Current S3Snow processor version: 2.3

The repository contains 3 tools:

• s3_extract_snow_products: the script is designed to extract the outputs from the S3 OLCI SNOW processor based on a list of Sentinel-3 (Hereafter “S3”) OLCI imagery, for a named list of user-defined lat/lon coordinates.
• s3_band_extract: the script allows to extract values from S3 bands (OLCI or SLSTR) from a list of S3 images for a named list of user-defined lat/lon coordinates.
• list_sat_bands: returns a list of all available bands from an S3 OLCI or SLSTR scene.

The work requires SNAP 7 and the following experimental SNAP plugins:

• s3tbx-snow-2.3-SNAPSHOT
• s3tbx-olci-o2corr-0.81-SNAPSHOT
• s3tbx-idepix-olci-s3snow-0.82-SNAPSHOT
• snap-slope-0.92-SNAPSHOT (if the S3 SNOW DEM products are to extracted)

Code developed within the Sentinel-3 for Science, Land Study 1: Snow project led by Jason Box. More information at snow.geus.dk.

Development and testing: Maxim Lamare, Jason Box, and Jonas Kvist Andersen.

1. Introduction
2. Requirements and setup
3. Workflow

The s3_extract_snow_products script, developed by the S34Sci Land Study 1: Snow, extracts the outputs of the S3Snow algorithm results for a list of S3 OLCI L1C scenes for a user-supplied list of latitude and longitude coordinates.

The processing of the S3 images is performed with snappy: the SNAP Java API from Python. The installation of the libraries is detailed below. The use of Anaconda is strongly recommended to run the scripts provided in this repository, and the installation steps provided assume the use of a Conda environment.

Note: there are 2 branches on this repository:

1. the master branch, designed for Mac OS
2. a Linux branch

There is currently no Windows OS support.

Setup steps:

1. You can get Anaconda or Miniconda here
2. The current version of the script was designed to work with Python 3.4 or 3.5.
3. Install SNAP 7 and the S3Snow plugins (listed below). The S34Sci Snow SUM describes the plugins.
4. Install or verify that JDK is installed on your system and JDK_HOME path is set.
5. Create a Conda environment (see steps below) and configure snappy to work with the environment's Python interpreter (the hardest part). The instructions are found here.

For MacOS users @mankoff created an installation guide (essentially the same for Linux or Windows systems):

# create conda environement
conda create -n SNAP python=3.4
source activate SNAP

# install pandas
conda install pandas

# install jpy
cd /local/folder/of/your/choice
git clone https://github.com/bcdev/jpy.git
cd jpy/
python setup.py bdist_wheel
cp dist/*.whl ~/.snap/snap-python/snappy

# set up snappy
# NOTE: customize paths here to where SNAP and Anaconda are
  installed
~/Applications/snap/bin/snappy-conf ~/local/anaconda/envs/SNAP/bin/python

cd ~/.snap/snap-python/snappy
python setup.py install

# test
python
import snappy # no error message = works.

Note: for advanced users, the Conda environment file is provided in the repository > req.txt

Run python s3_extract_snow_products.py -h for help.

The scripts needs the following obligatory inputs:

• -i, --input: the path to the folder containing unzipped S3 OLCI L1C granules (scenes). Each unzipped folder (.SEN3) contains the NetCDF data files (.nc) and an XML file (.xml). The script will also access S3 scenes that are located in sub-directories in the input path.
• -c, --coords: the path to a file containing the coordinates of the pixels values to extract from the S3 images. The file should be in a .csv format with each row containing: Name, lat, lon, with the latitude and longitude in degrees (EPSG:4326). I.E; Inukjuak, 58.4550, -78.1037
• -o, --output: the path to the output folder, where a .csv file for each site will be created, containing the output values from the S3Snow processor. A list of the S3 scenes for which the algorithm failed is created in a separate file. See note below.

The following optional inputs can be specified:

• -p, --pollution: activate the snow pollution option in the S3Snow processor. To run the processor for polluted snow: "yes", "true", "t", "y", or "1". To run the processor for clean snow: "no", "false", "f", "n", or "0". By default, the snow pollution flag is deactivated.

• -d, --delta_p: set the trigger to activate snow polluted mode. If the pollution flag is activated, and if the difference between the measured reflectance and the theoretical reflectance for clean snow is lower than the specified delta_p value, the polluted snow algorithm is activated. If the difference is smaller than the delta_p, the snow is considered clean and the polluted snow algorithm is not activated. Has no effect if the pollution flag is turned off.

• -g, --gains: multiply the Bottom-of-Atmosphere reflectance by the gains specified by the Ocean Color SVC before calculating albedo. Expert option only: it is not recommended to activate this option. To activate the use of the gains: "yes", "true", "t", "y", or "1". To deactivate the use of the gains: "no", "false", "f", "n", or "0". By default, the gains flag is deactivated.

