl = df_tbl[order(df_tbl$Num_IceSat2_points, decreasing = T), ]
DT_dtbl = DT::datatable(df_tbl, rownames = FALSE)all RGT’s (1387 in number) intersect with “Antarctica” and almost all with “Russia”.
The onshore and offshore number of ICESat-2 points and percentages for the “RGT_cycle_14” equal to
num_sea = sum(is.na(dat_both$sovereignt))
num_land = sum(!is.na(dat_both$sovereignt))
perc_sea = round(num_sea / nrow(dat_both), digits = 4) * 100.0
perc_land = round(num_land / nrow(dat_both), digits = 4) * 100.0
dtbl_land_sea = data.frame(list(percentage = c(perc_sea, perc_land),
Num_Icesat2_points = c(num_sea, num_land)))
row.names(dtbl_land_sea) = c('sea', 'land')
stargazer::stargazer(dtbl_land_sea,
type = 'html',
summary = FALSE,
rownames = TRUE,
header = FALSE,
table.placement = 'h',
title = 'Land and Sea Proportions')| percentage | Num_Icesat2_points | |
| sea | 67.070 | 88,369 |
| land | 32.930 | 43,396 |
We can also observe the ICESat-2 “RGT_cycle_14” coverage based on the 1 to 10 million large scale Natural Earth Glaciated Areas data,
data(ne_10m_glaciated_areas)We’ll restrict the processing to the major polar glaciers (that have a name included),
ne_obj_subs = subset(ne_10m_glaciated_areas, !is.na(name))
ne_obj_subs = sf::st_make_valid(x = ne_obj_subs) # check validity of geometries
ne_obj_subs## Simple feature collection with 68 features and 5 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89.99993 xmax: 180 ymax: 82.96573
## Geodetic CRS: WGS 84
## First 10 features:
## recnum scalerank featurecla name min_zoom
## 143 143 3 Glaciated areas Mount Brown Icefield 2.1
## 148 148 5 Glaciated areas Braithwaite Icefield 5.0
## 152 152 3 Glaciated areas Hooker Icefield 2.1
## 206 206 5 Glaciated areas Homathko Icefield 5.0
## 214 214 6 Glaciated areas Clachnacudainn Icefield 5.7
## 215 215 6 Glaciated areas Albert Icefield 5.7
## 228 228 3 Glaciated areas Plateau Icefield 2.1
## 230 230 5 Glaciated areas Pemberton Icefield 5.0
## 256 256 3 Glaciated areas Cambria Icefiled 2.1
## 273 0 3 Glaciated areas Lyell Icefield 2.1
## geometry
## 143 POLYGON ((-118.4066 52.7965...
## 148 POLYGON ((-119.9303 52.6144...
## 152 POLYGON ((-117.8572 52.5404...
## 206 POLYGON ((-124.6489 51.3257...
## 214 POLYGON ((-118.0284 51.1342...
## 215 POLYGON ((-117.6752 51.0917...
## 228 POLYGON ((-123.8453 50.5810...
## 230 POLYGON ((-123.3869 50.5279...
## 256 POLYGON ((-129.661 56.09113...
## 273 POLYGON ((-117.2649 52.0351...and we’ll visualize the subset using the mapview package,
mpv = mapview::mapview(ne_obj_subs,
color = 'cyan',
col.regions = 'blue',
alpha.regions = 0.5,
legend = FALSE)
mpvWe will see which orbits of the ICESat-2 “RGT_cycle_14” intersect with these major polar glaciers,
res_rgt_many$id_rgt = 1:nrow(res_rgt_many) # include 'id' for fast subsetting
dat_glac_sf = suppressMessages(sf::st_join(x = ne_obj_subs,
y = res_rgt_many,
join = sf::st_intersects))
dat_glac = data.table::data.table(sf::st_drop_geometry(dat_glac_sf), stringsAsFactors = F)
dat_glac = dat_glac[complete.cases(dat_glac), ] # keep non-NA observations
dat_glac## recnum scalerank featurecla name min_zoom day_of_year
