Code for linking half-hourly CO2 eddy covariance flux data with Sentinel-2 
derived vegetation indices (7) for 05/03/2020 - 23/08/2022 [code]

Gottschalk, Pia; Kalhori, Aram; Li, Zhan; Wille, C.; Sachs, T.

doi:10.5880/GFZ.1.4.2024.002

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Code for linking half-hourly CO2 eddy covariance flux data with Sentinel-2 derived vegetation indices (7) for 05/03/2020 - 23/08/2022 [code]

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/persons/resource/pgottsch

Gottschalk, Pia
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/akalhori

Kalhori, Aram
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Li, Zhan
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/persons/resource/cwille

Wille, C.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/tsachs

Sachs, T.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Gottschalk, P., Kalhori, A., Li, Z., Wille, C., Sachs, T. (2024): Code for linking half-hourly CO2 eddy covariance flux data with Sentinel-2 derived vegetation indices (7) for 05/03/2020 - 23/08/2022 [code].
https://doi.org/10.5880/GFZ.1.4.2024.002

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5029098

Abstract

This repository provides the code used for the article "Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery" by Pia Gottschalk, Aram Kalhori, Zhan Li, Christian Wille, Torsten Sachs. The data are used to exemplify how ground measured CO2 fluxes of an agricultural field can be linked with remotely sensed vegetation indices to provided an upscaling approach for spatial CO2-flux projection. The repository contains the codes produced for the article "Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery" by Pia Gottschalk, Aram Kalhori, Zhan Li, Christian Wille, Torsten Sachs. In this article, the authors present how local carbon dioxide (CO2) ground measurements and satellite data can be linked to project CO2 emissions spatially for agriculutral fields. The codes are provided for - footprint analysis and raw flux data quality control (MATLAB codes); - retrieving Sentinel-2 vegetation indices via Google Earth Engine (GEE code); - subsequent quality control, gap-filling and flux partitioning following the MDS approach by Reichstein et al. 2005 implemented by the R-package "REddyProc" (R codes); - statistical analyses of combined EC and Sentinel-2 data (R codes); - code for all figures as displayed in the manuscript (R codes). This software is written in MATLAB, R and JavaScript (GEE). Running the codes (R and .m files (Code)) and loading the data files (CSV files and .mat files (Data)) requires the pre-installation of [R and RStudio] (https://posit.co/downloads/) and (MATLAB). The GEE script runs in a browser and can also be opened/downloaded here: https://code.earthengine.google.com/858361ae4aac7c3fe5227076c9733040 The RStudio 2021.09.0 Build 351 version has been used for developping the R scripts. The land cover classification work was performed in QGIS, v.3.16.11-Hannover. Data were analyzed in both MATLAB and R; and plots created with R (R Core Development Team 2020) in RStudio®.The R codes in this repository contain a suite of external R-packages ("zoo"; "REddyProc"; "Hmisc"; "PerformanceAnalytics") which are required for data analysis in this manuscript. The data to run the codes are published with the DOI https://doi.org/10.5880/GFZ.1.4.2023.008 (Gottschalk et al., 2023).