c11/ee-atmcorr-timeseries

Atmospherically Corrected Time Series using Google Earth Engine

Purpose

Easily create time series of Landsat and Sentinel 2 data for anywhere on Earth.

• atmospherically corrected
• cloud-masked
• save to excel
• pretty plots

Installation

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 

Usage

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

Setup-time VS Run-time

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.