--- title: "ICESat-2 Virtual File System Orbits" output: rmarkdown::pdf_document always_allow_html: true vignette: > %\VignetteIndexEntry{ICESat-2 Virtual File System Orbits} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} urlcolor: blue --- ```{r, include = FALSE} knitr::opts_chunk$set(fig.width = 4, fig.height = 6, fig.align = "center", fig.pos = "!H", warning = FALSE, message = FALSE, echo = TRUE, eval = TRUE) ```
In this *third* vignette I'll make use of the [GDAL Virtual File Systems](https://gdal.org/user/virtual_file_systems.html) to download *nominal* and *time specific* orbit data (from the [ICESat-2 Technical Specs website](https://icesat-2.gsfc.nasa.gov/science/specs)) for an Area of Interest (AOI). This will allow me to reduce the download and computation time once I make the requests to the [OpenAltimetry API](https://openaltimetry.org/data/swagger-ui/). The area of interest is the **Himalayas mountain range** which has some of the *highest peaks* in the world, including *mount Everest*. The following map shows the bounding box area that I'll use in this vignette,
```{r himalayas-aoi, fig.pos='h', out.width = "105%", out.height = "100%", fig.align = 'center', fig.cap = "Area of Interest", fig.alt="Display the area of interest in Himalayas", echo = F} knitr::include_graphics("himalayas_aoi.png") # mp1 = mapview::mapview(geoms_himal[2, ], legend = F) # see next code snippet for the "geoms_himal" variable # mp2 = mapview::mapview(geoms_himal[2, ], legend = F) # # # leafsync::sync(mp1, mp2, ncol = 2) ```
First, we load the data,
```r pkgs = c('IceSat2R', 'magrittr', 'sf', 'mapview', 'leaflet') load_pkgs = lapply(pkgs, require, character.only = TRUE) # load required R packages geoms_himal_pth = system.file('data_files', 'vignette_data', 'himalayas.RDS', package = "IceSat2R") geoms_himal = readRDS(geoms_himal_pth) geoms_himal # Simple feature collection with 2 features and 1 field # Geometry type: POLYGON # Dimension: XY # Bounding box: xmin: 86.35254 ymin: 26.95635 xmax: 87.29736 ymax: 28.25842 # CRS: EPSG:4326 # area_size geometry # 1 small POLYGON ((86.36902 27.66164... # 2 big POLYGON ((86.35254 26.95635... ```
Since the *Himalayas mountain range* is located in the *Eastern Hemisphere* we'll pick this as an area when calling the *IceSat2R::vsi_nominal_orbits_wkt()* function. Moreover, we'll iterate over all *8 available repeats* for the *Eastern Hemisphere* to retrieve the *Reference Ground Tracks (RGTs)* of the AOI based on the *nominal orbits*,
```r sf_wkt = sf::st_geometry(subset(geoms_himal, area_size == 'big')) centr_wkt = sf::st_coordinates(sf::st_centroid(sf_wkt)) dat_wkt = sf::st_as_text(sf_wkt) lst_out = list() for (iter in 1:8) { # iterate over all available repeats cat(paste0(iter, '.')) dat_iter = IceSat2R::vsi_nominal_orbits_wkt(orbit_area = 'eastern_hemisphere', track = 'GT7', rgt_repeat = iter, wkt_filter = dat_wkt, download_method = 'curl', download_zip = FALSE, verbose = TRUE) lst_out[[iter]] = dat_iter } # 1.The available Icesat-2 orbits will be red from 'https://icesat-2.gsfc.nasa.gov/ ... # Access the data of the technical specs website ... # Extract the .zip files and the corresponding titles ... # Keep the relevant data from the url's and titles ... # Process the nominal and time specific orbits separately ... # Adjust the Dates of the time specific orbits ... # Create the nominal orbits data.table ... # Create the time specific orbits data.table ... # Return a single data.table ... # ............. # 8.The available Icesat-2 orbits will be red from 'https://icesat-2.gsfc.nasa.gov/ ... # Access the data of the technical specs website ... # ............. # Elapsed time: 0 hours and 0 minutes and 2 seconds. # Data based on repeat and track will be kept ... # Data based on repeat and track will be kept ... # The file 'EasternHem_repeat8_GT7.kmz' will be processed ... # Total Elapsed time: 0 hours and 0 minutes and 5 seconds. ```
