# Are Google Earth Engine analyses reproducible?

Detailed setup files and runnable python scripts for reproducing this blog post are at: https://github.com/jsta/earthengine

More and more research papers are making use of Google Earth Engine (EE) to do geocomputation with gridded data and satellite (remote sensing) output. Are these analyses reproducible? Will they be reproducible in 2-3 years? In the following blog post, I explore these questions and conclude that:

• If a paper uses EE to simply pull/crop/extract data the answer is likely yes.

• If a paper uses any EE specific computation functions then the analyses have a hard dependency on the EE remote servers. Should they go away, the paper will no longer be reproducible.

## Dependencies

Before we begin, let’s set up our python environment and initialize EE where the python dependencies are:

• python=3.7.3
• earthengine-api
• pydrive
• numpy
• scikit-image
\$ earthengine authenticate


## Walkthrough

First, in our python session, we must manually initialize EE and setup pydrive:

ee.Initialize()
gauth = GoogleAuth()
gauth.LocalWebserverAuth()
drive = GoogleDrive(gauth)


Then, we need to identify an EE image product. We will focus on the Landsat 8 panchromatic band product and select a tile overlapping Gull Lake Michigan (path: 21, row: 31, date: 2019-01-26). You can find the path and row numbers for an arbitrary region of interest using this tool. The scenario here is that we will attempt to extract the coastline of Gull Lake using an EE workflow. Note that we save the raw Landsat scene to Google Drive clipped to a bounding box around the Gull Lake watershed using the region argument of batch.Export.image.toDrive.

image = ee.Image('LANDSAT/LC08/C01/T1/LC08_021031_20190126').select('B8')
raw_export = batch.Export.image.toDrive(image, description = 'raw_export',
scale = 30,
region=([-85.4664, 42.36926],
[-85.37169, 42.36926],
[-85.37169, 42.46446],
[-85.4664, 42.46446],
[-85.4664, 42.36926]))
batch.Task.start(raw_export)


Next, we can pull the results from Drive to our local machine using some pydrive commands:

file_list = drive.ListFile({'q': "'root' in parents and trashed=false"}).GetList()
file_position = np.min(np.where(
[file1['title'] == "raw_export.tif" for file1 in file_list]))
file_id = file_list[file_position]['id']
file1 = drive.CreateFile({'id':file_id})
file1.GetContentFile('raw_export.tif')


Now, let’s find the edges of our scene using a non-reproducible EE workflow where ee.Algorithms.CannyEdgeDetector does the heavy lifting:

canny = ee.Algorithms.CannyEdgeDetector(image, 10, 1)
canny_export = batch.Export.image.toDrive(canny, description = 'canny',
scale = 30,
region=([-85.4664, 42.36926],
[-85.37169, 42.36926],
[-85.37169, 42.46446],
[-85.4664, 42.46446],
[-85.4664, 42.36926]))
batch.Task.start(canny_export)

# pull file from GDrive
file_list = drive.ListFile({'q': "'root' in parents and trashed=false"}).GetList()
file_position = np.min(np.where(
[file1['title'] == "canny.tif" for file1 in file_list]))
file_id = file_list[file_position]['id']
file1 = drive.CreateFile({'id':file_id})
file1.GetContentFile('canny.tif')


Finally, let’s find the edges of our scene using a reproducible scikit-image workflow:

raw_export = skimage.io.imread('raw_export.tif')
edge_sobel = skimage.filters.sobel(raw_export)


Here is a comparison of the raw data with the results of each approach:

## Recommendation

Use EE as a remote data source not as a computation platform. Future reproducibility of EE geocomputation depends on the existence of remote EE servers.

## Notes about EE

• It’s obnoxious that there seems to be no way to read the argument descriptions of EE functions without going to the online code editor. These docs don’t have embedded examples. You need to browse a separate webpage.

• I was able to reproduce EE data fetching code from 2016 that uses legacy formatting of Landsat file names.

• gdal has tools for finding and downloading from EE!

## Further reading

https://geohackweek.github.io/raster/04-workingwithrasters/

https://gis.stackexchange.com/a/297042/32531

https://geoscripting-wur.github.io/Earth_Engine/