Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R² = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R² = 0.78, RPD = 2.19; two-step: R² = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R² = 0.77, RPD = 2.15; two-step: R² = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration.

The degradation of European soils is a cause for concern. Examples are the reduction of carbon content and soil fertility. The European Commission therefore recommends further research on how to better monitor soils and their changes over time and space. Digital soil mapping (DSM) is already an established method for the use of hyperspectral information from soil samples for quantifying soil properties under laboratory conditions based on soil spectral libraries. At the remote sensing level, imaging spectroscopy has already achieved good results for the prediction of soil properties on a local scale. Major advantages of this method are that topsoil maps can be updated more frequently, spatially more accurately and with less costs.In this study we bring together pedometric and remote sensing approaches to achieve the development of soil spectral models applicable to upcoming global hyperspectral imagery, combining DSM methods and data with Earth Observation expertise. In a first step at laboratory level, we used the EU-wide topsoil database LUCAS. We investigated whether using solely spectral data (without any covariates) and selected classification algorithms combined with PLSR could allow and improve the quantification of soil organic carbon (SOC) content. The best results were obtained for the local PLSR approach with RMSE=5.16 g kg-1, RPD=1.74 and R²=0.67. In addition, the local PLSR approach was tested with LUCAS spectral data resampled to EnMAP satellite spectral resolution, resulting in a very similar SOC prediction model accuracy.In the next step, the local PLSR approach was applied to airborne HySpex image data and simulated satellite EnMAP data from a test area in north-eastern Germany where local soil data are available for model validation. This area is associated with one LUCAS point. A direct application of the laboratory-based SOC model to the spectra of the airborne image was not possible due to higher variability in the image data caused by different environmental conditions (solar illumination, mixed soil-vegetation pixels, surface state -roughness, wetness-) and sensor performances different from the laboratory data resulting in an overall lower signal-to-noise ratio in the airborne image. Therefore, after reducing the effect of soil moisture, green vegetation cover, residues coverage, we used a two-step approach where (i) wet chemistry SOC analyses for a set of soil samples from the test area were replaced by the local PLSR approach using the LUCAS database. Then (ii) an airborne model was calibrated using the SOC content from (i) and the corresponding image spectra to calibrate an airborne PLSR. Preliminary results show a good airborne model accuracy for HySpex imagery with RMSE=3.33 g kg-1, RPD=1.59, R²=0.63 and slightly lower but still good accuracy for simulated EnMAP imagery with RMSE=3.72 g kg-1, RPD=1.45, R²=0.55. Both models were then applied to the images to produce SOC maps for bare soils and validated with existing data and previous SOC mapping works in the area based on local datasets. This approach demonstrates the possibility to replace wet chemistry by the local PLSR approach based on large scale soil spectral libraries for SOC mapping.

Monitoring of soil quality and its degradation is essential to face the big challenges of food security and climate change. One of the main soil parameters to observe is the content of soil organic carbon (SOC) which is linked to soil fertility and other ecosystem services. We investigate the potential of spaceborne hyperspectral images to estimate the content of SOC in the uppermost soil layer. Therefore, we use the spectral information of bare soil pixels in multiple PRISMA images together with chemically analyzed SOC contents of a range of local soil samples and a large-scale soil spectral library. The study site is located in the North-East of Germany within the long-term observatory of TERENO-NE and near the village of Demmin. We compare different machine learning and regression algorithms (Partial Least Squares Regression, Random Forest, Gaussian Process Regression, spectral SOC indices) for each of the images separately and for a synthetic multitemporal image. The best performing models are applied to all bare soil pixels to produce SOC content maps. The preliminary results show medium to high quality models for most cases. With an increasing number of hyperspectral satellites in orbit the outcomes of this case study can provide valuable information for future SOC mapping and monitoring.