Digital soil classification and elemental mapping spatially explicit quantification of chemical hot spot characterisation byusing imaging Vis-NIR-SWIR spectroscopy: How to explicitly quantify stagnic properties of a Luvisol under Norway spruce

Kurzfassung

Imaging Vis-NIR-SWIR spectroscopy is a novel technique in soil science that can determine quantity and quality of various chemical soil properties with a hitherto unreached spatial resolution in undisturbed soil profiles. We have applied this technique to soil cores in order to get quantitative proof of redoximorphic processes under two different tree species and to proof tree-soil interactions at microscale. Due to the imaging capabilities of this technique Vis-NIR -spectroscopy a spatially explicit understanding of soil processes and properties can be achieved.. Strong sSpatial heterogeneity of the soil coreprofile can be taken into account. We took six 30 cm -long rectangular soil columns of adjacent, stagnic Luvisols derived from quaternary aeolian sediments (Loess) in a forest soil near Freising/Bavaria using stainless steel boxes (100×100×300 mm). Three profiles were sampled under Norway spruce and three under European beech. A Two A hyperspectral cameras (VNIR, 400–1000 nm in 160 spectral bands and SWIR, 1000-2500 nm in 256 spectral bands) with spatial spatial resolutionresolution ground sampling distances of 63×63 µm² per pixel and 256x256 µm2 was were was used for data acquisition. Reference samples were taken at representative spots and analysed for SOM organic carbon (OC) quantity and quality with a CN elemental analyser and for mineralogy iron oxides (Fe) content using dithionite extraction followed by ICP-OES measurement. We have applied compared two supervised classification algorithms i.e. Spectral Angle Mapper and Maximum Likelihood using different sets of training areas and spectral libraries. . Accuracy assessment has been made by error matrix and Cohen´s kappa analysis. As established in chemometrics we used multivariate analysis such as partial least-squares regression (PLSR) in addition to multivariate adaptive regression splines (MARS) to correlate chemical data with Vis-NIR- spectra. As a result elemental mapping of Fe and OC within the soil core at high spatial resolution has been achieved. The regression model was validated by a new set of reference samples for chemical analysis. Digital soil classification easily visualizes soil properties within the coresoil profiles. By combining both techniques detailed soil maps, material elemental balances and a deeper understanding of soil forming processes at the microscale and detailed soil mapping and characterization become feasible for complete soil profiles.