```r lst_out = unlist(lst_out, recursive = F) unq_rgts = as.vector(unique(unlist(lapply(lst_out, function(x) x$RGT)))) unq_rgts # [1] "96" "157" "363" "538" "599" "805" "866" "1041" "1308" "1247" ```
```{r reference-plt-1, echo = F} # #......................................................... mapview visualization # # # make the sf-objects valid # nams = names(lst_out) # lst_out = lapply(lst_out, function(x) sf::st_make_valid(x)) # names(lst_out) <- nams # # sf_wkt = sf::st_make_valid(sf_wkt) # # # Plot the two sf-objects # RGTs = mapview::mapview(lst_out, legend = F) # AOI_wkt = mapview::mapview(sf_wkt, legend = F) # # lft = RGTs + AOI_wkt # # require(magrittr) # # lft = lft@map %>% leaflet::setView(lng = as.numeric(centr_wkt[, 'X']), # lat = as.numeric(centr_wkt[, 'Y']), # zoom = 7) # lft # #....................................................... ```
For this specific use case we are interested in ICESat-2 data for a specific time period, * from *'2020-01-01'* to *'2021-01-01'* (1-year's data) Therefore, we'll make use of the *IceSat2R::vsi_time_specific_orbits_wkt()* function which queries all *15 ICESat-2 RGTs cycles* (as of March 2022) to come to the RGTs intersection for the specified 1-year time interval,
```r date_start = '2020-01-01' date_end = '2021-01-01' orb_cyc_multi = IceSat2R::vsi_time_specific_orbits_wkt(date_from = date_start, date_to = date_end, RGTs = unq_rgts, wkt_filter = dat_wkt, verbose = TRUE) # The available Icesat-2 orbits will be red from 'https://icesat-2.gsfc.nasa.gov/ ... # Access the data of the technical specs website ... # Extract the .zip files and the corresponding titles ... # Keep the relevant data from the url's and titles ... # Process the nominal and time specific orbits separately ... # Adjust the Dates of the time specific orbits ... # Create the nominal orbits data.table ... # Create the time specific orbits data.table ... # Return a single data.table ... # Elapsed time: 0 hours and 0 minutes and 0 seconds. # In total there are 5 intersected dates for which data will be processed! # The RGT cycles from which data will be processed are: # RGT_cycle_6, RGT_cycle_7, RGT_cycle_8, RGT_cycle_9, RGT_cycle_10 # ------------------------------------------------- # RGTs of cycle 'RGT_cycle_6' will be processed ... # ------------------------------------------------- # The 'sf' gdalinfo returned an empty character string! Attempt to read the url using # the OS configured 'gdalinfo' function ... # The internal type of the .zip file is 'kml' # The 'https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/IS2_RGTs_cycle6_...' # 'zip' file includes 1387 'kml' files. # Elapsed time: 0 hours and 0 minutes and 8 seconds. # 6 out of 10 sublists were empty and will be removed! # ------------------------------------------------- # RGTs of cycle 'RGT_cycle_7' will be processed ... # ------------------------------------------------- # # ................. # # ------------------------------------------------- # RGTs of cycle 'RGT_cycle_10' will be processed ... # ------------------------------------------------- # The 'sf' gdalinfo returned an empty character string! Attempt to read the url using th ... # 'gdalinfo' function ... # The internal type of the .zip file is 'kml' # The 'https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/IS2_RGTs_cycle10_date ... # 'zip' file includes 1387 'kml' files. # Elapsed time: 0 hours and 0 minutes and 6 seconds. # 6 out of 10 sublists were empty and will be removed! # In total 5 RGT cycles will be included in the output 'sf' object (RGT_cycle_6, RGT_cycle_7, # RGT_cycle_8, RGT_cycle_9, RGT_cycle_10)! # output of 'RGT_cycle_6' will be re-formatted ... # The 'description' column of the output data will be processed ... # output of 'RGT_cycle_7' will be re-formatted ... # The 'description' column of the output data will be processed ... # output of 'RGT_cycle_8' will be re-formatted ... # The 'description' column of the output data will be processed ... # output of 'RGT_cycle_9' will be re-formatted ... # The 'description' column of the output data will be processed ... # output of 'RGT_cycle_10' will be re-formatted ... # The 'description' column of the output data will be processed ... # Total Elapsed time: 0 hours and 2 minutes and 37 seconds. ```
The query returns 18 different Date-Time matches for our defined 1-year time period,
```r orb_cyc_multi # Simple feature collection with 18 features and 14 fields # Geometry type: POINT # Dimension: XY # Bounding box: xmin: 86.45225 ymin: 27.09347 xmax: 87.22874 ymax: 27.11331 # CRS: EPSG:4326 # First 10 features: # .... drawOrder icon RGT Date_time day_of_year cycle geometry # 1 .... NA 96 2020-01-02 00:37:11 2 6 POINT (86.97015 27.10272) # 2 .... NA 538 2020-01-30 23:13:14 30 6 POINT (87.22874 27.09347) # 3 .... NA 599 2020-02-03 23:04:54 34 6 POINT (86.45225 27.11331) # 4 .... NA 1041 2020-03-03 21:40:57 63 6 POINT (86.71086 27.1045) # 5 .... NA 96 2020-04-01 20:17:02 92 7 POINT (87.09815 27.08729) # 6 .... NA 599 2020-05-04 18:44:45 125 7 POINT (86.58026 27.09789) # 7 .... NA 1041 2020-06-02 17:20:48 154 7 POINT (86.83886 27.08907) # 8 .... NA 96 2020-07-01 15:56:55 183 8 POINT (87.00215 27.09888) # 9 .... NA 538 2020-07-30 14:32:58 212 8 POINT (87.26075 27.08963) # 10 .... NA 599 2020-08-03 14:24:38 216 8 POINT (86.48426 27.10947) ```
We'll use the *mapview* R package to visualize our AOI bounding box with the intersected time-specific RGTs,
```r # make the sf-objects valid orb_cyc_multi = sf::st_make_valid(orb_cyc_multi) sf_wkt = sf::st_make_valid(sf_wkt) # plot the sf-objects orbit_cy = mapview::mapview(orb_cyc_multi, legend = F) AOI_wkt = mapview::mapview(sf_wkt, legend = F) lft = orbit_cy + AOI_wkt lft = lft@map %>% leaflet::setView(lng = as.numeric(centr_wkt[, 'X']), lat = as.numeric(centr_wkt[, 'Y']), zoom = 7) lft ```
```{r himalayas-rgts, fig.pos='h', out.width = "80%", out.height = "80%", fig.align = 'center', fig.cap = "Intersected RGTs", fig.alt="Intersected Reference Ground Tracks", echo = F} knitr::include_graphics("himalayas_rgts.png") ``` \newpage The output of *'vsi_time_specific_orbits_wkt()'* can be verified with the *OpenAltimetry's 'getTracks()'* function,
```r bbx_aoi = sf::st_bbox(obj = sf_wkt) dtbl_rgts = verify_RGTs(nsidc_rgts = orb_cyc_multi, bbx_aoi = bbx_aoi, verbose = TRUE) dtbl_rgts # Date_time RGT_OpenAlt RGT_NSIDC # 1: 2020-01-02 96 96 # 2: 2020-01-30 538 538 # 3: 2020-02-03 599 599 # 4: 2020-03-03 1041 1041 # 5: 2020-04-01 96 96 # 6: 2020-05-04 599 599 # 7: 2020-06-02 1041 1041 # 8: 2020-07-01 96 96 # 9: 2020-07-30 538 538 # 10: 2020-08-03 599 599 # 11: 2020-09-01 1041 1041 # 12: 2020-09-30 96 96 # 13: 2020-11-02 599 599 # 14: 2020-12-01 1041 1041 # 15: 2020-12-30 96 96 # 16: 2021-01-28 538 538 # 17: 2021-02-01 599 599 # 18: 2021-03-02 1041 1041 ```