7 documents found in 137ms
# 1
Waldhoff, Guido • Lussem, Ulrike
Abstract: This data set contains the final land use classification of 2015 - update for the study area of the CRC/Transregio 32: "Patterns in Soil-Vegetation-Atmosphere Systems: monitoring, modelling and data assimilation", which corresponds to the catchment of the river Rur. The study area is mainly situated in the western part of North Rhine-Westphalia (Germany) and parts of the Netherlands and Belgium. The classification is provided in GeoTIFF and in ASCII format. Spatial resolution: 15 m; Projection: WGS84, UTM Zone 32N.
# 2
Reichenau, Tim G. • Stadler, Anja • Korres, Wolfgang • Langensiepen, Matthias • Ewert, Frank • (et. al.)
Abstract: LAI field measurements used in Reichenau et. al (2016), "Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)". Name of th dataset in the article: field. The zip-File contains a data-file (LAI_field_obs.csv) and an information file (README). The data-file contains a table with point observations of green LAI in the northern part of Rur catchment (Juelicher Boerde). LAI was determined destructively on several points per field and several dates per growing season as described by Reichenau et al. (2016). Data was filtered for outliers, only high quality data was used for the analysis. For information on columns, abbreviations, etc. read the README file.
# 3
Reichenau, Tim G. • Montzka, Carsten • Waldhoff, Guido • Korres, Wolfgang • Schneider, Karl
Abstract: LAI from remote sensing used in Reichenau et al. (2016), "Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)". Name of th dataset in the article: rs5m. The dataset contains LAI for the arable area of the northern part of the Rur catchment (Juelicher Boerde). The data (5 m resolution) was generated from RapidEye remote sensing data using a method described by Hasan et al. (2014) as shown in Reichenau at al. (2016). Pixels with potentially heterogeneous vegetation, were excluded from the evaluation. For this means, pixels from a 15 m resolution land use dataset (Lussem and Waldhoff, 2014), which are not surrounded by the same land use type were marked as potentially mixed. Corresponding pixels from the LAI dataset were removed. RapidEye data were provided by the RapidEye Science Archive (RESA). The zip-file contains separate files for seven dates in 2011 where cloud-free scenes were recorded for (almost) the entirety of the Rur-catchment. Each file is accompanied by a landuse file. For additional information including filenames etc. read the included README file. Spatial resolution: 5 m; Projection: WGS84, UTM Zone 32N.
# 4
Huber, Katrin • Vanderborght, Jan • Javaux, Mathieu • Schnepf, Andrea • Schröder, Natalie • (et. al.)
Abstract: R-SWMS is a numerical model for simulating solute transport and water flow in and between the soil and the plant systems. The acronym stands for Modeling “Root-Soil Water Movement and Solute transport”. Based on the flow and transport equations in the 3D soil matrix and within the 3D root xylem network, it simulates the uptake of solute and water by plant roots for a growing plant. Three-dimensional root growth is function of environmental conditions (soil strength, temperature) and plant parameters (gravitropism, sensitivity to strength, etc.). The code has been used in several projects and labs around the world. An updated list of publications dealing with R-SWMS can be found at https://www.zotero.org/groups/r-swms. The handbook includes theory, numerics, input files, output files, installation, and some example calculations.
# 5
Huber, Katrin
Abstract: The dataset was generated using the simulation tool rswms (used version of the model is included in the dataset). The model describes the 3D water flow in soil and the root system. For this study it was extended by a module describing stomatal closure as a function of plant hormones generated in the root system. The dataset includes the input and output data used for the study. The results can be found in the article: Huber, K., et al. (2014) 'Modelling the impact of heterogeneous rootzone water distribution on the regulation of transpiration by hormone transport and/or hydraulic pressures' (DOI: 10.1007/s11104-014-2188-4). An more detailed description of the model as well as the input and output files can be found in the rswms user manual (DOI: 10.5880/TR32DB.25). If you want to use this data / model, please contact one of the authors.
# 6
Reichenau, Tim G. • Korres, Wolfgang • Schneider, Karl
Abstract: LAI from simulation used in Reichenau et. al (2016), "Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)". Name of the dataset in the article: sim. The dataset contains LAI for the main crops (maize, sugar beet, winter wheat) in the arable area of the fertile loess plain in the northern part of the Rur catchment. The data (150 m resolution) was simulated using the DANUBIA simulation system (Barth et al., 2004; Barthel et al., 2012) as described in Reichenau at al. (2016). Data is given in separate files for each day in 2011. The dataset is accompanied by a landuse file. The landuse data on 150 m resolution was generated by assigning the most frequent landuse type from the 15 m resolution landuse map (Lussem and Waldhoff, 2014, DOI 10.5880/TR32DB.7) to each 150 m pixel. Spatial resolution: 150 m; Projection: WGS84, UTM Zone 32N.
# 7
Reichenau, Tim G. • Montzka, Carsten • Wilken, Florian • Korres, Wolfgang • Schneider, Karl
Abstract: Field mean LAI from remote sensing used in Reichenau et. al (2016), "Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)". Name of the dataset in the article: rsfm. The table contains data on mean values and standard deviations of LAI for 24712 agricultural fields in the fertile loess plain of the Rur catchment. The data is based on LAI data generated from RapidEye remote sensing data (5 m resolution) using the method shown in Reichenau at al. (2016) based on Hasan et al. (2014). Fields were defined as continuous areas with uniform land use. Pixels with potential heterogeneous vegetation were excluded from the evaluation. For this means, pixels from a 15 m resolution land use dataset (Lussem and Waldhoff, 2014), that are not surrounded by the same land use type were marked as potentially mixed. Corresponding pixels from the LAI dataset were removed prior to the calculation of the mean values and standard deviations. Data is given for seven dates in 2011 where cloud-free scenes were recorded for (almost) the entirety of the Rur-catchment. Since the remote sensing scenes do not always cover the entirety of each field, the area of each field is given separately for each date. RapidEye data were provided by the RapidEye Science Archive (RESA).
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