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Continuous wave and tide observations at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2022-09 to 2023-07

Data presented here were collected between September 2022 to July 2023 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.77 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

Continuous wave and tide observations (2021) at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2021-01 to 2021-10

Data presented here were collected between January 2021 to October 2021 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

Continuous wave and tide observations (2020) at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2020-01 to 2020-12

Data presented here were collected between January 2020 to December 2020 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

Continuous wave and tide observations at DynaCom artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2019-01 to 2019-12

Data presented here were collected between January 2019 to December 2019 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

Continuous wave and tide observations at BEFmate artificial islands in the back-barrier tidal flat, Spiekeroog, Germany, 2017-04 to 2018-12

Data presented here were collected between April 2017 to December 2018 within the BEFmate project (Biodiversity - Ecosystem Functioning across marine and terrestrial ecosystems, https://uol.de/icbm/verbundprojekte/abgeschlossene-projekte/befmate/ ) of the Universities of Oldenburg and Göttingen and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Local tide and wave conditions were recorded with a RBRduo TDǀwave sensor (RBR Ltd., Ontario/Canada). The sensor was bottom mounted in a shallow tidal creek (0.71 / 0.78 m NHN) through a steel girder (buried 0.3m deep in the sediment) and was positioned 10 cm above sediment surface, as was determined by using a portable differential GPS. This resulted in the sensor falling dry during low tide. For accurate depth calculations, raw pressure data were manually corrected for atmospheric pressure derived from a locally installed weather station. The sensor was pre-calibrated by the manufacturer and the sampling rate was 3 Hz with 1024 samples per burst at a sample interval of 10 min. Recorded data were internally logged until the readout with the Ruskin (V1.13.13) software. Date and time is given in UTC. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, and (c) visually checks. Low-tide data is not removed, but were easily identified through the manually calculated water depth data, where all depths < 0.05m represented low tide data.

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.

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).

KlimPro: Schaffung einer alternativen Verwendung einer auf DRI-Basis erzeugten Elektroofenschlacke für die Zementindustrie zur Verringerung der CO2-Emissionen, Teilprojekt 5: Bereitstellung von direktreduziertem Eisenerz und Begleitung der Schmelzversuche im Elektrolichtbogenofen

KlimPro: Schaffung einer alternativen Verwendung einer auf DRI-Basis erzeugten Elektroofenschlacke für die Zementindustrie zur Verringerung der CO2-Emissionen, Teilprojekt 2: Ökologische und ökonomische Prozessbewertung

KlimPro: Schaffung einer alternativen Verwendung einer auf DRI-Basis erzeugten Elektroofenschlacke für die Zementindustrie zur Verringerung der CO2-Emissionen, Teilprojekt 3: Schmelzversuche und Metallanalytik

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