Truckenbrodt, Sina C; Baade, Jussi (2017): Gebesee Database for the enhancement of crop monitoring applications: evolution of plant physiology, soil moisture, surface reflectance and atmospheric conditions on the agricultural Gebesee test site (central Germany) in 2013 and 2014 [dataset publication series]. PANGAEA, https://doi.org/10.1594/PANGAEA.874251,

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Supplement to: Truckenbrodt, Sina C; Schmullius, Christiane C (2018): Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models. Earth System Science Data, 10(1), 525-548, https://doi.org/10.5194/essd-10-525-2018

Ground reference data are a prerequisite for the calibration, update and validation of land data assimilation systems facilitating large-scale crop monitoring based on Earth Observation data. Here, we provide a comprehensive database which was elaborated to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS). In situ data was collected for seven crop types cultivated on the agricultural Gebesee test site (central Germany) in 2013 and 2014, i.e., winter barley, winter wheat, spring wheat, durum, winter rape, potato and sugar beet. The database contains information on the evolution of biophysical and biochemical plant parameters, phenology, leaf structure, soil moisture, atmospheric states and illumination conditions. In addition, surface reflectance was recorded with a field spectrometer. Data was collected with an approximately weekly resolution throughout the growing season. In addition, it was attempted to schedule days of field work according to the acquisition plans of RapidEye, SPOT5, Landsat 7 and 8, and on days with a high probability of low cloud coverage. Data was collected on up to three elementary sampling units (ESUs) per acreage. ESUs represent squares with a diagonal length of 24 m and consist of five secondary sampling points (SSPs). This experimental design permits an assessment of spatio-temporal variations of the investigated parameters. Coordinates of survey sites, nearby ground control points (GCPs) and check points (CPs) were recorded with a differential Global Navigation Satellite System (dGNSS) which supports accurate geo-referencing of satellite images. The experimental design of the field campaigns, methods employed in the determination of all parameters, and meteorological conditions in the measurement period are described in detail in Truckenbrodt & Schmullius, 2017.

Funding for the set-up of this dataset by the German Federal Ministry of Economic Affairs and Energy via the research project “Application of the EO-LDAS Prototype and Database to Prepare Sentinel-2 Assimilation” (grant: 50EE1307) is gratefully acknowledged.