Access OPeNDAP OBPG data from R. Extract point or bounded boxes (as raster).
Find a catalog of the available OBPG data offerings here
Packages from CRAN:
remotes::install_github("BigelowLab/obpg")
suppressPackageStartupMessages({
library(rnaturalearth)
library(dplyr)
library(sf)
library(stars)
library(obpg)
})See the xyzt package for more details on the example Gulf of Maine data; the location data following is adapted from that package. Here we make a bounding box and a selection of points. We also load a coastline from NaturalEarth to use with plotting.
coast = rnaturalearth::ne_coastline(returnclass = 'sf', scale = "large")
gom_box = c(xmin = -72,
xmax = -63,
ymin = 39,
ymax = 46) |>
sf::st_bbox(crs = 4326)
points = dplyr::tribble(
~id, ~name, ~lon, ~lat,
"44098", "Jeffreys Ledge", -70.2, 42.8,
"44005", "Cashes Ledge", -69.2, 43.2,
"44037", "Jordan Basin", -67.9, 43.5,
"44011", "Georges Bank", -66.6, 41.1,
"44008", "Nantucket SE", -69.2, 40.5,) |>
sf::st_as_sf(coords = c("lon", "lat"), crs = 4326)
plot(points['name'], axes = TRUE, main = "Gulf of Maine",
extent = gom_box, reset = FALSE, key.width = lcm(8), pch = 16, cex = 2)
plot(sf::st_geometry(coast), add = TRUE, border = "black")
Create a URL for one of the (many) available resources at OBPG. And then open the resource - it’s a NetCDF. Printed below is a summary of the contents of the resource including the variables and the dimensions over which the are defined.
# generate a OBPG url for a given date
url <- obpg_url(date = "2020-07-12",
mission = "MODIS",
instrument = "AQUA",
period = "DAY",
product = "SST.sst",
resolution = "9km")
# open the resource
X <- ncdf4::nc_open(url)
X## File https://oceandata.sci.gsfc.nasa.gov/opendap/MODISA/L3SMI/2020/0712/AQUA_MODIS.20200712.L3m.DAY.SST.sst.9km.nc (NC_FORMAT_CLASSIC):
##
## 3 variables (excluding dimension variables):
## byte palette[eightbitcolor,rgb]
## short sst[lon,lat]
## long_name: Sea Surface Temperature
## scale_factor: 0.00499999988824129
## add_offset: 0
## units: degree_C
## standard_name: sea_surface_temperature
## _FillValue: -32767
## valid_min: -1000
## valid_max: 10000
## display_scale: linear
## display_min: -2
## display_max: 45
## byte qual_sst[lon,lat]
## long_name: Quality Levels, Sea Surface Temperature
## _FillValue: -1
## valid_min: 0
## valid_max: 5
##
## 4 dimensions:
## eightbitcolor Size:256 (no dimvar)
## lat Size:2160
## long_name: Latitude
## units: degrees_north
## standard_name: latitude
## _FillValue: -999
## valid_min: -90
## valid_max: 90
## lon Size:4320
## long_name: Longitude
## units: degrees_east
## standard_name: longitude
## _FillValue: -999
## valid_min: -180
## valid_max: 180
## rgb Size:3 (no dimvar)
##
## 61 global attributes:
## product_name: AQUA_MODIS.20200712.L3m.DAY.SST.sst.9km.nc
## instrument: MODIS
## title: MODISA Level-3 Standard Mapped Image
## project: Ocean Biology Processing Group (NASA/GSFC/OBPG)
## platform: Aqua
## temporal_range: day
## processing_version: R2019.0
## date_created: 2020-08-22T03:07:40.000Z
## history: l3mapgen par=AQUA_MODIS.20200712.L3m.DAY.SST.sst.9km.nc.param
## l2_flag_names: LAND,HISOLZEN
## time_coverage_start: 2020-07-12T01:00:01.000Z
## time_coverage_end: 2020-07-13T02:20:00.000Z
## start_orbit_number: 96750
## end_orbit_number: 96766
## map_projection: Equidistant Cylindrical
## latitude_units: degrees_north
## longitude_units: degrees_east
## northernmost_latitude: 90
## southernmost_latitude: -90
## westernmost_longitude: -180
## easternmost_longitude: 180
## geospatial_lat_max: 90
## geospatial_lat_min: -90
## geospatial_lon_max: 180
## geospatial_lon_min: -180
## latitude_step: 0.0833333358168602
## longitude_step: 0.0833333358168602
## sw_point_latitude: -89.9583358764648
## sw_point_longitude: -179.95832824707
## spatialResolution: 9.28 km
## geospatial_lon_resolution: 0.0833333358168602
## geospatial_lat_resolution: 0.0833333358168602
## geospatial_lat_units: degrees_north
## geospatial_lon_units: degrees_east
## number_of_lines: 2160
## number_of_columns: 4320
## measure: Mean
## suggested_image_scaling_minimum: -2
