timesat 4.4.1

Python bindings to the TIMESAT Fortran core for analyzing satellite time-series data

Timesat

License Notice

TIMESAT is proprietary software. It is freely available for non-commercial scientific research, academic teaching, and personal use only.

Commercial use requires a separate written agreement with the authors.

See the LICENSE and NOTICE files for full terms.

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Timesat provides Python bindings for the TIMESAT algorithms — a suite of routines for analyzing time-series of satellite remote sensing data.
This package wraps the original Fortran-based TIMESAT core into a modern Python interface for convenient use in data analysis and research workflows.

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Features

• Native Python bindings for the TIMESAT Fortran core
• Cross-platform precompiled binaries (macOS Intel & ARM, Linux, Windows)
• Supports Python 3.10–3.12
• Compatible with NumPy ≥ 2.0
• Provides high performance through the compiled Fortran backend
• Simple API for fitting and extracting vegetation metrics from time-series data

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Installation

You can install the latest release directly from PyPI:

pip install timesat

Note

TIMESAT is proprietary software licensed for non-commercial research, academic teaching, and personal use only. Commercial use requires a separate written agreement.

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Version log

4.4.1 – Configurable preprocessing range policy

Add configurable lower/upper range handling for time-series preprocessing before smoothing and phenology extraction.

New class-specific parameters:

p_lowrangemode p_highrangemode p_rangedownweight

Default vegetation strategy:

lowrangemode = 1 # clip low values to lower bound and keep weight highrangemode = 0 # invalidate high values by setting weight to zero rangedownweight = 0.5

4.3.3 – Delete all integral scaling

4.3.2 – Debug seasonfit & integral scaling

integral = integral * 1000

4.3.1 – Update output date format

YYDOY -> YYYYDOY

4.2.1 – License Change

Starting from version 4.2.1, TIMESAT is licensed under the TIMESAT Research License.

This software is now licensed for non-commercial scientific research, academic teaching, and personal use only.

Commercial use requires a separate written agreement with the authors.

Versions prior to v4.2.1 remain available under their original license.

4.1.12 – Debugged Windows parallel processing

4.1.11 – Added parallel processing

4.1.10 – Improved NoData Handling

Pixels whose land-cover class is not included in the SETTINGS table now receive a proper NoData value instead of zero.

4.1.9 – Performance release

• Build system updated to compile the Fortran core with high-optimization for improved runtime performance.
• Minor internal clean-ups to keep behaviour consistent across platforms.
• Note: Due to more aggressive optimization, very small floating-point differences (round-off level) may occur compared to earlier versions.

4.1.8 – Bugfixes and QA improvements

• Fixed: Issue related to handling of negative slopes in the time-series processing.
• Added: Switch for VPP (vegetation peak/phenology) calculation to give users more control over how metrics are derived.
• Added: yfitqa output for basic quality assessment of the fitted time-series.

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License

SPDX-License-Identifier: LicenseRef-Proprietary-TIMESAT-Research-Only

TIMESAT is proprietary software licensed under the TIMESAT Research License.

It is freely available for non-commercial scientific research, academic teaching, and personal use.

Commercial use is not permitted under this license and requires a separate written agreement with the authors.

See the LICENSE file for the full license text.

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Citation

If you use TIMESAT in your research, please cite the corresponding release on Zenodo:

Cai, Z., Eklundh, L., & Jönsson, P. (2025). TIMESAT4: is a software package for analysing time-series of satellite sensor data (Version 4.1.x) [Computer software]. Zenodo.
https://doi.org/10.5281/zenodo.17369757

If you use the underlying TIMESAT algorithms, please also cite the relevant TIMESAT publications listed in the official repository.

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Contact and Licensing Inquiries

For licensing questions, including commercial use, please contact:

Dr. Zhanzhang Cai Department of Physical Geography and Ecosystem Science Lund University, Sweden Email: [e-mail]

https://www.nateko.lu.se

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Acknowledgments

• TIMESAT — Original analysis framework for satellite time-series data.
• This project acknowledges the Swedish National Space Agency (SNSA), the European Environment Agency (EEA), and the European Space Agency (ESA) for their support and for providing access to satellite data and related resources that made this software possible.

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