126 documents found in 540ms
# 1
Reyer, Christopher • Silveyra Gonzalez, Ramiro • Dolos, Klara • Hartig, Florian • Hauf, Ylva • (et. al.)
Abstract: Current process-based vegetation models are complex scientific tools that require proper evaluation of the different processes included in the models to prove that the models can be used to integrate our understanding of forest ecosystems and project climate change impacts on forests. The PROFOUND database (PROFOUND DB) described here aims to bring together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It has been designed to fulfill two objectives:- Allow for a thorough evaluation of complex, process-based vegetation models using multiple data streams covering a range of processes at different temporal scales- Allow for climate impact assessments by providing the latest climate scenario data. Therefore, the PROFOUND DB provides general a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data for 9 forest stands spread throughout Europe. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. The climate and nitrogen deposition data contains several datasets for the historic period and a wide range of future climate change scenarios following the Representative Emission Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). In addition, we also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND Database is available freely but we incite users to respect the data policies of the individual datasets as provided in the metadata of each data file. The database can also be accessed via the PROFOUND R-package, which provides basic functions to explore, plot and extract the data. The data (PROFOUND DB) are provided in two different versions (ProfoundData.sqlite, ProfoundData_ASCII.zip) and documented by the following three documents: (1) PROFOUNDdatabase.pdf: describes the structure, organisation and content of the PROFOUND DB.(2) PROFOUNDsites.pdf: displays the main data of the PROFOUND DB for each of the 9 forest sites in tables and plots.(3) ProfoundData.pdf: explains how to use the PROFOUND R-Package "ProfoundData" to access the PROFOUND DB and provides example scripts on how to apply it.
# 2
Oeser, Ralf A. • Stroncik, Nicole • Moskwa, Lisa-Marie • Bernhard, Nadine • Schaller, Mirjam • (et. al.)
Abstract: The Chilean Coastal Cordillera features a spectacular climate and vegetation gradient, ranging from arid and unvegetated areas in the north to humid and forested areas in the south. The DFG Priority Program "EarthShape" (Earth Surface Shaping by Biota) uses this natural gradient to investigate how climate and biological processes shape the Earth's surface. We explored the critical zone, the Earth's uppermost layer, in four key sites located in desert, semidesert, mediterranean, and temperate climate zones of the Coastal Cordillera, with the focus on weathering of granitic rock. Here, we present first results from four ~2m-deep regolith profiles to document: (1) architecture of weathering zone; (2) degree and rate of rock weathering, thus the release of mineral-derived nutrients to the terrestrial ecosystems; (3) denudation rates; and (4) microbial abundances of bacteria and archaea in the saprolite. From north to south, denudation rates from cosmogenic nuclides are ~10 t km-2 yr-1 at the arid Pan de Azúcar site, ~20 t km-2 yr-1 at the semi-arid site of Santa Gracia, ~60 t km-2 yr-1 at the mediterranean climate site of La Campana, and ~30 t km-2 yr-1 at the humid site of Nahuelbuta. A and B horizons increase in thickness and elemental depletion or enrichment increases from north (~26 °S) to south (~38 °S) in these horizons. Differences in the degree of chemical weathering, quantified by the chemical depletion fraction (CDF), are significant only between the arid and sparsely vegetated site and the other three sites. Differences in the CDF between the sites, and elemental depletion within the sites are sometimes smaller than the variations induced by the bedrock heterogeneity. Microbial abundances (bacteria and archaea) in saprolite substantially increase from the arid to the semi-arid sites. With this study, we provide a comprehensive dataset characterizing the Critical Zone geochemistry in the Chilean Coastal Cordillera. This dataset confirms climatic controls on weathering and denudation rates and provides prerequisites to quantify the role of biota in future studies. The data are supplementary material to Oeser et al. (2018). All samples are assigned with International Geo Sample Numbers (IGSN), a globally unique and persistent Identifier for physical samples. The IGSNs are provided in the data tables and link to a comprehensive sample description in the internet. The content of the eight data tables is: Table S1: Catena properties of the four primary EarthShape study areas.Table S2: Major and selected trace element concentration for bedrock samples.Table S3 Normative modal abundance of rock-forming minerals.Table S4: Major and selected trace element concentration for regolith samples and dithionite and oxalate soluble pedogenic oxides.Table S5: Weathering indices CDF and CIA, and the mass transfer coefficients (τ) for major and trace elements along with volumetric strain (ɛ).Table S6: Chemical weathering and physical erosion ratesTable S7: Relative microbial abundances in saprolite of the four study areas.Table S8: Uncorrected major and trace element concentration. The data tables are provided as one Excel file with eight spreadsheets, as individual tables in .csv format in a zipped archive and as printable PDF versions in a zipped archive.