• -e, --elevation: run the S3Snow slope processor that calculates the elevation, slope, aspect, and subpixel variance from the DEM. The algorithm currently uses the default DEM band that is provided within the S3 OLCI product. To run the slope processor use: "yes", "true", "t", "y", or "1". To run the algorithm without the aforementioned variables in the output specify the options: "no", "false", "f", "n", or "0". By default, the option is turned off.

• -r, --recovery: run the algorithm in recovery mode. If the processing stopped in the middle of a run for some reason, the output temporary files are not sorted. The recovery mode will attempt to convert the temporary files to final files, therefore reducing the number of images to re-process the next time. A manual selection of the unprocessed scenes in the input folder will be necessary to run only the unprocessed scenes after recovery mode (don't forget to save the recovery mode output files elsewhere or they will be overwritten). In recovery mode, the elevation flag has to be set to the value of the failed run otherwise the code will crash. Activate recovery mode by setting the flag to "yes", "true", "t", "y", or "1". To run in normal mode: "no", "false", "f", "n", or "0". By default, the option is turned off.

• -f, --platform specify the Sentinel-3 platform (i.e. Sentinel-3A, -3B, or both) to include data from. Options are 'A', 'B', or 'AB' (for both platforms).

Example run:

python s3_extract_snow_products.py -i "/path/to/folder/containing/S3/folders"\
-c "/path/to/input/csvfile.csv" -o "/path/to/output/folder" -p false -d 0.05 -g false

Outputs: The output csv file contains:

• Year, Month, Day, Hour, Minute, Second of acquisition
• Day of year of acquisition
• Satellite platform ID (S3A = 0, S3B = 0)
• Grain Diameter (mm), SSA (m².kg^-1)
• NDSI, NDBI
• Cloud flag (cloud = 1, no cloud = 0)
• Solar and viewing angles
• Planar and spherical albedo (Shortwave, Visible, NIR)
• TOA reflectance for the 21 bands (Oa_reflectance)
• BOA reflectance for the 21 bands (rBRR)
• Spectral planar albedo for the 21 bands

Run python s3_band_extract.py -h for help.

The scripts needs the following obligatory inputs:

• -i, --insat: the path to the folder containing unzipped S3 OLCI L1C granules (scenes). Each unzipped folder (.SEN3) contains the NetCDF data files (.nc) and an XML file (.xml).
• -c, --coords: the path to a file containing the coordinates of the pixels values to extract from the S3 images. The file should be in a .csv format with each row containing: Name, lat, lon, with the latitude and longitude in degrees (EPSG:4326). i.e; Inukjuak, 58.4550, -78.1037
• -o, --output: the path to the output folder, where a .csv file for each site will be created, containing the output values from the S3Snow processor. A list of the S3 scenes for which the algorithm failed is created in a separate file. See note below.
• -b, --bands: a list of band names for which the data extraction will occur. The bands can be regular bands, TiePointGrids, or Masks. The band names should be listed, separated by a space. For example to extract data from S3 OLCI first two radiance bands: Oa01_radiance Oa02_radiance.

The following optional inputs can be specified:

• -r, --res: specifies the reader to be used to open SLSTR images. By default the 500m resolution reader is specified, but the 1km reader can be set using this flag. The flag values can either be "500" or "1000". For specific applications only.
• -p, --platform specify the Sentinel-3 platform (i.e. Sentinel-3A, -3B, or both) to include data from. Options are 'A', 'B', or 'AB' (for both platforms).

Example run:

python s3_band_extract.py -i "/path/to/folder/containing/S3/folders"\
-c "/path/to/input/csvfile.csv" -o "/path/to/output/folder" -b Oa01_radiance quality_flags_bright SZA

Outputs: The output csv file contains:

• Year, Month, Day, Hour, Minute, Second of acquisition
• Day of year of acquisition
• Satellite platform ID (S3A = 0, S3B = 1)
• The values for all the bands specified as inputs

Run python list_sat_bands.py -h for help.

The script takes only two inputs:

• -i, --insat: the path to a single S3 OLCI or SLSTR scene, i.e. a .SEN3 folder containing NetCDF data files (.nc) and an XML file (.xml).
• -f, --file: path to the output text file to which the list of bands will be written.

Example run:

python list_sat_bands.py -i "/path/to/folder/containing/S3/scene"\
-f "/path/to/output/textfile.txt"

Outputs: The output text file contains:

• The OLCI/SLSTR band names
• The OLCI/SLSTR TiePointGrid names
• The OLCI/SLSTR mask names