Frank Waldmann’s scientific contributions

Detailed soil information is a valuable resource for various approaches like agricultural management, soil protection, or spatial planning. Preserving, using and enriching soils are complex processes that fundamentally need a sound regional database. Many countries lack this sort of extensive data or the existing data must be urgently updated when land use changes in major patterns. The projects "ReCharBo" (Regional Characterization of Soil Properties) and “BopaBW” (Soil Parameter Baden-Wuerttemberg) aim at the combination of methods from remote sensing, geophysics, pedology, and digital soil maps, in order to develop a new system to map soils on a regional scale in a quick and efficient manner. This system could be very useful especially in countries where comprehensive harmonized soil data are scarce. First tests are performed in existing soil monitoring districts and on existing and newly developed soil databases, using newly available sensing systems as well as established techniques. High resolution digital soil maps do exist in Germany, as for many countries basic soil data and soil maps in even medium resolution are lacking, however, more sharpness of detail and a higher data density and monitoring enables more precise planning. Therefore, the integration of remote sensing products is a powerful source for spatial mapping of near-surface soil parameters (NSSP). Among such NSSP are sand, silt, clay contents, soil density, soil moisture, and soil organic carbon, presence of carbonates, and surface-stone cover. Sentinel-2 remote sensing data are combined as a multispectral complementary data source with existing digital soil maps, and especially hyperspectral data measured from satellites or airborne platforms are systematically correlated with gamma-ray spectroscopy. The results may demonstrate that the combination of traditional field surveys with remote sensing data can lead to a quick and comprehensive understanding of soil properties and their regional interactions. Furthermore, the integration of remote sensing data in soil maps enables to keep soil maps up-to-date and integrate new information cost-effectively and timesaving. The goal is to generate a system that enables users to map soil parameters and patterns on a local to regional scale using remote sensing data and to calibrate the data with only a limited number of soil samples.

So far the most state-of-the-art soil map (soil map 1:50'000) of the German federal state Baden-Wuerttemberg is based on conventional data sources as soil surveys, forest mapping, geological and topographical maps, and digital elevation models. However, the integration of remote sensing products as a complementary data source has attained little attention over the years due to various reasons (e.g., high costs for commercial products, low spatial and/ or temporal resolution). Nowadays with Sentinel-2 data, obtained within the Copernicus Mission, new multispectral images of the earth at a relatively high spatial resolution (up to 10 m) and a repetition rate of 5 days are made available free of charge. As such, Sentinel-2 data can be used as an additional database that delivers continuous information about the soil surface since 2015. The aim is to elaborate the relationships of near-surface soil parameter (NSSP) and their spectral characteristics by fusing the multispectral Sentinel-2 data with analyzed soil samples (n=164) of arable lands in Baden-Wuerttemberg and hyperspectral field and laboratory spectrometer measurements. The soil sampling network is relatively dense with a mean distribution of 5.7 km x 5.7 km on arable land. Such NSSP are the contents of sand, silt, clay, organic carbon, and the presence of carbonates. A correlation of measured NSSP and spectral characteristics in the spectrum of Sentinel-2 is to be expected. Based on the identified relationships of NSSP and spectral signatures, a proposed regression equation should be applicable to the total arable land within Sentinel-2 scenes to model the spatial distribution of NSSP. Mixed pixels first need to be unmixed to provide a clearer and unambiguous spectral signal. Additionally, the high repetition rate of Sentinel-2 enables to capture the influence of temporal dynamics as soil moisture on the reflectance and spectral signatures on NSSP. That information facilitates the interpretation of spectra regarding the overruling effect of water saturated NSSP that reduce the actual soil parameter signal. Other soil data like the Land Use/Cover Area frame statistical Survey (LUCAS) can serve as an independent dataset to validate the derived NSSP of Sentinel-2. The objective is to integrate the obtained additional information about NSSP into the existing soil map of Baden-Wuerttemberg. The results may demonstrate that the combination of traditional field surveys with remote sensing data can lead to a better understanding of soil properties and their interactions. Furthermore, the future integration of remote sensing data in soil maps enables to keep soil maps up-to-date and integrate additional information cost-effectively and time-saving.

The Department 93 of the State Authority for Geology, Raw Materials and Mining (LGRB) Baden-Württemberg has the task of carrying out the systematic soil survey in Baden-Württemberg, Germany's third-largest state in southwest Germany. The results are used, for example, in the implementation of soil protection concerns in planning (regional planning, urban land-use planning and large-scale projects such as long-distance traffic routes and power lines), in water management (water framework regulations, discharge modelling), in agriculture (cross-compliance erosion, disadvantaged areas) and in nature conservation (biotope network). In current work processes, the information source satellite-based earth observation has only been used sporadically. Nowadays free high quality multispectral imagery is available from space such as from the twin Sentinel-2 (S2) sensors providing wide coverage, minimum five-day global revisit-time at 10 to 20 m spatial scale, and improved spectral characteristics than previous multispectral satellites. The capabilities of the S2 sensors for soil assessment (soil organic carbon, soil texture) were recently demonstrated in local areas using ground databases for calibration (Gholizadeh et al., Castaldi et al., 2018). In this study, we evaluate the potential of satellite data of the Copernicus Mission for the assessment of area-wide, high-resolution, near-surface soil parameters (e.g. organic carbon content, clay content, soil moisture, stone content, soil roughness) for the Baden-Württemberg region in southeastern Germany. In particular, this work is based on the collaborative project BopaBW supported by the Copernicus-Services program in Germany and lead by the LGRB, Freiburg. BopaBW aims at the development of a data processing concept for the derivation of additional soil information for soil maps from Copernicus satellite imagery. The aim is to elaborate the relationships of near-surface soil parameter and their spectral characteristics by fusing the multispectral S2 data with analyzed soil samples of arable and vine-growing areas in Baden-Wuerttemberg and hyperspectral field and laboratory spectrometer measurements. For calibration and validation, extensive ground truth data from soil surveys including chemical and physical soil properties and hyperspectral field measurements are available. One of the most important cornerstones in the use of Copernicus earth observation data is the operational and long-term availability of physically homogeneous, high-quality data. The requirements on the products for the integration into the official LGRB tasks have to be defined with regard to temporal and spatial availability, quality standards as well as processing levels for the creation of the data processing concept. The technical implementation of the overall concept is carried out during the project in an iterative process.

Basierend auf den Satellitendaten der Copernicus Mission werden für Baden-Württemberg flächendeckende, hochaufgelöste, oberflächennahe Bodenparameter (z.B. organischer Kohlenstoffgehalt, Tongehalte, Bodenfeuchte, Steingehalte, Bodenrauhigkeiten) sowie die Änderungen der Geländehöhe der Moorflächen ermittelt.