Instructions for using these scripts

These instructions are designed for the Above Science Cloud (ASC) environment, but you will need to install the required packages and conda env beforhand, regardless!

Workflow for extracting stats for all lakes observed by UAVSAR:

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.)

Workflow for extracting stats for dozen or so visited lakes:

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.

Workflow for sensitivity figure:

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

Workflow for further analysis and plots:

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

Workflow for Polarimetric SAR decomposition using PolSAR Pro and Random Forests classification in Matlab

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

Workflow for Look-up Table (LUT)-based classification

• 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.