## suggested_image_scaling_maximum: 45
## suggested_image_scaling_type: LINEAR
## suggested_image_scaling_applied: No
## _lastModified: 2020-08-22T03:07:40.000Z
## Conventions: CF-1.6 ACDD-1.3
## institution: NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group
## standard_name_vocabulary: CF Standard Name Table v36
## naming_authority: gov.nasa.gsfc.sci.oceandata
## id: AQUA_MODIS.20200712.L3b.DAY.SST.nc/L3/AQUA_MODIS.20200712.L3b.DAY.SST.nc
## license: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy/
## creator_name: NASA/GSFC/OBPG
## publisher_name: NASA/GSFC/OBPG
## creator_email: [email protected]
## publisher_email: [email protected]
## creator_url: https://oceandata.sci.gsfc.nasa.gov
## publisher_url: https://oceandata.sci.gsfc.nasa.gov
## processing_level: L3 Mapped
## cdm_data_type: grid
## keywords: Earth Science > Oceans > Ocean Optics > Sea Surface Temperature
## keywords_vocabulary: NASA Global Change Master Directory (GCMD) Science Keywords
## data_bins: Attribute elided: Unsupported attribute type (NC_INT64)
## data_minimum: -1.75499999523163
## data_maximum: 39.9949989318848
Below we extract data for each point, and then bind it to the point data itself.
covars <- obpg::extract(points, X, varname = obpg::obpg_vars(X))
# bind to the input
(y <- dplyr::bind_cols(points, covars))## Simple feature collection with 5 features and 3 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -70.2 ymin: 40.5 xmax: -66.6 ymax: 43.5
## Geodetic CRS: WGS 84
## # A tibble: 5 × 4
## id name geometry sst
## * <chr> <chr> <POINT [°]> <dbl>
## 1 44098 Jeffreys Ledge (-70.2 42.8) 19.4
## 2 44005 Cashes Ledge (-69.2 43.2) 19.9
## 3 44037 Jordan Basin (-67.9 43.5) 17.3
## 4 44011 Georges Bank (-66.6 41.1) 24.3
## 5 44008 Nantucket SE (-69.2 40.5) 17.4
Often a raster of a region is needed. In that case we can use a simple bounding box, or a polygon, to extract a subset raster. We return a stars object which is how raster data is handle within the simple features paradigm.
covars <- obpg::extract(gom_box, X, varname = obpg::obpg_vars(X), flip = "none")
covars## stars object with 2 dimensions and 1 attribute
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## sst 11.705 17.50375 20.2025 20.12578 22.98 27.945 5652
## dimension(s):
## from to offset delta refsys x/y
## x 1 110 -72 0.08182 WGS 84 [x]
## y 1 86 46 -0.0814 WGS 84 [y]
Plot the bounding box of data with the points we pulled above. Note that surface temperatures are also returned for open bodies of water including lakes. Apparently cloud cover is an issue on this day.
par(mfrow = c(1,2))
plot(covars, attr = 'sst', col = sf.colors(n=16), axes = TRUE, reset = FALSE)
plot(sf::st_geometry(coast), border = "black", add = TRUE)
plot(sf::st_geometry(points), add = TRUE, col = "green", pch = 19, cex = 2)
Always close the resource when you are done. That frees up the server to other tasks.
# cleanup
ncdf4::nc_close(X)Mission-length climatologies are available (for the entire mission, a particular season or month.) You can search for these. Here we find the seasonal (northern) summer.
uri <- query_obpg_climatology(
climatology = "SCSU",
res = "9km",
param = "SST.sst")
uri## [1] "https://oceandata.sci.gsfc.nasa.gov/opendap/MODISA/L3SMI/2002/0621/AQUA_MODIS.20020621_20220920.L3m.SCSU.SST.sst.9km.nc"
We open the resource and then extract rasterized data.
# open the resource
X <- ncdf4::nc_open(uri)
covars <- obpg::extract(gom_box, X, varname = obpg::obpg_vars(X), flip = "none")
covars## stars object with 2 dimensions and 1 attribute
## attribute(s):
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## sst 10.85 17.205 19.755 20.21593 23.945 31.035 2283
## dimension(s):
## from to offset delta refsys x/y
## x 1 110 -72 0.08182 WGS 84 [x]
## y 1 86 46 -0.0814 WGS 84 [y]
par(mfrow = c(1,2))
plot(covars, attr = 'sst', col = sf.colors(n=16), axes = TRUE, reset = FALSE)
plot(sf::st_geometry(coast), border = "black", add = TRUE)
plot(sf::st_geometry(points), add = TRUE, col = "green", pch = 19, cex = 2)
# cleanup
ncdf4::nc_close(X)
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