This repository contains the source codes for Sentinel 2 and Sentinel 1 data processing on the Google Earth Engine platform using Colab Jupyter notebooks. This material is part of the ESA 2023 summer school.
This repository contains four Jupyter Colab notebooks comprising the following exercises:
- S2 based LAI retrieval using parametric indices in Google Earth Engine
- ARTMO based LAI retrieval modelling using Gaussian process regression in Google Earth Engine
- Unsupervised land cover classification based on S1 time series in GEE
- Fusing radar and optical time series in Google Earth Engine
Gabriel Caballero
Jochem Verrelst
These Colab Jupyter notebooks are open access, and the source codes are available for users to modify and apply them all over the world. Please be sure to cite our articles as a form of compensation.
- [1] Caballero, G.; Pezzola, A.; Winschel, C.; Sanchez Angonova, P.; Casella, A.; Orden, L.; Salinero-Delgado, M.; Reyes-Muñoz, P.; Berger, K.; Delegido, J.; et al. Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes. Remote Sens. 2023, 15, 1822. https://doi.org/10.3390/rs15071822
- [2] Caballero, G.; Pezzola, A.; Winschel, C.; Casella, A.; Sanchez Angonova, P.; Orden, L.; Berger, K.; Verrelst, J.; Delegido, J. Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles. Remote Sens. 2022, 14, 5867. https://doi.org/10.3390/rs14225867
- [3] Caballero G, Pezzola A, Winschel C, Casella A, Sanchez Angonova P, Rivera-Caicedo JP, Berger K, Verrelst J, Delegido J. Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery. Remote Sensing. 2022; 14(18):4531. https://doi.org/10.3390/rs14184531