# 3
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 4
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
# 5
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1:improved calculation and mapping, for details see README.PDF
# 6
Porwollik, Vera • Rolinski, Susanne • Müller, Christoph
Abstract: Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on type and spatial distribution. This dataset is the result of a study on global classification of tillage practices and the spatially explicit mapping of crop-specific tillage systems for around the year 2005. This global gridded tillage system data set is dedicated to modeling communities interested in the quantitative assessment of biophysical and biogeochemical impacts of land use and soil management on cropland. The data set is complemented by the publication of the R- code and can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as Conservation Agriculture (Porwollik et al. 2018, http://doi.org/10.5880/PIK.2018.013). Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). We present the mapping result of six tillage systems for 42 crop types and potential suitable Conservation Agriculture area as the following variables: We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = Scenario Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference): 1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. GLADIS - Water erosion: Nachtergaele et al. (2011) CHANGELOG for Version 1.1improved calculation and mapping, for details see README.PDF
This tillage dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/.
# 7
Arzhanov, Maxim • Betts, Richard • Eliseev, Alexey • Morfopoulos, Catherine • Schaphoff, Sibyll • (et. al.)
Abstract: Description of changes in the new version:- On October 18, 2018 we republished all simulation data for all impact models to get the data sets into the new search facet structure. There were no changes to the simulation data.- Files for JULES-B1 (formerly JULES_UoE) were not available since the date of issuing the DOI until March 13, 2019. Until that date, these files were only available in the ISIMIP DKRZ server. ---------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2018.006 before March 13, 2019 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de--------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 8
Arzhanov, Maxim • Betts, Richard • Eliseev, Alexey • Morfopoulos, Catherine • Schaphoff, Sibyll • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see ISIMIP 2a Input Data & Bias Correction at https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from permafrost models: JULES-B1 (formerly JULES_UoE), LPJmL, IAPRAS-DSS.
The ISIMIP2a Permafrost outputs are based on simulations from 3 permafrost models (see listing) according to the ISIMIP2a Simulation Protocol (https://www.isimip.org/protocol/#isimip2a). The models simulate coupled water and carbon processes, like the soil carbon storage on permafrost soils, non-linear effects in changing vegetation and fire, and the physical state of the permafrost based on soil, climate and physio-geographical information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/.
# 9
Itzerott, Sibylle • Hohmann, Christian • Stender, Vivien • Maass, Holger • Borg, Erik • (et. al.)
Abstract: This data collection compiles the soil moisture stations of the DEMMIN test site operated by the GFZ German Research Centre for Geosciences in cooperation with the National Ground Segment Neustrelitz (Remote Sensing Data Center, German Aerospace Center DLR). The site was originally installed by the DLR in 2000 and has become part of the TERENO Northeastern German Lowland Observatory in 2011. This data collection only comprises the GFZ soil moisture stations. Climate stations operated by DLR and GFZ are published as separate data compilations (Borg et al. 2018, Itzerott et al., 2018). The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulating ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (1981–2010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25-15 k ago (Weichselian glaciation). Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ) and 63 soil moisture stations (GFZ). A lysimeter-hexagon (DLR, FZJ) was installed near to the village Rustow and is part of the SOILCan project. A crane completes the measurement infrastructure currently available in the test site installed by GFZ/ DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data is carried out by DLR using visual inspection and automatic quality processing is performed by GFZ since 2012. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The dataset is also available through the TERENO Data Discovery Portal. The dataset will be dynamically extended as more data is acquired at the stations. New data will be added after a delay of several months to allow manual interference with the quality control process. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich – FZJ, Helmholtz Centre for Environmental Research – UFZ, Karlsruhe Institute of Technology – KIT, Helmholtz Zentrum München - German Center for Environmental Health – HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center – DLR) (http://www.tereno.net/overview-de)..TERENO spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.
