Skip to main content

Surface Temperature Initiated Closure (STIC) Evapotranspiration Model Python Implementation

Project description

STIC-JPL

Surface Temperature Initiated Closure (STIC) Evapotranspiration Model Python Implementation

CI

This repository contains the python implementation for the Surface Temperature Initiated Closure (STIC) evapotranspiration model, used by the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Surface Biology and Geology (SBG) missions.

This software package is a Python implementation of the Surface Temperature Initiated Closure (STIC) version 1.3 model designed to implement LST to solve the aerodynamic temperature, which is critical for ET modeling. The original STIC model produced by Kaniska Mallick (Mallick et al. 2015; 2018; 2022) was re-implemented from MATLAB code to Python by Gregory Halverson and Madeleine Pascolini-Campbell. The software was developed under a research grant by the NASA Research Opportunities in Space and Earth Sciences (ROSES) program. It is intended for use by the Hyperspectral Thermal Emission Spectrometer (HyTES), MODIS/ASTER (MASTER) Airborne Simulator, Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission and as a precursor for the Surface Biology and Geology (SBG) mission.

The software was developed as part of a research grant by the NASA Research Opportunities in Space and Earth Sciences (ROSES) program. It was designed for use by the Hyperspectral Thermal Emission Spectrometer (HyTES), MODIS/ASTER (MASTER) Airborne Simulator, Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission as a precursor for the Surface Biology and Geology (SBG) mission. It may also be useful for general remote sensing and GIS projects in Python. This package can be utilized for remote sensing research in Jupyter notebooks and deployed for operations in data processing pipelines.

The software is being released according to the SPD-41 open-science requirements of NASA-funded ROSES projects.

Gregory H. Halverson (they/them)
gregory.h.halverson@jpl.nasa.gov
Lead developer
NASA Jet Propulsion Laboratory 329G

Kaniska Mallick (he/him)
kaniska.mallick@list.lu
Algorithm inventor
Luxembourg Institute of Science and Technology

Tian Hu (he/him)
tian.hu@list.lu
Algorithm developer
Luxembourg Institute of Science and Technology

Madeleine Pascolini-Campbell (she/her)
madeleine.a.pascolini-campbell@jpl.nasa.gov
Algorithm developer
NASA Jet Propulsion Laboratory 329F

Claire Villanueva-Weeks (she/her)
claire.s.villanueva-weeks@jpl.nasa.gov
Code maintenance
NASA Jet Propulsion Laboratory 329G

Installation

Use the pip package manager to install the STIC-JPL PyPi package with dashes in the name.

pip install STIC-JPL

Usage

Import the STIC_JPL function from the STIC_JPL package with underscores in the name.

from STIC_JPL import STIC_JPL

See the ECOSTRESS example notebook for usage.

See the STIC sensitivity notebook for sensitivity analysis.

References

Mallick, K., Boegh, E., Trebs, I., Alfieri, J. G., Kustas, W. P., Prueger, J. H., ... & Jarvis, A. J. (2015). Reintroducing radiometric surface temperature into the P enman‐M onteith formulation. Water Resources Research, 51(8), 6214-6243. https://doi.org/10.1002/2014WR016106

Mallick, K., Toivonen, E., Trebs, I., Boegh, E., Cleverly, J., Eamus, D., ... & Garcia, M. (2018). Bridging Thermal Infrared Sensing and Physically‐Based Evapotranspiration Modeling: From Theoretical Implementation to Validation Across an Aridity Gradient in Australian Ecosystems. Water Resources Research, 54(5), 3409-3435. https://doi.org/10.1029/2017WR021357

Mallick, K., Baldocchi, D., Jarvis, A., Hu, T., Trebs, I., Sulis, M., ... & Kustas, W. P. (2022). Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal‐Based Evaporation Modeling. Geophysical Research Letters, 49(15), e2021GL097568. https://doi.org/10.1029/2021GL097568

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

stic_jpl-1.6.0.tar.gz (964.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

stic_jpl-1.6.0-py3-none-any.whl (973.6 kB view details)

Uploaded Python 3

File details

Details for the file stic_jpl-1.6.0.tar.gz.

File metadata

  • Download URL: stic_jpl-1.6.0.tar.gz
  • Upload date:
  • Size: 964.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for stic_jpl-1.6.0.tar.gz
Algorithm Hash digest
SHA256 c5cd9aea4b0349382193da6f5f6535df09088e6544d40fbe9e0a8d7ccd6c4c5f
MD5 7eaf92c21f0479a8103b311a7ff53b1a
BLAKE2b-256 34d7af654e26f507c1818298cd70f266027294ca6c83cc3667e69f2dffe9974a

See more details on using hashes here.

File details

Details for the file stic_jpl-1.6.0-py3-none-any.whl.

File metadata

  • Download URL: stic_jpl-1.6.0-py3-none-any.whl
  • Upload date:
  • Size: 973.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.25

File hashes

Hashes for stic_jpl-1.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 95c81d554b44ab0b1edec802fe5549938b3a2ef9778cb6742d84336c9269c2c1
MD5 96f24d98a1f9d7f0308a99c25d16c2ea
BLAKE2b-256 a49349a8737a0c616af52932254981aba251bc840c7f619887334e6573320a1d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page