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.