# 10
Itzerott, Sibylle • Hohmann, Christian • Stender, Vivien • Maass, Holger • Borg, Erik • (et. al.)
Abstract: The Ueckeritz BF1 soil moisture station is part of an agrometeorological test site and aims at supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration.The site is intensively used for practical tests of remote sensing data integration in agricultural land management practices. First measurement infrastructure was installed by DLR in 1999 and instrumentation was intensified in 2011 and later as the site became part of the TERENO-NE observatory. The soil moisture station station Ueckeritz BF1 was installed in 2014. It is located next to a pylon on a flat field. The station is equipped with sensor for measuring the following variables: Spade_1, Spade_2, Spade_3, Spade_4, Spade_5 and Spade_6 The dataset is also available through the TERENO Data Discovery Portal. The datafile will be extended once per year as more data is acquired at the stations and the metadatafile will be updated. New columns for new variables will be added as necessary. In case of changes in dta processing, which will result in changes of historical data, an new Version of this dataset will be published using a new doi. New data will be added after a delay of several months to allow manual interference with the quality control process.
The DEMMIN test site is located within the central monitoring sites of the TERENO Northeastern German Lowland Observatory. It covers 900 km² and exhibits mostly glacial formed lowlands with terminal moraines in the southern part, containing the highest elevation of 83m a.s.l. The region between the rivers Tollense and Peene consists of flat ground moraines, whereas undulation ground moraines determine the landscape character north of the river Peene. The lowest elevation is located near the town Loitz with 0.5m a.s.l. The region is characterized by intense agricultural use and the three rivers Tollense and Trebel which confluence into the Peene River at the Hanseatic city Demmin. The present climate is characterized by a long-term (1981–2010) mean temperature of 8.7 °C and mean precipitation of 584 mm/year, measured at the Teterow weather station by Deutscher Wetterdienst (DWD). The Northeastern German Lowland Observatory is situated in a region shaped by recurring glacial and periglacial processes since at least half a million years. Within this period, three major glaciations covered the entire region, the last time this happened approximately 25 15 k ago (Weichselian glaciation).Since that time, a young morainic landscape developed characterized by many lakes and river systems that are connected to the shallow ground water table. The test site is instrumented with more than 40 environmental measurement stations (DLR, GFZ). Additionally, 63 soil moisture stations were installed by GFZ, a lysimeter-hexagon (DLR, FZJ) near to the village Rustow and is part of the SOILCan project. A crane completes the measurement technique currently available in the test site installed by GFZ/DLR in 2011. Data is automatically collected via a telemetry network by DLR. The quality control of all environmental data transferred via Telemetry network of DLR is carried out by DLR by visual control and, since 2012, by automatic processing by GFZ. The delivered dataset contains the measured data and quality flags indicating the validity of each measured value and detected reasons for exclusion. The TERENO (TERrestrial ENvironmental Observatories) is an initiative of the Helmholtz Centers (Forschungszentrum Jülich – FZJ, Helmholtz Centre for Environmental Research – UFZ, Karlsruhe Institute of Technology – KIT, Helmholtz Zentrum München - German Center for Environmental Health – HMGU, German Research Centre for Geosciences - GFZ, and German Aerospace Center – DLR) (http://www.tereno.net/overview-de). TERENO Northeastern German Lowland Observatory.TERENO (TERrestrial ENvironmental Observatories) spans an Earth observation network across Germany that extends from the North German lowlands to the Bavarian Alps. This unique large-scale project aims to catalogue the longterm ecological, social and economic impact of global change at regional level. Further specific goals of the TERENO remote sensing research group at GFZ are (1) supplying environmental data for algorithm development in remote sensing and environmental modelling, with a focus on soil moisture and evapotranspiration, and (2) practical tests of remote sensing data integration in agricultural land management practices.
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