Skip to main content

JPL implementation of the MOD16 evapotranspiration algorithm for high resolution instantaneous remote sensing imagery

Project description

MODIS Global Evapotranspiration Project (MOD16)

The MODIS Global Evapotranspiration Project (MOD16) is a software package developed by a team of researchers from NASA Jet Propulsion Laboratory and the University of Montana. This software is a Python implementation of the MOD16 evapotranspiration algorithm, designed to process high-resolution instantaneous remote sensing imagery.

Unique features of the software include the ability to process remote sensing data with the MOD16 model and partition latent heat flux into canopy transpiration, interception, and soil evaporation. The MOD16 algorithm has been re-implemented to run on instantaneous high spatial resolution remote sensing imagery instead of 8-day MODIS imagery, making the model more accessible for remote sensing researchers.

The software is written entirely in Python and is intended to be distributed using the pip package manager.

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 Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission as a precursor for the Surface Biology and Geology (SBG) mission. However, 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
NASA Jet Propulsion Laboratory 329G

Kanishka Mallick (he/him)
kaniska.mallick@gmail.com
Luxembourg Institute of Science and Technology

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

Installation

pip install MOD16-JPL

Usage

import MOD16_JPL

References

  • Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781-1800. doi:10.1016/j.rse.2011.02.019
  • Mu, Q., Heinsch, F. A., Zhao, M., & Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4), 519-536. doi:10.1016/j.rse.2007.04.015

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

mod16_jpl-1.0.0.tar.gz (24.2 kB view details)

Uploaded Source

Built Distribution

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

mod16_jpl-1.0.0-py3-none-any.whl (34.8 kB view details)

Uploaded Python 3

File details

Details for the file mod16_jpl-1.0.0.tar.gz.

File metadata

  • Download URL: mod16_jpl-1.0.0.tar.gz
  • Upload date:
  • Size: 24.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.16

File hashes

Hashes for mod16_jpl-1.0.0.tar.gz
Algorithm Hash digest
SHA256 3c2088661809cd95dce4f454f9c0995680fe1789f2b88e969a7c56e6f927da66
MD5 a014ad2e0ace511d47ffd68a4df9250b
BLAKE2b-256 6008c262bf9a417b1c719276e4e5e6ddb26d02ef7d43a7ca48bfb3e205361aec

See more details on using hashes here.

File details

Details for the file mod16_jpl-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: mod16_jpl-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 34.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.10.16

File hashes

Hashes for mod16_jpl-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 4cb61ecaeaa4a72bccb59ac37ba690347233df199dc4c6fda527d9718f6b8883
MD5 610579005b23587acca1dd6e1db040f8
BLAKE2b-256 43bd7a67850961400bb1dedac129c5b3cc5eae5e15802a4a03c207d94801a6d3

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