Sensor Independent Atmospheric Correction
Sensor Independent Atmospheric Correction of optical Earth Observation (EO) data from both multispectral and hyperspectral instruments. Currently, SICOR can be applied to Sentinel-2 and EnMAP data but the implementation of additional space- and airborne sensors is under development. As a unique feature for the processing of hyperspectral data, SICOR incorporates a three phases of water retrieval based on Optimal Estimation (OE) including the calculation of retrieval uncertainties. The atmospheric modeling in case of hyperspectral data is based on the MODTRAN radiative transfer code whereas the atmospheric correction of multispectral data relies on the MOMO code. The MODTRAN trademark is being used with the express permission of the owner, Spectral Sciences, Inc. Please check the documentation for usage, examples and in depth information.
Using Anaconda or Miniconda (recommended)
Using conda (latest version recommended), SICOR is installed as follows:
Create virtual environment for SICOR (optional but recommended), and install SICOR itself:
$ conda create -c conda-forge --name sicor_env sicor $ conda activate sicor_env
Alternatively, you can of course install SICOR in an already existing environment by simply running:
$ conda install -c conda-forge sicor
conda is the preferred method to install SICOR, as it will always install the most recent stable release and automatically resolve all the dependencies.
Using pip (not recommended)
There is also a pip installer for SICOR. However, please note that SICOR depends on some open source packages that may cause problems when installed with pip. Therefore, we strongly recommend to resolve the following dependencies before the pip installer is run:
Then, the pip installer can be run by:
$ pip install sicor
Using git (not recommended)
Alternatively you can install SICOR by cloning the following repository:
git clone https://git.gfz-potsdam.de/EnMAP/sicor.git cd sicor python setup.py install
SICOR repository operations can be started using make, available options are:
$ make make options: (run make [option] to perform action): clean: Remove all build, test, coverage and Python artifacts. clean-build: Remove build artifacts including build/ dist/ and .eggs/ folders. clean-pyc: Remove Python file artifacts, e.g. pyc files. clean-test: Remove test and coverage artifacts. convert_examples_to_doc: Use nbconvert to convert jupyter notebooks in examples to doc/examples. Links to internal images are adjusted such that SPHINX documentation can be build. coverage: Use coverage to run tests and to produce a coverage report. coverage_view: Open default browser to check coverage report. docs: Generate HTML documentation using SPHINX. If example jupyer notebooks should be updated, run the target 'convert_examples_to_doc' first. download-tables (currently, only needed for multispectral case): Download tables for atmospheric correction and scene classification from google drive if not found locally (anywhere in $PATH). Gdrive might be unreliable and fail. Just try again later. Files are checked for their hash before continuing here. download-tables-all (currently, only needed for multispectral case): Download ALL tables for atmospheric correction and scene classification. examples_notebooks: Start a jupyter notebook server in the examples directory and open browser. gitlab_CI_docker: Build a docker image for CI use within gitlab. This is based on docker and required sudo access to docker. Multiple images are build, the 'sicor:latest' includes a working environment for SICOR and is used to run the tests. SICOR is not included in this image and it is cloned and installed for each test run. install: Install the package to the active Python site-packages. lint: Check style and pep8 conformity using multiple pep8 and style checkers. Flake8 and pycodestyle need to complete without error to not fail here. For now, pylint and pydocstyle are included, but their results are ignored. The target 'test' depends on 'lint' which means that testing can only be a success when linting was run without errors. Run this before any commit! nose2: Run all tests using nose2. Coverage and other plugins are included in the ini settings file. nose2_debug: Run a single test using nose2. This is useful for debugging. Change this if needed. requirements: Install requirements as defined in requirements.txt using pip. test: Run tests quickly with the default Python interpreter and without coverage. test_single: Run a single test quickly with the default Python interpreter and without coverage. This is useful for debugging errors and feel free to change the considered test case to your liking.
Usage from python:
from sicor import AC AC()from sicor.sicor_enmap import sicor_ac_enmap enmap_l2a_vnir, enmap_l2a_swir, res = sicor_ac_enmap(data_l1b, options, logger)
From command line (currently, only applicable to multispectral case):
sicor_ecmwf.py --help sicor_ac.py --help
- Sentinel-2 L1C to L2A processing
- EnMAP L1B to L2A processing
- generic atmospheric correction for hyperspectral airborne and spaceborne data
- retrieval of the three phases of water from hyperspectral data
- calculation of various retrieval uncertainties (including a posteriori errors, averaging kernels, gain matrices, degrees of freedom, information content)
- atmospheric correction for Landsat-8: work in progress
- CH4 retrieval from hyperspectral data: work in progress
This software was developed within the context of the EnMAP project supported by the DLR Space Administration with funds of the German Federal Ministry of Economic Affairs and Energy (on the basis of a decision by the German Bundestag: 50 EE 1529) and contributions from DLR, GFZ and OHB System AG. The MODTRAN trademark is being used with the express permission of the owner, Spectral Sciences, Inc.
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