Other language confidence: 0.9905305277677829
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.
The Global Gravity-based Groundwater Product (G3P) provides groundwater storage anomalies (GWSA) from a cross-cutting combination of GRACE/GRACE-FO-based terrestrial water storage (TWS) and storage compartments of the water cycle (WSCs) that are part of the Copernicus portfolio. The data set comprises gridded anomalies of groundwater, TWS, and the WSCs glacier, snow, soil moisture and surface water bodies plus layers containing uncertainty information for the individual data products. All WSCs are spatially filtered with a Gaussian filter to be compatible with TWS. Spatial coverage is global, except Greenland and Antarctica, with 0.5-degree resolution. Temporal coverage is from April 2002 to September 2023 with monthly temporal resolution. Gridded data sets are available as NetCDF files containing variables for the parameter value as anomaly in mm equivalent water height and the parameter’s uncertainty as mm equivalent water height. The latest version of the data is visualized at the GravIS portal: https://gravis.gfz-potsdam.de/gws. From GravIS, the data is also available as area averages for several large river basins and aquifers, as well as for climatically similar regions. G3P was funded by the EU Horizon 2020 programme in response to the call LC-SPACE-04-EO-2019-2020 “Copernicus evolution – Research activities in support of cross-cutting applications between Copernicus services” under grant agreement No. 870353. --------------------------------------------------------------------------------------------- Version History: 10 March 2023: Release of Version v1.11. That version is the initial release of the data (Güntner et al., 2023; https://doi.org/10.5880/G3P.2023.001) (DATE TO BE ADDED) Release of Version v1.12. Temporal coverage has been extended until September 2023.
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).
The Global Gravity-based Groundwater Product (G3P) provides groundwater storage anomalies (GWSA) from a cross-cutting combination of GRACE/GRACE-FO-based terrestrial water storage (TWS) and storage compartments of the water cycle (WSCs) that are part of the Copernicus portfolio. The data set comprises gridded anomalies of groundwater, TWS, and the WSCs glacier, snow, soil moisture and surface water bodies plus layers containing uncertainty information for the individual data products. All WSCs are spatially filtered with a Gaussian filter to be compatible with TWS. Spatial coverage is global, except Greenland and Antarctica, with 0.5-degree resolution. Temporal coverage is from April 2002 to December 2020 with monthly temporal resolution. Gridded data sets are available as NetCDF files containing variables for the parameter value as anomaly in mm equivalent water height and the parameter’s uncertainty as mm equivalent water height. The latest version of the data is visualized at the GravIS portal: https://gravis.gfz-potsdam.de/gws. From GravIS, the data is also available as area averages for several large river basins and aquifers, as well as for climatically similar regions. G3P was funded by the EU Horizon 2020 programme in response to the call LC-SPACE-04-EO-2019-2020 “Copernicus evolution – Research activities in support of cross-cutting applications between Copernicus services” under grant agreement No. 870353. --------------------------------------------------------------------------------------------- Version History: 10 March 2023: Release of Version v1.11. This is the initial release of the data.
sandbox is an R-tool for probabilistic numerical modelling of sediment properties. A flexible framework for definition and application of time/depth- based rules for sets of parameters for single grains that can be used to create artificial sediment profiles. Such profiles can be used for virtual sample preparation and synthetic, for instance, luminescence measurements.
