Mapping vegetation traits in Google Earth Engine using Gaussian process models and the Sentinel-2 top-of-atmosphere product.
Author: José Estévez
Code: Matías Salinero-Delgado
This is a guideline of running a GPR model for mapping vegetation variables on Google Earth Engine (GEE), as proposed in Estévez et al., 2022.
- Please, click the link below to get access to the GEE repo (a GEE account is required):
https://code.earthengine.google.com/?accept_repo=users/jose_estevez/S2TOA_GPR_MapVegetation - After accept to add the code in your GEE account, you should click the refresh button.
- Then you should be able to see the code in the Reader section under the Scripts tab.
Please cite the code as: Estévez, J., Salinero-Delgado, M., Berger, K., Pipia, L., Rivera-Caicedo, J.P., Wocher, M., Reyes-Muñoz, P., Tagliabue, G., Boschetti, M., Verrelst, J., 2022. Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data. Remote Sens. Environ. 273, 112958. https://doi.org/10.1016/J.RSE.2022.112958.
Please email me at jose.a.estevez@uv.es for any further information.
The GEE repository includes the codes for some mapping demos:
1. Mapping at local scale
(Run the script 'localScaleGPR': In line 15 you can change to the variable you want to map)
2. Mapping at national scale
(Run the script 'nationalScaleGPR': In line 3 you can change to the country you want to map)
3. Mapping uncertainties