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Geographically aware estimates of remotely sensed water properties for Chesapeake Bay

RESOURCE

Publicly Accessible Repository
https://github.com/DOE-ICoM/geowq-chesapeake
https://doi.org/10.5281/zenodo.7332558

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Abstract

This software contains the code for a data-driven model to produce high-resolution (< 1 km2) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation Earth Systems Models. Our model fits daily MODIS Aqua reflectance data to surface observations (< 1 m depth) from 2000-2021 in Chesapeake Bay, USA.
Developers:
Release Date:
2022-11-22
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
GNU General Public License v3.0
Sponsoring Org.:
Code ID:
98283
Site Accession Number:
C23002
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/DOE-ICoM/geowq-chesapeake
https://doi.org/10.5281/zenodo.7332558

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LANL Badge

Citation Formats

Stachelek, Jemma, and Schwenk, Jonathan. Geographically aware estimates of remotely sensed water properties for Chesapeake Bay. Computer Software. https://github.com/DOE-ICoM/geowq-chesapeake. USDOE Office of Science (SC), Engineering & Technology. 22 Nov. 2022. Web. doi:10.5281/zenodo.7332558.
Stachelek, Jemma, & Schwenk, Jonathan. (2022, November 22). Geographically aware estimates of remotely sensed water properties for Chesapeake Bay. [Computer software]. https://github.com/DOE-ICoM/geowq-chesapeake. https://doi.org/10.5281/zenodo.7332558.
Stachelek, Jemma, and Schwenk, Jonathan. "Geographically aware estimates of remotely sensed water properties for Chesapeake Bay." Computer software. November 22, 2022. https://github.com/DOE-ICoM/geowq-chesapeake. https://doi.org/10.5281/zenodo.7332558.
@misc{ doecode_98283,
title = {Geographically aware estimates of remotely sensed water properties for Chesapeake Bay},
author = {Stachelek, Jemma and Schwenk, Jonathan},
abstractNote = {This software contains the code for a data-driven model to produce high-resolution (< 1 km2) estimates of temperature, salinity, and turbidity over decadal time scales as required by next-generation Earth Systems Models. Our model fits daily MODIS Aqua reflectance data to surface observations (< 1 m depth) from 2000-2021 in Chesapeake Bay, USA.},
doi = {10.5281/zenodo.7332558},
url = {https://doi.org/10.5281/zenodo.7332558},
howpublished = {[Computer Software] \url{https://doi.org/10.5281/zenodo.7332558}},
year = {2022},
month = {nov}
}