Central Europe is an intraplate domain which is characterized by low to moderate seismicity with records of larger seismic events occurring in historical and recent times. These records of seismicity are restricted to just over one thousand years. This does not reflect the long seismic cycles in Central Europe which are expected to be in the order of tens of thousands of years. Therefore, we have developed a paleoseismic database (PalSeisDB) that documents the records of paleoseismic evidence (trenches, soft-sediment deformation, mass movements, etc.) and extends the earthquake record to at least one seismic cycle. It is intended to serve as one important basis for future seismic hazard assessments. In the compilation of PalSeisDB, paleoseismic evidence features are documented at 129 different locations in the area of Germany and adjacent regions. A brief explanation of the folder structure, file list and file contents included in the data publication of PalSeisDB is provided in the data description .A detailed explanation of the data collection, the content of the data files and the table headers is available (Hürtgen et al., 2020). A full list of source references for PalSeisDB is provided in Hürtgen (2017, Appendix 8.3, p. 128 ff) and also included in the zip folder here
This data publication contains mineralogical, geochemical and magnetic susceptibility data of an 87.2 m deep profile of hydrothermally altered plutonic rock in a semi-arid region of the Chilean Coastal Cordillera (Santa Gracia). The profile was recovered during a drilling campaign (March and April 2019) as part of the German Science Foundation (DFG) priority research program SPP-1803 “EarthShape: Earth Surface Shaping by Biota” which aims at understanding weathering of plutonic rock in dependency on different climatic conditions. The goal of the drilling campaign was to recover the entire weathering profile spanning from the surface to the weathering front and to investigate the weathering processes at depth. To this end, we used rock samples obtained by drilling and soil/saprolite samples from a manually dug 2 m deep soil pit next to the borehole. To elucidate the role of iron-bearing minerals for the weathering, we measured the magnetic susceptibility, determined the mineral content and analysed the geochemistry as well as the composition of Fe-bearing minerals (Mössbauer spectroscopy) in selected samples.
Large rock slope failures play a pivotal role in long-term landscape evolution and are a major concern in land use planning and hazard aspects. While the failure phase and the time immediately prior to failure are increasingly well studied, the nature of the preparation phase remains enigmatic. This knowledge gap is due, to a large degree, to difficulties associated with instrumenting high mountain terrain and the local nature of classic monitoring methods, which does not allow integral observation of large rock volumes. Here, we analyse data from a small network of up to seven seismic sensors installed during July--October 2018 (with 43 days of data loss) at the summit of the Hochvogel, a 2592 m high Alpine peak. We develop proxy time series indicative of cyclic and progressive changes of the summit. Fundamental frequency analysis, horizontal-to-vertical spectral ratio data and end-member modelling analysis reveal diurnal cycles of increasing and decreasing coupling stiffness of a 126,000 m^3 large, instable rock volume, due to thermal forcing. Relative seismic wave velocity changes also indicate diurnal accumulation and release of stress within the rock mass. At longer time scales, there is a systematic superimposed pattern of stress increases over multiple days and episodic stress release within a few days, expressed in an increased emission of short seismic pulses indicative of rock cracking. We interpret our data to reflect an early stage of stick slip motion of a large rock mass, providing new information on the development of large-scale slope instabilities towards catastrophic failure.
The data collection presented here is the data inventory of the VARved sediments DAtabase (VARDA) in version 1.3. VARDA is freely accessible and was created to assess outputs from climate models with high-resolution terrestrial palaeoclimatic proxies. All data were collected as raw data from freely available online sources, either from online data repositories (Pangaea, NOAA, and Neotoma) or data archives within the supplementary materials section of online publications. The current data collection consists of meta information and datasets from 95 lake archives. The data is stored in JSON and CSV format. All datasets are stored as individual files (JSON and CSV). Each dataset consists of samples for either i) chronologies; ii) radiocarbon data; iii) tephra layer; or iv) varve thickness data. Meta-information for each dataset is summarized in one csv and seven JSON files. Additional paleoclimate proxy data will be provided in forthcoming updates of VARDA. The data collection of VARDA Version 1.3 is provided as an archive (.tar.gz) with the following files/folders. Overview lists with categories, cores, countries, datasets, lakes and publications included in VARDA. Each item in the lists is cross-referenced with the other files via its $ref property which includes the corresponding list index or the dataset's UUID (from the VARDA database). The data points themselves are provided in the "records" folder and named with each dataset's UUID respectively. For more information on the data structure please read the "index.html" file included in the archive and available on the DOI landing page. VERSION HISTORY: 26 July 2020: release of Version 1.3: 1. Fix issues with chronologies in the export 2. Provide recalculated machine readable error estimates 3. Correct some metadata values (e.g. core labels) 5 March 2020: release of Version 1.1 1. Added fields: "distributor" - Field containing name of data distributor "url" - Field containing DOIs and URLs, which lead to the original data publications 2. Correction of publication DOIs in 9 cases The version 1.0 is available in the "previous-versions" subfolder via the Data Download link. The index file is unchanged.
EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).
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