1. Run classification in Matlab and analysis/reclass_rasters.py
2. analysis/upscaleFromLandcover.ipynb
3. analysis/Raster2PercentageLakePoly.py (Polygonizes landcover map to lake polygons, with attributes for EM%, whether or not it is a border lake, and whether it was observed by AirSWOT CIR camera.)

1. Run classification in Matlab and analysis/reclass_rasters.py
2. analysis/Raster2PercentagePoly.py (Convert a land cover raster to a shapefile, with attributes for fractional class coverage)
3. analysis/calcVegAreaFromPts.py (An automatic way of calculating littoral fraction from a shapefile of water bodies and a list of points)
4. analysis/Average_em.ipynb (Averages em_fraction for each site along all available dates)

Note: If running over maximum ROI, use the ROI file: 'ROI-analysis_albers.shp'. If running me under ROI common to all acquisition dates, use ROI file: 'ROI-analysis_albers-sub.shp'. When toggling between these two ROIs, be sure to to update vars 'file_basenames' and 3 others with tag #sub-roi.

Be sure to update file paths in python_env.py and filepath vars in Raster2PercentageLakePoly.py before running

1. lake-histograms-multi-temporal.ipynb
2. lake-bar-chart-time-series.ipynb
3. lake-histogram-by-area-etc.ipynb

1. Run polsar pro workflow in parallel using the following command:

bash polsar_pro/parallel_cat_run.sh polsar_pro/psp_workflow_function.sh [path/to/textfile/with/input/IDs] [number of cores]

Number of cores: 2 recommended to conserve memory on ASC, since each function call will need ~40 GB of mem max dand cause un-noticed errors if memory limit is exceeded If using slurm to run a batch job, substitute slurm for bash in the above command.

2. In matlab: trainingImageImport.m

3. If reclassifying raster, use python: analysis/reclass_rasters.py

• This method was used for the Daring Lake scenes (CSD) only.

1. Activate conda environment. On ASC, the command would be:

conda activate base

2. Move original GRD files to default_grd folder, using the script polsar_pro/psp_mv_hgt_slope_inc_function.sh.
3. Run python script radiocal_example_script_ek.py located in an additional forked repo.
4. Continue with step 1. of regular workflow, above.