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openEO Hackathon - Solution Python client / GEE back-end

Task 1

  • Prerequisites: Python 3.5, pip
  • Clone/Download: openeo-python-client
  • Hint: Use anaconda for python versioning
  • Recommendation: Use Linux based operating system.

Installing and loading the required packages, in your console:

# inside the project directory
pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install --user -e .

Connecting to the openEO Earth Engine back-end. You may need to change the credentials:

import openeo
from openeo.auth.auth_bearer import BearerAuth
endpoint = "http://giv-project8.uni-muenster.de"
username = "groupX"
password = "test123"
session = openeo.session(username, endpoint=endpoint)
# The GEE back end uses a Bearer Token for authentication, therefore it has to be imported
session.auth(username, password, BearerAuth)

Requesting the capabilities that are provided by the back-end:

session.list_capabilities()

Task 2

Requesting the processes offered by the back-end:

session.get_all_processes()

Requesting the arguments required for the ndvi process:

session.get_process('NDVI')

Requesting the products offered by the back-end:

session.list_collections()

Requesting information about the Sentinel-2 dataset, including the temporal and spatial extent:

collection = session.get_collection("COPERNICUS/S2")
collection["extent"]
collection["time"]

Task 3

First of all, requesting the supported file formats to see whether PNG or GeoTiff (GTiff) is supported and color stretching is required for PNG files or not:

session.get_outputformats()

Only PNG files are supported and therefore we need to apply color stretching.

For the construction of the process graph we need the following steps:

  1. Construct the process graph builder
  2. Specify the required dataset (COPERNICUS/S2)
  3. Filtering by date range
  4. Filtering by bounding box
  5. Calculating the NDVI on the red band B4 and the near-infrared band B8
  6. Computing a maximum time composite
  7. Strecthing the colors
  8. Executing the process graph on the back-end, requesting a PNG file with the name task_3_out.png

We define commonly used parameters:

product = "COPERNICUS/S2"
bbox = {
    "left": 16.138916, 
    "top": 48.320647, 
    "right": 16.524124, 
    "bottom": 48.138600, 
    "srs": "EPSG:4326"
}
time = {
    "start": "2018-01-01",
    "end": "2018-01-31"
}

We are building the process graph as follows:

s2a_prd_msil1c = session.image(product)
timeseries = s2a_prd_msil1c.bbox_filter(left=bbox["left"], right=bbox["right"], top=bbox["top"], bottom=bbox["bottom"], srs=bbox["srs"])
timeseries = timeseries.date_range_filter(time["start"], time["end"])
timeseries = timeseries.ndvi("B4", "B8")
timeseries = timeseries.max_time()
timeseries = timeseries.stretch_colors(-1, 1)

# Send Job to back end.
job = timeseries.send_job(out_format="png")
# download result from back end.
job.download("task_3_out.png")

Task 4

Checking if the process zonal_statistics is provided by the back-end:

session.get_process('zonal_statistics')

If you haven't done so yet, download the GeoJSON file containing the polygon into the working directory of the Python client.

Uploading the downloaded file to the back-end:

session.user_upload_file("polygon.json")

Construct and execute the process graph. Downloads the file in JSON format with the file name task_4.json:

s2a_prd_msil1c = session.image(product)
timeseries = s2a_prd_msil1c.date_range_filter(time["start"], time["end"])
timeseries = timeseries.bbox_filter(left=bbox["left"], right=bbox["right"], top=bbox["top"], bottom=bbox["bottom"], srs=bbox["srs"])
timeseries = timeseries.band_filter("B8")
timeseries = timeseries.zonal_statistics(regions="polygon.json", func="mean")

job = timeseries.send_job(out_format="json")
job.download("task_4.json")

Task 5

Task 5 is currently not implemented in the openEO Earth Engine back-end.