A Python implementation of Two Source Energy Balance models for estimating evapotranspiration using remote sensing data.
pyTSEB is a Python library that implements Two Source Energy Balance (TSEB) models for estimating evapotranspiration using remote sensing data. It calculates sensible and latent heat fluxes by separating soil and vegetation contributions, enabling accurate water and energy exchange modeling in various landscapes. The tool addresses the need for open-source, customizable solutions in hydrological and agricultural research.
Researchers, hydrologists, and environmental scientists working on evapotranspiration modeling, remote sensing applications, and soil-plant-atmosphere interaction studies. It is also suitable for agricultural professionals and educators in geospatial and environmental sciences.
pyTSEB offers a flexible, modular implementation of established TSEB models with support for multiple input types and extensive customization. Its open-source nature and collaborative development approach ensure continuous improvement and adaptability to diverse environmental conditions, making it a reliable alternative to proprietary or less transparent tools.
A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data
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Implements Priestley-Taylor TSEB-PT, Dual Time Difference (DTD), and TSEB-2T, offering flexibility for different research scenarios as outlined in the README's synopsis and features.
Supports both tabulated time series and satellite/airborne imagery through high-level scripts and configuration files, enabling diverse data integration for evapotranspiration estimation.
Provides core modules for net radiation, resistances, and Monin-Obukhov similarity, allowing researchers to customize and extend the model beyond standard implementations.
Includes Jupyter notebooks with graphical interfaces for configuration and running, making initial setup and experimentation more accessible, as described in the Code Example section.
The README's warning section highlights common misconfigurations like incorrect fractional cover or green fraction, which can lead to erroneous results and require expert knowledge to avoid.
Requires external libraries like GDAL and pyPro4Sail, which can be challenging to install and maintain, especially in environments without strong geospatial support.
Explicitly admits that model performance may be unsatisfactory in some landscapes, relying on user feedback for improvements, indicating potential reliability issues without tuning.