Technical documentation

Sar-Pre-Processing

Wrapper module to launch preprocessor

class sar_pre_processing.sar_pre_processor.PreProcessor(**kwargs)

Bases: object

static pre_process()

class sar_pre_processing.sar_pre_processor.SARPreProcessor(**kwargs)

Bases: sar_pre_processing.sar_pre_processor.PreProcessor

add_netcdf_information()

Add information from original S1 image to processed NetCDF file. - update date information (wrong date information were stored due to coregistration process) - orbitdirection (ASCENDING, DESCENDING) - relative orbit - Satellite (S1A, S1B) - Frequency

create_netcdf_stack(filename: Optional[str] = None)

create one NetCDF stack file of pre-processed data Orbitdirection: ‘0 = Ascending, 1 = Descending’ Satellite: ‘0 = Sentinel 1A, 1 = Sentinel 1B’

create_processing_file_list()

create a list with all to be processed file names

pre_process_step1()

Pre-process Sentinel-1 data - Default processing chain of SenSARP:

Pre-process S1 SLC data with SNAP’s GPT

1. apply precise orbit file

2. thermal noise removal

3. calibration

4. TOPSAR-Deburst

5. Geometric Terrain Correction

6. Radiometric Correction (after kellndorfer et al.)

7. backscatter normalisation on specified angle in config file (based on Lambert’s Law)

Output layers:

• longitude

• latitude

• localIncidenceAngle

• projectedLocalIncidenceAngle

• incidenceAngleFromEllipsoid

• elevation

• Sigma0_VV (VV-polarized Sigma nought backscatter no radiometric correction applied)

• Sigma0_VH (VH-polarized Sigma nought backscatter no radiometric correction applied)

• sigma0_vv_kelln (VV-polarized Sigma nought backscatter including radiometric correction after Kellndorfer)

• sigma0_vh (VH-polarized Sigma nought backscatter including radiometric correction after Kellndorfer)

• sigma0_vv_kelln_normalisation (VV-polarized Sigma nought backscatter including radiometric correction after Kellndorfer and normalization to one specific incidence angle)

• sigma0_vh_kelln_normalisation (VH-polarized Sigma nought backscatter including radiometric correction after Kellndorfer and normalization to one specific incidence angle)

• use processing chain of expert user

pre_process_step2()

pre_process_step1 has to be done first

pre_process_step2 only useful for processing time series data

1. co-register pre-processed data

!!! all files will get metadata of the master image !!! That is how SNAP does it! Metadata will be corrected within netcdf output files at the end of the preprocessing chain (def add_netcdf_information)

pre_process_step3()

pre_process_step1 and/or 2 has to be done first

processing time series data:

• apply multi-temporal speckle filter and single speckle filter

processing single image:

• apply single speckle filter

Output layers:

• theta (local incidence angle)

• sigma0_vv_single (single speckle filtered radiometric and geometric corrected sigma nought backscatter)

• sigma0_vh_single (single speckle filtered radiometric and geometric corrected sigma nought backscatter)

• sigma0_vv_multi (multi speckle filtered radiometric and geometric corrected sigma nought backscatter)

• sigma0_vh_multi (multi speckle filtered radiometric and geometric corrected sigma nought backscatter)

• sigma0_vv_norm_single (single speckle filtered radiometric and geometric corrected sigma nought backscatter normalized to a specific incidence angle)

• sigma0_vh_norm_single (single speckle filtered radiometric and geometric corrected sigma nought backscatter normalized to a specific incidence angle)

• sigma0_vv_norm_single (multi speckle filtered radiometric and geometric corrected sigma nought backscatter normalized to a specific incidence angle)

• sigma0_vh_norm_single (multi speckle filtered radiometric and geometric corrected sigma nought backscatter normalized to a specific incidence angle)

File list for Sar-Pre-Processing

Create List of SAR data which will be processed by sar_pre_processer module

class sar_pre_processing.file_list_sar_pre_processing.SARList(**kwargs)

Bases: object

Object for creation of file list for preprocessing of Sentinel-1 data based on configuration file

create_list(**kwargs)

Create file list for further processing

Filter option via config file - year - area of interest

Checking for

• double processed data by ESA

• area of interest contained in two tiles of same swath

netcdf-stack

class sar_pre_processing.netcdf_stack.NetcdfStackCreator(**kwargs)

Bases: object

Create NetCDF stack

create_netcdf_stack()

stacking()

stack all files into a new netcdf file created by function _create_empty_netcef_file

Attribute Dict

class sar_pre_processing.attribute_dict.AttributeDict(**entries)

Bases: object

A class to convert a nested Dictionary into an object with key-values accessibly using attribute notation (AttributeDict.attribute) instead of key notation (Dict[“key”]). This class recursively sets Dicts to objects, allowing you to recurse down nested dicts (like: AttributeDict.attr.attr)

add_entries(**entries)

add_entry(key, value)

has_entry(key)