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Half-hourly CO2 eddy covariance flux data, associated meteorological data and Sentinel-2 derived vegetation indices (7) for 05/03/2020 - 23/08/2022 [data]

This repository contains all the data 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 provided data form the basis for running the data processing scripts sequentially for (re-)producing all statistical analyses, results and figures in the article. The data are given in the formats as used in the data-processing scripts written in R, MATLAB and JavaScript of Google Eearth Engine. All codes for processing the data and a workflow description can be found here. The dataset covers three types of data: half-hourly eddy covariance (EC) data, satellite derived vegetation indices and GIS/image data. Continuous EC CO2 fluxes (03/2020 - 08/2023) are measured at the agricultural site "Heydenhof" in Northeastern Germany. The data file is provided in .mat (MATLAB) format containing the standard EddyPro software output variables which are described in an accompanying meta data file. The land use information used for footprint modeling is included as .jpeg and .png-files for visulisation and as .mat-file to be used for running the footprint modeling script. Sentinel-2 vegetation indices are provided as .csv files. These files are provided for convenience and version control only as the JavaScript for generating Sentinel-2 derived vegetation indices in Google Earth Engine is provided in the associated code repository. Here, the field boundaries are provided as shape file. Data file description: "HEY_LandUse_image.mat": MATLAB file in raster format, containing the land use codes in a 4x4 km raster with a resolution of 1 m used for running the Korman-Meixner foot print model for flux source area attribution. "meta_data_HEY_LandUse_image.txt": description of land use codes used in the "HEY_LandUse_image.mat" "HEY_LandUse_image.png": Visualisation of HEY_LandUse_image.mat. Figure A2 in manuscript. Showing the land use distribution around the measurement tower encoded in the number of land use classes used for foot print modeling. "HEYDENHOF.jpeg": Visualisation of land use classes from digitisation. Auxiliary information. Showing the land use distribution around the measurement tower. "HEY_FluxData_20200304_20220824_all_data.mat": MATLAB data file containing the half-hourly EC measurements plus auxiliary meteorological variables from 04/03/2020 to 24/08/2022 in matrix format with rows being the half-hourly measurements and including the unique time identifier "Timestamp", and "NaN" as missing data value. "meta_data_HEY_FluxData.txt": text file accompanying "HEY_FluxData_20200304_20220824_all_data.mat" containing the variable names, units, format, range and description for the variables of "HEY_FluxData_20200304_20220824_all_data.mat" "TERENO_prec_data_2020_2022.csv": comma separated text file containing the half-hourly precipitation data for the measurement site (HEY) from 01/01/2020 to 13/10/2022. "meta_data_TERENO_prec.txt": text file accompanying " TERENO_prec_data_2020_2022.csv " containing the variable description of the TERENO precipitation data. "HEY_tower_field.zip": zipped shape file outlining the agricultural field used as source area for the satellite data retrieval. "S2.csv": comma separated text file containing the vegetation indices from Sentinel-2 for the agricultural field from 02/03/2020 to 29/08/2022. "meta_data_Sentinel2_S2.txt": text file accompanying "S2.csv" containing the variable description of Sentinel-2 derived vegetation indices, i.e. "S2.csv". "S2_SD.csv": comma separated text file containing the standard deviation of the vegetation indices for the agricultural field from 02/03/2020 to 29/08/2022. "meta_data_Sentinel2_S2_SD.txt": text file accompanying "S2_SD.csv" containing the variable description of the standard deviation for the Sentinel-2 derived vegetation indices.

Airborne Wind and Eddy Covariance Dataset - Recorded with the ASK-16 EC Platform between 2017 – 2022

This data publication contains airborne wind and eddy covariance data files, that were recorded with the ASK-16, a motorized glider owned by the FU Berlin, Germany. These data files include a large range of meteorological variables (wind speed, direction, temperature, humidity, etc.), positioning information, but also information on atmospheric chemistry (mainly methane concentration, carbon dioxide concentration, water vapor concentration) and turbulent matter (CH4 and CO2) and energy fluxes (latent heat flux) is available. Measurements were recorded between 2017 and 2022 to: (1) obtain three-dimensional wind vectors in within the atmospheric boundary layer (2) calibrate of wind measurements (3) record turbulent energy and matter fluxes A lot of these data files have been used in the publication “The ASK-16 Motorized Glider: An Airborne Eddy Covariance Platform to measure Turbulence, Energy and Matter Fluxes (to be published in atmospheric measurement techniques)” by Wiekenkamp et al., 2024a. This publication also provides a lot of additional details on the measurement system, the data handling and processing.

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]

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).

Surface energy balance observations at a grassland site in Luxembourg

This dataset provides half-hourly surface energy balance measurements for a temperate grassland site in Luxembourg. The data were obtained during a field campaign in June and July 2015. The observations comprise multiple variables measurements by an Eddy-Covariance station, a net radiometer, soil moisture, temperature and soil heat flux probes and meteorological standard measurements. For details please see the reference article Renner et al. (2019, HESS) with the general setup described in Wizemann et al., 2015. The data are complemented by half-hourly model output of sensible and latent heat fluxes that are published as individual data publication (Renner et al., 2018).The data is provided as comma-separated-values (csv) format in a long table format. Columns represent Date, Time, variable, value, source. The column “variable” sets the name of the variable (following CEOP standards) with an information of the measurement depth for soil measurements. Column “source” describes the data source with an acronym(Observations “ObsEC”).The data contributes to the Joint Research Group "Catchments As Organized Systems" (CAOS) funded by the German Research Foundation.Methods: Eddy Covariance, Surface energy balance observations

PyWingpod

In the last years, a whole series of codes has been developed to process airborne wind data. Initially, the PyWingpod package was mainly build to handle data from the Wingpod of the ASK-16 motorized glider of the FU Berlin. However, due to the modular buildup of the package, functions within the different libraries can also be used to process data from other airborne platforms. Functions and scripts within PyWingpod have been developed to: a. load and process airborne five hole probe and meteo data, this includes (1) 5 hole probe pressure sensor data (static pressure, dynamic pressure and the differential alpha and beta pressure), (2) INS-GNSS data, (3) Temperature and humidity data and (4) any auxillary data that you want to add to the time series/ data frame. b. calibrate pressure sensor data from the five hole probe (mainly to correct for any effect of aircraft movement) c. calculate a reliable wind vector based on the available data that are specified in a. and the calibration parameters, which are obtained in step b.

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