i.sentinel1.pyrosargeocode - GRASS GIS manual

NAME

i.sentinel1.pyrosargeocode - Pre-process and import Sentinel-1 imagery using pyroSAR / ESA SNAP

KEYWORDS

import, raster, imagery, copernicus, sentinel, satellite, radar

SYNOPSIS

i.sentinel1.pyrosargeocode

i.sentinel1.pyrosargeocode --help

i.sentinel1.pyrosargeocode [-sfnedlmr] input=name [output_directory=name] [aoi=name] elevation=string [auxillary_data=string[,string,...]] polarizations=string[,string,...] [speckle_filter=string] [nprocs=integer] [temporary_directory=name] [register_file=name] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-s: Execute each node in graph seperately (can circumvent memory limits but takes more time)
-f: Fetch precise orbit files if possible
-n: Apply radiometric normalization
-e: If the input elevation model represents ellipsoidal heights, no Earth Gravitational Model is applied during geocoding
-d: Rescale backscatter to dB
-l: Link resulting data
-m: Link resulting data and read statistics from metadata
-r: Link resulting data and do not read statistics
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

input=name [required]: Input file or directory with Sentinel-1 imagery; Comma separated list of paths to Sentinel-1 SAFE files or single text file with list of Sentinel-1 SAFE files (one path per row)
output_directory=name: Directory where geocoded Sentinel-1 data is stored; Name for output directory where geocoded Sentinel-1 data is stored (default: ./)
aoi=name: Path to GeoJSON file with the Area Of Interest (aoi); Path to GeoJSON file with the Area Of Interest (aoi)
elevation=string [required]: Digital elevation model to use for geocoding (either a path to a GeoTiff or a linked raster map)
auxillary_data=string[,string,...]: Auxillary data to include in the output (scatteringArea and gammaSigmaRation require the n-flag); Options: incidenceAngleFromEllipsoid, localIncidenceAngle, projectedLocalIncidenceAngle, DEM, layoverShadowMask, scatteringArea, gammaSigmaRatio; Default: incidenceAngleFromEllipsoid,localIncidenceAngle,projectedLocalIncidenceAngle,layoverShadowMask
polarizations=string[,string,...] [required]: Polarizations to process; Options: VV, VH
speckle_filter=string: Apply speckle filter algorithms from ESA SNAP; Options: boxcar, median, frost, gamma_map, lee, refined_lee, lee_sigma, IDAN, mean
nprocs=integer: Number of threads for parallel computing; Default: 1
temporary_directory=name: Performance can benefit from putting temporary data on a fast storage area; Path to the directory where temporary data is stored (default: systems temporary directory)
register_file=name: File to be used to register results in a Space Time Raster Dataset

DESCRIPTION
EXAMPLES
REQUIREMENTS
SEE ALSO
REFERENCES
AUTHOR

DESCRIPTION

i.sentinel1.pyrosargeocode geocodes Sentinel-1 imagery on a user defined Digital Elevation Model (DEM) elevation using ESA SNAP Sentinel-1 Tool Box (S1TBX) through the pyrosar library. Sentinel-1 images will be projected to the Coordinate reference systems (CRS) of the input DEM, wich is assumed to match the CRS of the current LOCATION. In that process, results will be aligned with and resampled to the pixels in the input DEM.

By default, a digital elevation model with geoidal reference height is the expected input in the elevation option. During geocoding ESA SNAP applies an Earth Gravitational Model (EGM) to compute ellipsoidal heights. Users may apply a specific EGM in advance to their elevation model and use that as input, if the e-flag is set. EGMs can be downloaded as GeoTiffs here: https://cdn.proj.org/ and added to the heights in the geoidal elevation model:

r.mapcalc expression="DTM_ellipsoidal=DTM+EGM"

If the f-flag is set, a precise orbit file is fetched from ESAs web service if available (recommended). Note that there is usually a several (~ 20) days time lag after sensing time before these files are made available. If precise orbit files for the given scene are not available, the process will proceeed and give a warning.

input can be a single Sentinel-1 SAFE file (also in zipped format), a comma separated list of Sentinel-1 files or a text file with a list of Sentinel-1 files to geocode. Geocoded images are stored in GeoTIFF format in the output_directory with file names following the naming convention in pyroSAR. Resulting files are then either imported into the GRASS GIS database r.in.gdal or linked using r.external, if the l-flag, m-flag, or r-flag are given.

If only a specifc extent of the Sentinel-1 images is supposed to be geocoded, the user can define the Area of Interest aoi in form of a GeoJSON with a single geometry.

In the nprocs option, users can specify the number of parallel process to run. If more than one Sentinel-1 scene is given in the input option, nprocs number of scenes will be processed in parallel. Otherwise, nprocs defines the number of cores available to the graph processing tool (gpt) in ESA SNAP. As geocoding is quite memory demanding, the user has to consider for the amount of memory made available to ESA SNAP / gpt when activating parallel processing with nprocs > 1.

Currently, only geocoding of Sentinel-1 GRD imagery in IW mode and with VV or VH polarization is supported. For more information on the pyroSAR processing chain, please consult the pyroSAR manual, especially the documentation for processing with ESA SNAP.

EXAMPLES

i.sentinel1.pyroargeocode -dfmn input=S1A_IW_GRDH_1SDV_20200304T161544_20200304T161609_031528_03A1CB_4A98.zip \
  output_directory=./ elevation=elevation temporary_directory=/tmp speckle_filter=refined_lee \
  aoi=aoi.geojson

REQUIREMENTS

i.sentinel1.pyrosargeocode requires ESA SNAP to be installed with the Graph Processing tool (gpt) available on PATH.
In addition, the pyroSAR Python libraries is required which can be installed from pypi with:

pip install pyroSAR

REFERENCES

pyroSAR documentation
J. Truckenbrodt, F. Cremer, I. Baris, and J. Eberle. Pyrosar: a framework for large-scale sar satellite data processing. In P. Soille, S. Loekken, and S. Albani (editors): Big Data from Space, 197–200. Luxembourg, 2019. Publications Office of the European Union. doi:10.2760/848593
Truckenbrodt J, Freemantle T, Williams C, Jones T, Small D, Dubois C, Thiel C, Rossi C, Syriou A, Giuliani G. 2019: Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube. Data 2019 4(3):93. https://doi.org/10.3390/data4030093

AUTHOR

Stefan Blumentrath

SOURCE CODE

Available at: i.sentinel1.pyrosargeocode source code (history)

Accessed: Monday Sep 16 09:37:59 2024