The AA. focused on : (1) using of the laboratory-based, proximally sensed in the visible-near-infrared (Vis-NIR, 400-2500 nm) spectral range to predict SOM content in the study area; (2) combining soil spectroscopy and geostatistics for mapping SOM
content; (3) mapping zones affected by water erosion processes in the study area; and (4) analyzing the relationship among soil erosion, SOM and soil spectral data. The study was performed in the Turbolo catchment, located in northern Calabria. Areas
were used as validation set. The optimum number of factors to retain in the calibration model was determined by cross validation. The results showed that zones with low content of SOM are affected by water erosion processes.
and drier climate. They evaluated the effect of fire on the chemical and physical characteristics of SOM along the altitudinal sequence. At 2 sites at a similar altitude but having a different recent fire history, they also examined the effect of fire
frequency on SOM. It is shown that fire is an important but not the only factor controlling SOM dynamics.
The AA. investigated the potential effect of permafrost thawing by the analysis of the physical-chemical soil properties of permafrost versus non-permafrost sites. Specifically, they 1) quantified the SOM stocks at such sites, 2) characterised SOM
was verified) were investigated in detail. It is confirmed that different decomposition processes occur between permafrost and non-permafrost sites. The results suggest that a warmer climate may not necessarily lead to an increased CO2 release from SOM
This study proposed a radial basis function neural networks model (RBFNN), combined with principal component analysis (PCA), to predict the spatial distribution of soil organic matter (SOM) content across China. To assess its feasibility, 6 241 soil
matter. The result suggests that the proposed method can play a vital role in improving prediction accuracy of SOM within a large area.