Mid-infrared spectroscopy to support regional-scale digital soil mapping on selected croplands of South-West Germany

Digital soil mapping (DSM) relies on statistical relationships between soil properties and covariates (e.g. terrain attributes, land use class, geology) which may not explain a large proportion of measured soil properties variability. This uncertainty combined with low spatial resolution of existing maps makes it difficult to monitor soils. Diffuse reflectance infrared Fourier transform mid-infrared spectroscopy (midDRIFTS) with partial least square regression (PLSR) may offer an alternative source of high quality data for improved DSM. Previously validated midDRIFTS-PLSR models were used to predict soil total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC) and texture of 1170 samples from contrasting agroecological regions (~ 3600 km2), Kraichgau (K) and Swabian Alb (SA), Southwest Germany. MidDRIFTS-PLSR predictions were integrated with geostatistics for soil property maps (200 m resolution). An average of 93% of the total soil samples were predicted within the confidence intervals of the midDRIFTS-PLSR models for the respective properties. Ordinary kriging (OK) resulted in maps for TC, TIC, TOC and texture with a root mean square standardized error (RMSSE) ~ 1. Soil organic matter (SOM) (DRIFTS_SOM) and texture (DRIFTS_texture) maps developed in the current study were of higher spatial resolution than previously existing maps. The DRIFTS_SOM and DRIFTS_texture in the both regions showed considerable differences when compared to existing maps. The DRIFTS_SOM in K and SA regions had overlaps of 45 and 69% with the 1:200,000 existing map. While the DRIFTS_texture in K had an overlap of 92% with the 1:1,000,000 existing map, the overlap in SA region was only 11%. We conclude that traditional DSM with covariates can be improved via higher sampling density which is made possible using midDRIFTS-PLSR. Incorporation of mid-infrared spectral data with both remote sensing and other environmental data would be a further application to cope with uncertainty associated to both spectroscopic and spatial modeling.