Satellite and airborne remote sensing of the Earth's environment is a comparatively young science which is characterized by technological innovations in short temporal intervals. For successful, sustainable, and timely utilization of new and upcoming remote sensing data, methodological knowhow and new skills (e.g. handling imaging spectrometer and full polarimetric SAR data) are required by individuals. Training people in these competences is essential to ensure high quality in the field. Therefore, attention has to be turned to teaching and learning remote sensing in higher education (and even earlier) as an ongoing and scholarly process to address the academic and industrial market requirements. Aside from its fast development, a further didactic challenge is the interdisciplinary character of remote sensing, including, for example, competences from electrical engineering, earth sciences, physics, or biology. Interdisciplinary -E-learning concepts will be developed in cooperation with international scientific partners.
Ground reference data are essential for the calibration, update and validation of empirically and physically based models as well as hybrid methods that facilitate crop monitoring with the help of Earth Observation data. This database contains in situ measured values from 2016 for various parameters characterizing vegetation, soil and atmosphere conditions all of which are required to test and validate the Earth Observation Land Data Assimilation System (EO-LDAS). This new database is complementary to another database for the growing seasons in 2013 and 2014 that was previously published by Truckenbrodt & Baade (2017; doi:10.1594/PANGAEA.874251) and has been described in detail by Truckenbrodt & Schmullius (2018). In 2016, ground reference data were collected for five crop types (i.e., winter barley, winter wheat, spring wheat, potato and sugar beet) grown on the agricultural Gebesee test site (central Germany). Between May and October, hyperspectral surface reflectance, information on the evolution of biophysical and biochemical plant parameters (like leaf area index, biomass and leaf chlorophyll content), phenology, leaf structure, soil moisture, atmospheric states and illumination conditions were recorded. The field working days were preferentially scheduled for days with expectedly low cloud coverage and close to overflights of Sentinel-2, Landsat 7 and 8. Each crop type was investigated on average every 16 days on at least one elementary sampling unit (ESU). The data collection for potato and winter wheat was complemented with up to two additional ESUs on single field working day. All ESUs are designed as a square with a diagonal length of 24 m. On the diagonal from the most southwestern to the most northeastern corner of the ESU five secondary sampling points (SSPs) are distributed: at 0 m (SSP00), 8 m (SSP08), 12 m (SSP12), 16 m (SSP16), and 24 m (SSP24). The corner coordinates were determined with a differential Global Navigation Satellite System (dGNSS). A set of dGNSS recorded ground control points (GCPs) and check points (CPs) has been provided by Truckenbrodt & Baade (2017; doi:10.1594/PANGAEA.874247) and allows for a solid geo-referencing of satellite images depicting the agricultural Gebesee test site.
This dataset comprises data of an interdisciplinary pedon-scale irrigation experiment at a grassland site near Karlsruhe, Germany, including pedo-hydrological, geophysical, and remote sensing data. The objective of this experiment is to monitor soil moisture dynamics during a well-defined infiltration process with a combination of direct and non-invasive techniques.Overall, the quantification of soil water dynamics and, in particular, its spatial distributions is essential for the understanding of land-atmosphere interactions. However, the precise measurement of soil water dynamics and its spatial distribution in a continuous manner is a challenging task. Pedo-hydrological monitoring techniques rely on direct, point-based measurement with buried probes for soil water content and matric potential. Non-invasive remote sensing (RS) and geophysical measurement techniques allow for spatially continuous measurements on different spatial scales and extents. This experiment provides a basis for the analyses of signal coherence between the measurement techniques and disciplines. It contributes to forthcoming developments of monitoring setups and modeling approaches to landscape-water dynamics.For direct monitoring, an array of time-domain reflectometry (TDR) probes and tensiometers was used. As non-invasive techniques, we applied a ground-penetrating radar (GPR), a hyperspectral snapshot sensor, a long-wave infrared (LWIR) sensor, and a hyperspectral field spectroradiometer. We provide the data in nearly raw format, including information about the site properties and calibration references. The data are organized along with the different sensors and disciplines. Thus, the distinct sensor data can also be used independently of each other. In addition, exemplary scripts for reading and processing the data are included.