Just a quick note to announce that the makeGlobcolourField and isin.convert functions have been added to the sinkr package. In addition, the makeGlobcolourField function now used the ncdf4 package to read the .nc files. Both functions are only set up to deal with the higher resolution 4 km data based on the ISIN grid ("L3b").
The following script is an example of extracting data for the Philippines, and produces a map of mean Chl1 values:
Example script:
Showing posts with label remote sensing. Show all posts
Showing posts with label remote sensing. Show all posts
Wednesday, February 17, 2016
Working with Globcolour data (Part 2)
Labels:
chlorophyll,
map,
map projection,
netcdf,
ocean color,
R,
remote sensing,
sinkr,
sparse data,
spatial
Friday, November 8, 2013
Working with hdf files in R - Example: Pathfinder SST data
Following a question that I posted on stackoverflow.com, I recieved the great advice to use the Bioconductor rhdf5 package to work with HDF5 files. The package is not located on CRAN, but can be sourced from the Bioconductor website:
source("http://bioconductor.org/biocLite.R") biocLite("rhdf5")
As an example, I use the package to extract Pathfinder sea surface temperature (SST) data, available in netCDF-4 format (the features of netCDF-4 are a subset of the features of HDF5). This type of file is not readable by the netCDF package ncdf.The result is the above plot of a subarea from one of the daily data sets.
To reproduce the figure, you will need the image.scale and val2col functions found on this blog.
To reproduce example:
Labels:
climate science,
hdf5,
imaging,
map,
netcdf,
R,
remote sensing
Monday, April 2, 2012
Working with Globcolour data
The Globcolour project (http://www.globcolour.info/)
provides relatively easy access to ocean color remote sensing data. Data is
provided at http://hermes.acri.fr/
and the following parameters are available:
· Chlorophyll-a (CHL1 and CHL2)
· Fully normalised water leaving radiances at 412, 443, 490,
510, 531, 550-565, 620, 665-
670, 681 and 709 nm (Lxxx)
· Coloured dissolved and detrital organic materials
absorption coefficient (CDM)
· Diffuse attenuation coefficient (Kd(490))
· Particulate back-scattering coefficient (bbp)
· Total Suspended Matter (TSM)
· Relative excess of radiance at 555 nm (EL555)
· Photosynthetic Available Radiation (PAR)
· Heated layer depth (ZHL)
· Secchi disk depth (ZSD)
· Primary production (PP)
· Aerosol optical thickness over water (T865)
· Cloud Fraction (CF)
Of particular interest to ecologists are the estimates of Chlorophyll a (chla) , which combines data from several satellites for better coverage - SeaWiFS (NASA), MODIS (NASA), MERIS (ESA). Data is available
at several temporal (daily, 8-days, and monthly averages) and spatial (4.63 km,
0.25°, and 1°) resolutions for the global domain. Several merged products are available: simple averaging (AV), weighted averaging (AVW), and Garver, Siegel, Maritorena Model (GSM) [for more information see the Product User Guide].
Due to the gappy nature of the data (e.g. due to land and clouds), many of the data products only provide values at grids where estimation was possible. For high resolution data, such as in 4.63 km resolution daily estimates, grids with values are often far fewer than the total number of ISIN grids (n=23,761,676) used by the product. This saves space in the files for download, but you may need to reconstruct the field (with NaN's included for grids without observations) for some analyses.
Due to the gappy nature of the data (e.g. due to land and clouds), many of the data products only provide values at grids where estimation was possible. For high resolution data, such as in 4.63 km resolution daily estimates, grids with values are often far fewer than the total number of ISIN grids (n=23,761,676) used by the product. This saves space in the files for download, but you may need to reconstruct the field (with NaN's included for grids without observations) for some analyses.
The following example shows how to retrieve Globcolour data
and process it using R. Global data is available, but I have provided
instructions for processing a smaller area of 4.63 km resolution chla data from
the Galapagos archipelago. One can define lat/lon limits for the desired area on the http://hermes.acri.fr/ interface. An ftp address will be sent via Email as to the location of the data when finished.
Labels:
chlorophyll,
climate science,
gappy data,
map,
map projection,
netcdf,
ocean color,
phytoplankton,
R,
remote sensing,
sparse data,
spatial
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