This repository contains four Jupyter Notebooks showing interactive examples of Earth Observation tasks using openEO Platform.
The easiest way to run the notebooks is by clicking the Binder button at the top. This will create an independent Python environment with all the required libraries, where you will be able to experiment the notebooks.
Alternatively, you can run them locally (please note: the Anaconda Python enviornment has been tested on Linux Ubuntu 18.04, on Windows please use in step 3 the runtime optimized trimmed version environment_windows.yml):
- Install Anaconda to manage virtual environments. You can follow the instructions here
- Clone the repository and get into the repo folder:
git clone https://github.com/openEOPlatform/SRR1_notebooks.git
cd SRR1_notebooks
- Create a new conda environment with the following command:
conda env create -f environment.yml
conda env create -f environment_windows.yml (use this line on Windows)
- Once the process is complete, you can activate the environment:
conda activate openeo_platform
- Now you can start the Jupyter Notebook Server and use the notebooks, just typing:
jupyter notebook
- This should open up a new window in your default web browser, where you can select the notebook you prefer.
This user story covers the access of pre-computed ARD Sentinel 1 and 2 data over a large area. Ten MGRS tiles are accessed. It is shown that the grids align which makes analysis and further processing more convenient and precise since resampling steps are not required anymore. The CARD4L compliant metadata is presented.
This user story shows how to retrieve time series trajectories from pre-computed and on-demand ARD data. The focus lies on facilitating the access to time series data through interactive selection of pixels or small polygons in the client and receiving time series directly. This allows for quick access and comparison of time series of spectral bands or indices such as NDVI for optical data or sigma to gamma ratio for SAR backscatter.
This user story demonstrates the capabilities of the ARD backscatter generation for Sentinel-1 data. CARD4L compliant data will be generated using the process ard_normalized_radar_backscatter including according metadata. The custom parametrization will be shown using the process sar_backscatter.
This user story demonstrates the capabilities of the ARD surface reflectance generation for Sentinel-2 data. CARD4L compliant data will be generated using the process ard_surface_reflectance including according metadata. The possibilities of custom parametrizations will be demonstrated as well as the differences between different atmospheric correction methods (e.g., FORCE, iCor).