<p>Soil is one of the world&#8217;s most important natural resources for human livelihood as it provides food and clean water. Therefore, its preservation is of huge importance. For this purpose, a proficient regional database on soil properties is needed. The project &#8220;ReCharBo&#8221; (Regional Characterisation of Soil Properties) has the objective to combine remote sensing, geophysical and pedological methods to determine soil characteristics on a regional scale. Its aim is to characterise soils non-invasive, time and cost efficient and with a minimal number of soil samples to calibrate the measurements. Konen et al. (2021) give detailed information on the research concept and first field results in a presentation in the session &#8220;SSS10.3 Digital Soil Mapping and Assessment&#8221;. Hyperspectral remote sensing is a powerful and well known technique to characterise near surface soil properties. Depending on the sensor technology and the data quality, a wide variety of soil properties can be derived with remotely sensed data (Chabrillat et al. 2019, Stenberg et al. 2010). The project aims to investigate the effects of up and downscaling, namely which detail of information is preserved on a regional scale and how a change in scales affects the analysis algorithms and the possibility to retrieve valid soil parameter information. Thus, e.g. laboratory and field spectroscopy are applied to gain information of samples and fieldspots, respectively. Various UAV-based sensors, e.g. thermal & hyperspectral sensors, are applied to study soil properties of arable land in different study areas at field scale. Finally, airborne (helicopter) hyperspectral data will cover the regional scale. Additionally forthcoming spaceborne hyperspectral satellite data (e.g. Prisma, EnMAP, Sentinel-CHIME) are a promising outlook to gain detailed regional soil information. In this context it will be discussed how the multisensor data acquisition is best managed to optimise soil parameter retrieval. Sensor specific properties regarding time and date of acquisition as well as weather/atmospheric conditions are outlined. The presentation addresses and discusses the impact of a multisensor and multiscale remote sensing data collection regarding the results on soil parameter retrieval.</p><p>&#160;</p><p>References</p><p>Chabrillat, S., Ben-Dor, E. Cierniewski, J., Gomez, C., Schmid, T. & van Wesemael, B. (2019): Imaging Spectroscopy for Soil Mapping and Monitoring. Surveys in Geophysics 40:361&#8211;399. https://doi.org/10.1007/s10712-019-09524-0</p><p>Stenberg, B., Viscarra Rossel, R. A., Mounem Mouazen, A. & Wetterlind, J. (2010): Visible and Near Infrared Spectroscopy in Soil Science. In: Donald L. Sparks (editor): Advances in Agronomy. Vol. 107. Academic Press:163-215. http://dx.doi.org/10.1016/S0065-2113(10)07005-7</p>
Generalized Signal Models and Direct FID-Based Dielectric Parameter Retrieval in MRI
