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.

• The first part of the workflow (TSEB_Workflow_ERA5_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.
• 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.

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