The Land Surface Analysis Satellite Applications Facility (LSA SAF) distributes several products derived from the EUMETSAT satellites data. I am working with R
to read and process the data (the file of this example is available from the Static Auxiliary Data page of the LSA-SAF service.)
library(rgdal)
library(raster)
r <- raster("HDF5_LSASAF_USGS_DEM_Euro.hdf")
Although the file is read correctly, the projection info is missing.
> r
class : RasterLayer
dimensions : 651, 1701, 1107351 (nrow, ncol, ncell)
resolution : 1, 1 (x, y)
extent : 0, 1701, 0, 651 (xmin, xmax, ymin, ymax)
coord. ref. : NA
data source : /home/oscar/temp/HDF5_LSASAF_USGS_DEM_Euro.hdf
names : HDF5_LSASAF_USGS_DEM_Euro
values : -32768, 32767 (min, max)
If I use gdalsrsinfo
to parse the file and obtain the projection, I get ERROR 1: ERROR - failed to load SRS definition from ...
which is more or less the same if I use GDALspatialRef
in R
:
> GDALSpatialRef('HDF5_LSASAF_USGS_DEM_Euro.hdf')
[1] ""
As far as I know these files use the GEOS projection. However, if I set this PROJ.4 string and the file is transformed to a latitude-longitude projection, the results are erroneous (look at the extent of the projectExtent
output):
projection(r) <- CRS("+proj=geos +lon_0=0 +h=35785831 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs ")
> projectExtent(r, CRS(projLL))
class : RasterLayer
dimensions : 651, 1701, 1107351 (nrow, ncol, ncell)
resolution : 8.983153e-06, 9.043695e-06 (x, y)
extent : 0, 0.01528034, 0, 0.005887445 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0
It seems that the LSA-SAF data are not yet geo-corrected and the images are expressed in the "raw" Column-Row system, so this could be the root of the problem. There are Latitude and Longitude static files which could be used for remapping the rasters. However, I would like to use a more direct approach with a correct PROJ.4 string and a projection transform with gdalwarp
or rgdal::spTransform
in R
.
Answer
Finally I found a solution using the Latitude and Longitude static files together with the DEM raster:
library(raster)
library(rgdal)
dem <- raster("HDF5_LSASAF_USGS_DEM_Euro.hdf")
lat <- raster("HDF5_LSASAF_MSG_LAT_Euro_4bytesPrecision.hdf")
lon <- raster("HDF5_LSASAF_MSG_LON_Euro_4bytesPrecision.hdf")
## Only the numeric content is needed
dem <- dem[]
## Scale lat and long values
lat <- lat[]/10000
lon <- lon[]/10000
## It is important to exclude 90ยบ angles to avoid problems
## with reprojection. Besides, I reduce the data to a region
valid <- (lon < 10 & lon > -10) & (lat < 50 & lat > 30)
lat <- lat[valid]
lon <- lon[valid]
dem <- dem[valid]
dem[dem<0] <- NA
I define an SpatialPointsDataFrame
object (irregularly spaced points) using the latitude and longitude values as the coordinates, the DEM file as the data, and with the long/lat projection.
projLL <- CRS('+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0')
ll <- SpatialPointsDataFrame(cbind(lon, lat), data=data.frame(dem),
proj4string=projLL)
Next, I reproject this SpatialPointsDataFrame
to the GEOS projection with spTransform
.
projGeos <- CRS("+proj=geos +lon_0=0 +h=35785831 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs ")
xy <- spTransform(ll, projGeos)
Finally, I define an SpatialPixelsDataFrame
(a regular grid) with the new coordinates, which can be easily converted to a RasterLayer
object.
demGrid <- SpatialPixelsDataFrame(xy, data.frame(dem), tolerance=0.311898)
dem <- raster(demGrid)
> dem
class : RasterLayer
dimensions : 360, 592, 213120 (nrow, ncol, ncell)
resolution : 2986.185, 2992.444 (x, y)
extent : -883596.1, 884225.5, 3470127, 4547407 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=geos +lon_0=0 +h=35785831 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs
data source : in memory
names : dem
values : 0, 3866 (min, max)
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