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daily_tseb_gee_et

A GEE TSEB Workflow for estimating Daily High Resolution (30m) fully Remote Sensing Evapotranspiration (ET)

https://github.com/ikramelhazdour/daily_tseb_gee_et

Science Score: 57.0%

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Keywords

evapotranspiration gee high high-resolution javascript python remote-sensing tseb
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A GEE TSEB Workflow for estimating Daily High Resolution (30m) fully Remote Sensing Evapotranspiration (ET)

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evapotranspiration gee high high-resolution javascript python remote-sensing tseb
Created over 1 year ago · Last pushed 9 months ago
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Readme License Citation

README.md

A GEE TSEB Workflow for Daily 30-m Evapotranspiration (ET)

Overview

These algorithms constitute the workflow of a Google Earth Engine (GEE) implementation of the Two Source Energy Balance (TSEB) Model. This implementation uses an Artificial Neural Network to retrieve Leaf Area Index (LAI), a simple regression relationship with NDVI to retrieve Canopy Height, and a gap-filling approach based on reference ET and Kc to produce daily Evapotranspiration.

Parts of the workflow

  • The first part of the workflow (TSEBWorkflowERA5_Forcing) involves preparing the model's inputs through a series of data retrieval and processing steps. This process generates inputs as GEE assets, which are subsequently transformed into outputs, also as GEE assets.
  • The second part of the workflow applies a gap-filling approach to the outputs (Instantaneous ET) using the "Gap-filling" algorithm, which can be run directly in the GEE coding environment. The result is a daily Evapotranspiration product at a 30-meter resolution.

## Requirements - The users are invited to download the geeet package: https://github.com/kaust-halo/geeet, and replace the TSEB module with the tseb.py module.

Authors (software) & Aknowledgement

  • Ikram EL HAZDOUR (PhD candidate at CESBIO and UCAM)
  • Michel LE PAGE (Researcher at CESBIO IRD)
  • Oliver LOPEZ (Researcher at KAUST)
  • The algorithms are made available for research purposes. Any other use, including commercial applications, requires permission from the author.
  • Please use the citation below when referencing any research that uses these algorithms.

Citation

The research paper related to this workflow is accessible at : https://doi.org/10.1016/j.envsoft.2025.106365

Owner

  • Login: ikramelhazdour
  • Kind: user

Citation (Citation.md) 

The full paper related to this workflow algorithm can be accessed using the following link: https://www.sciencedirect.com/science/article/pii/S1364815225000490?via%3Dihub
and DOI : https://doi.org/10.1016/j.envsoft.2025.106365

Full citation:

El Hazdour, I., Le Page, M., Hanich, L., Chakir, A., Lopez, O., & Jarlan, L. (2025). A GEE TSEB workflow for daily high-resolution fully remote sensing evapotranspiration: Validation over four crops in semi-arid conditions and comparison with the SSEBop experimental product. Environmental Modelling & Software, 187, 106365. https://doi.org/10.1016/j.envsoft.2025.106365

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