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

作者：Mirzaeitalarposhti Reza; Demyan Michael Scott; Rasche Frank; Cadisch Georg; Mueller Torsten

来源：Catena, 2017, 149: 283-293.

DOI：10.1016/j.catena.2016.10.001

• Spatial variability
• midDRIFTS
• Geostatistics
• Organic carbon
• Kraichgau
• Swabian Alb

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 (similar to 3600 km(2)), 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) similar to 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.

• 出版日期2017-2