GEE_watermasks

GEE_watermasks is a software package to download Google Earth Engine data and generate client-side binary masks of river location.

Dependencies

• Python version=3.10
• GDAL (can be installed via conda)
• shapely (tested on 1.7.1 )
• earthengine-api
• geemap
• numpy
• rasterio
• pyproj
• fiona
• scikit-image
• geopandas

Dependencies other than GDAL can be installed using the Python Package Installer (pip):

pip install -r requirements.txt 

Running Puller

The puller is run from shell and python scripts found in the /scripts directory. These scripts include the parameters used to determine what you pull and how you want to generate the masks

Parameters include:

• river - name of the study area/river (also used as name for file storage and file names)
• poly - path to the .gpkg file used to determine the region of interest. Examples of these can be found in the /examples directory
• mask_method - method to generate water masks (options include Jones, ESA
• dataset= - if Jones or Zou are selected, this dtermines if you pull Landsat or Sentinel-2 data
• network_method - determines how the water mask is reduced to a river mask (options are grwl, merit, largest, or all). The merit option requires an additional local file
• network_path - (optional) path to the merit path used for hydroshed river network reductions (good for small rivers)
• images - true/false flag to tell program to pull images
• masks - true/false flag to tell program to generate masks
• water_level - (only needed if Jones is the mask method) tells which water level the program will use as the threshold
• start - Month-day to use as the beginning period for compositing
• end - Month-day to use as the ending period for compositing
• start_year - First year to run
• end_year - Last year to run (program will run all years in between start and end)
• out - root to export all images and masks to

Example: river='Indus'
poly="/Shapes/$river.gpkg"
mask_method="Jones"
dataset='landsat'
network_method="grwl"
images="true"
masks="true"
water_level="2"
start="01-01"
end="12-31"
start_year="2021"
end_year="2021"
out="Figures/Indus"