Easily create time series of Landsat and Sentinel 2 data for anywhere on Earth.
- atmospherically corrected
- cloud-masked
- save to excel
- pretty plots
Install Anaconda.
If necessary, create a python3 environment
conda create --name py3 python=3
and activate it
source activate py3
on windows the above command is just
activate py3
then install the Earth Engine API
pip install google-api-python-client
pip install earthengine-api
If first time, authenticate the Earth Engine API.
earthengine authenticate
grab source code
git clone https://github.com/samsammurphy/ee-atmcorr-timeseries
run in Jupyter Notebook:
cd ee-atmcorr-timeseries/jupyter_notebooks
jupyter-notebook ee-atmcorr-timeseries.ipynb
This code is optimized to run atmospheric correction of large image collections. It trades setup-time (i.e. ~30 mins) for run time (i.e. ~ 1 minute). Setup is only performed once and is fully automated. This solves the problem of running radiative transfer code for each image which would take ~2 secs/scene, 500 scenes would therefore take over 16 mins (everytime).
It does this using the 6S emulator which is based on n-dimensional interpolated lookup tables (iLUTs). These iLUTs are automatically downloaded and constructed locally.