## 1: 952 4 Glaciated areas Jostedalsbreen 3.0 40
## 2: 1696 3 Glaciated areas Agassiz Ice Cap 2.1 357
## 3: 1696 3 Glaciated areas Agassiz Ice Cap 2.1 358
## 4: 1696 3 Glaciated areas Agassiz Ice Cap 2.1 361
## 5: 1696 3 Glaciated areas Agassiz Ice Cap 2.1 362
## ---
## 13245: 0 3 Glaciated areas Kluane Ice Cap 2.1 42
## 13246: 0 3 Glaciated areas Kluane Ice Cap 2.1 44
## 13247: 0 3 Glaciated areas Kluane Ice Cap 2.1 48
## 13248: 0 3 Glaciated areas Kluane Ice Cap 2.1 71
## 13249: 0 3 Glaciated areas Kluane Ice Cap 2.1 73
## Date hour minute second RGT id_rgt
## 1: 2022-02-09 17 23 15 755 71662
## 2: 2021-12-23 1 41 0 12 1072
## 3: 2021-12-24 12 10 22 34 3157
## 4: 2021-12-27 1 32 40 73 6867
## 5: 2021-12-28 12 2 3 95 8952
## ---
## 13245: 2022-02-11 14 42 39 784 74402
## 13246: 2022-02-13 3 6 19 807 76602
## 13247: 2022-02-17 2 57 59 868 82397
## 13248: 2022-03-12 13 18 42 1226 116392
## 13249: 2022-03-14 1 42 22 1249 118592We’ll split the merged data by the ‘name’ of the glacier,
dat_glac_name = split(x = dat_glac, by = 'name')
sum_stats_glac = lapply(dat_glac_name, function(x) {
dtbl_glac = x[, .(name_glacier = unique(name),
Num_unique_Dates = length(unique(Date)),
Num_unique_RGTs = length(unique(RGT)))]
dtbl_glac
})
sum_stats_glac = data.table::rbindlist(sum_stats_glac)
sum_stats_glac = sum_stats_glac[order(sum_stats_glac$Num_unique_RGTs, decreasing = T), ]The next table shows the total number of days and RGTs for each one of the major polar glaciers,
stargazer::stargazer(sum_stats_glac,
type = 'html',
summary = FALSE,
rownames = FALSE,
header = FALSE,
table.placement = 'h',
title = 'Days and RGTs')| name_glacier | Num_unique_Dates | Num_unique_RGTs |
| Antarctic Ice Sheet | 92 | 1,387 |
| Greenland Ice Sheet | 91 | 352 |
| Agassiz Ice Cap | 56 | 58 |
| Academy of Sciences Ice Cap | 34 | 34 |
| Manson Icefield | 14 | 19 |
| Müller Ice Cap | 16 | 16 |
| Kluane Ice Cap | 12 | 12 |
| Sydkap Ice Cap | 6 | 7 |
| Southern Patagonian Ice Field | 5 | 5 |
| Stikine Icecap | 4 | 4 |
| Vestfonna | 3 | 3 |
| Brasvellbreen | 3 | 3 |
| Northern Patagonian Ice Field | 2 | 2 |
| Jostedalsbreen | 1 | 1 |
We can restrict to one of the glaciers to visualize the ICESat-2 “RGT_cycle_14” coverage over this specific area (‘Southern Patagonian Ice Field’),
sample_glacier = 'Southern Patagonian Ice Field'
dat_glac_smpl = dat_glac_name[[sample_glacier]]
cols_display = c('name', 'day_of_year', 'Date', 'hour', 'minute', 'second', 'RGT')
stargazer::stargazer(dat_glac_smpl[, ..cols_display],
type = 'html',
summary = FALSE,
rownames = FALSE,
header = FALSE,
table.placement = 'h',
title = 'Southern Patagonian Ice Field')| name | day_of_year | Date | hour | minute | second | RGT |
| Southern Patagonian Ice Field | 357 | 2021-12-23 | 0 | 40 | 43 | 11 |
| Southern Patagonian Ice Field | 2 | 2022-01-02 | 12 | 28 | 4 | 171 |
| Southern Patagonian Ice Field | 20 | 2022-01-20 | 23 | 16 | 46 | 453 |
| Southern Patagonian Ice Field | 49 | 2022-02-18 | 21 | 52 | 48 | 895 |
| Southern Patagonian Ice Field | 64 | 2022-03-05 | 9 | 31 | 50 | 1,116 |
and we gather the intersected RGT coordinates points with the selected glacier,
subs_rgts = subset(res_rgt_many, id_rgt %in% dat_glac_smpl$id_rgt)
set.seed(1)
samp_colrs = sample(x = grDevices::colors(distinct = TRUE),
size = nrow(subs_rgts))
subs_rgts$color = samp_colrs
ne_obj_subs_smpl = subset(ne_obj_subs, name == sample_glacier)
mpv_glacier = mapview::mapview(ne_obj_subs_smpl,
color = 'cyan',
col.regions = 'blue',
alpha.regions = 0.5,
legend = FALSE)
mpv_RGTs = mapview::mapview(subs_rgts,
color = subs_rgts$color,
alpha.regions = 0.0,
lwd = 6,
legend = FALSE)and visualize both the glacier and the subset of the intersected RGT coordinate points (of the different Days) in the same map. The clickable map and point popups include more information,
lft = mpv_glacier + mpv_RGTs
lft