Google Earth Engine Python API Examples

A collection of Jupyter Notebooks for Google Earth Engine Python API.

Note: More tutorials for Google Earth Engine Python API coming in 2019.

Jupyter Notebook Tutorials for Google Earth Engine

001 Landcover Classfication for Landsat 8 TOA imagery

Classification Example for Landsat 8 including several vegetation indices and object feature extraction. Eventually you can't access the training data. In case you are interested in the training data, feel free to contact me.

002 Tasseled Cap Transformation for Landsat 8 TOA imagery

Tasseled Cap Transformation for Landsat 8 TOA imagery based on the scientfic work "Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance" by M.Baigab, L.Zhang, T.Shuai & Q.Tong (2014).

003 Display Proba-V NDVI Imagery

Display Proba-V NDVI (Normalized Difference Vegetation Index) Imagery.

004 Display Proba-V Time-Series

Display Proba-V NDVI Time-Series using Pandas and Matplotlib.

005 Basic Proba-V Time-Series Analysis

Basic Proba-V NDVI Time-Series Analysis, including auto correlation, fast fourier transformation and outlier detection.

006 Basic Proba-V Time-Series Prediction

Basic Proba-V NDVI Time-Series Prediction, using Fourier extrapolation and ARIMA model.

007 Linear Regression

Linear regression on Proba-V, Landsat and Climate Hazards Group InfraRed Precipitation (CHRIPS) data. This tutorial demonstrates the comparison of one of the most common supervised machine learning methods, the linear regression. We are going to compare scikit-learn and Statsmodels. For more information about types of Machine Learning, check this link.

008 Proba-V Time-Series Forecast

Multiple step Time-Series Forecast on Proba-V NDVI data using Facebook Prophet. Landsat and Climate Hazards Group InfraRed Precipitation (CHRIPS) data were used as additional regressors.