Hub
Pricing About
WorkflowWorkflow

02 Apply GEE Reducer with GeoPolygon

Spatial Data LabHarvard UniversityGoogle Earth Engine
Center for Geographic Analysis at Harvard University profile image
Draft Latest edits on 
Aug 12, 2024 6:34 PM
Drag & drop
Like
Download workflow
Workflow preview
This KNIME workflow demonstrates the process of reading and visualizing images from Google Earth Engine (GEE). The workflow includes steps for authenticating with GEE, reading image collections, selecting specific bands, and visualizing the images on an interactive map.

Step 1: GEE Authenticator
This node handles authentication with Google Earth Engine using a service account. Ensure to execute this authenticator first.

Requirements:
Install geemap and ee using pip install geemap ee in your Python Enviroment.
Provide the path to the local JSON key file and the service account email for Google Cloud Account.

Inputs:
Local JSON: G:/xxx.json
Service Account: xxx@xxx.iam.gserviceaccount.com

Step 2:Visualize Elevation in "USGS/SRTMGL1_003"

GEE Image Reader: Reads the elevation image from the GEE dataset "USGS/SRTMGL1_003".
GEE Image Visualization: Visualizes the elevation image on an interactive map.

Step 3:Visualize "maxFRP" in "MODIS/061/MYD14A1"

GEE Collection Image Reader: Reads the image collection from the GEE dataset "MODIS/061/MYD14A1".
GEE Image Band Selector: Selects the "maxFRP" band from the image collection.
GEE Image Visualization: Visualizes the "maxFRP" band on an interactive map.

Instructions:
Check the available bands and their IDs in the interactive view.
Input "maxFRP" in the Band Selector node to visualize this specific band.

Created By:
Lingbo Liu
lingboliu@fas.harvard.edu
Spatial Data Lab project
Center for Geographic Analysis
Harvard University
Loading deploymentsLoading ad hoc jobs

Used extensions & nodes

Created with KNIME Analytics Platform version 5.3.0
  • Go to item
    KNIME Base nodesTrusted extension

    KNIME AG, Zurich, Switzerland

    Versions 5.2.2, 5.3.0

    knime
  • Go to item
    KNIME Python IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.3.0

    knime
  • Go to item
    KNIME Quick FormsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.3.0

    knime
  • Go to item
    KNIME ViewsTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 5.3.0

    knime

Legal

By using or downloading the workflow, you agree to our terms and conditions.

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • Courses + Certification
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more about KNIME Business Hub
© 2025 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Data Processing Agreement
  • Credits