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| Type: | Resource | |
| Storage: | The size of this resource is 2.7 GB | |
| Created: | Mar 19, 2025 at 2:17 p.m. (UTC) | |
| Last updated: | May 30, 2025 at 6:53 p.m. (UTC) | |
| Published date: | May 30, 2025 at 6:53 p.m. (UTC) | |
| DOI: | 10.4211/hs.0ef3eda3534f44a6bbd65786d57222ea | |
| Citation: | See how to cite this resource | |
| Content types: | CSV Content |
| Sharing Status: | Published |
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| Views: | 1952 |
| Downloads: | 146 |
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Abstract
This data contains eddy covariance, soil, and meteorological data collected from the Goose Creek Tower site in central IL from 2016-2024. A 25m tower was installed April 2016 with 15 min measurements, and it was supplemented by a 10m height eddy covariance instrument in August 2021. The "25m" files contain the main tower data (eddy covariance at 25m, soil moisture and temperature, solar radiation and other meteorological variables), and the "10m" is the newer instrument at 30 min resolution. The "raw" files obtained directly from the sensors are provided, with a nomenclature document describing several hundred variable names. We also processed this data using ReddyProc R package (https://cran.r-project.org/web/packages/REddyProc/index.html) for carbon flux partitioning estimates at 30 min resolution, and applied the flux-data-qaqc python package (https://flux-data-qaqc.readthedocs.io/en/latest/) for daily energy balance corrections. We also provide a solar radiation corrected file, due to some sensor errors in incoming and outgoing radiation, and note that some NDVI and PRI (vegetation indices) values contain errors that are not corrected from the raw data. Please see the Readme file for short descriptions of each file type.
Subject Keywords
Coverage
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| U.S. National Science Foundation | CZ Cluster: Critical Interface Network for Intensively Managed Landscapes (CINet) | EAR 2012850 |
| NASA Earth Science | NASA New Investigator Grant: Leveraging information theory and flux tower footprints towards improved satellite-based evapotranspiration estimates | #80NSSC24K1271 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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