5 documents found in 102ms
# 1
Reichenau, Tim G. • Korres, Wolfgang
Abstract: LAI was modelled for arable land in the northern part of the Rur catchment (fertile loess plain) for the year 2009. Modelled crops are Winter Wheat, Sugar Beet, Maize, and Winter Barley. Data was modelled using the dynamically coupled process-oriented ecohydrological models from the DANUBIA simulation system using the configuration described by Korres at al. (2013, doi:10.1016/j.jhydrol.2013.05.050). This paper does also provide model validation data for LAI.
# 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
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.
# 5
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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