Abstract
Abstract. The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”. Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (https://www.tr32db.uni-koeln.de/data.php?dataID=1889, last access: 29 September 2020) and has the DOI https://doi.org/10.5880/TR32DB.39 (Reichenau et al., 2020).
Highlights
System states and processes at the land surface are of major interest in the context of climate change and hydrological and biogeochemical research
We present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) “Patterns in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”
The dataset comprises data from four sites. It consists of almost 1500 records of vegetation parameters and more than 200 000 entries of weather data complemented by 15 flux datasets, management information for 27 management periods, and soil information for all four sites
Summary
System states and processes at the land surface are of major interest in the context of climate change and hydrological and biogeochemical research. Dependencies of processes on vegetation states and properties and on environmental conditions are often investigated using models, while their spatial variability is inferred using remote-sensing techniques. In this context, well-documented and quality-controlled comprehensive field measurements of vegetation-related variables are essential for research tasks like model development, calibration, parameterization, and validation or as ground truth for remote-sensing products. Well-documented and quality-controlled comprehensive field measurements of vegetation-related variables are essential for research tasks like model development, calibration, parameterization, and validation or as ground truth for remote-sensing products These variables include biomass per organ differentiated between living (green) and senescent or diseased (brown) material, leaf area index (LAI), and the phenological state of the vegetation. With the provision of this dataset, we want to document our measurement and quality control strategy and provide the scientific community with a comprehensive dataset for further applications
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