Soil erosion by water is the main cause of soil degradation in large areas of the Mediterranean belt. Soil erosiondetermines loss of surface horizon, which is rich in organic matter. The content of soil organic matter (SOM) is akey property for evaluating soil erosion and/or soil preservation and quality.Conventional methods to estimate quantitatively SOM content, based on conventional laboratory analyses,are costly and time consuming. An alternative approach to ascertain SOM content is based on the use ofsoil spectral reflectance, which has the advantage to be rapid, non-destructive and cost effective.In this study we focused on: (i) 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; (ii) combining soil spectroscopyand geostatistics for mapping SOM content; (iii) mapping zones affected by water erosion processesin the study area; and (iv) analyzing the relationship among soil erosion, SOM and soil spectral data.Areas affected by water erosion processes (sheet wash and/or rill and gully erosions) in the study area weredetected through air-photo interpretation and field surveys. Topsoil samples from 215 locations in differentsoil types and erosion conditions were collected and each sample was air-dried and sieved at 2 mm and thensplit into two sub-samples: one was used for spectral measurements, while the other was analyzed to determineSOM content. Analysis of spectral curve showed that topsoil samples were spectrally separable on thebasis of SOM content and of their erosion severity. Partial least squared regression (PLSR) analysis wasapplied to establish the relationships between spectral reflectance and SOM content. PLSR was performedon the calibration set including 161 of the 215 available samples, while 54 samples were used as validationset. The optimum number of factors to retain in the calibration model was determined by cross validation.The models were independently validated using the 54 validation soil samples. The results were satisfactorywith high adjusted coefficient of determination (Radj2 = 0.84) and with a value of residual predictive deviation(RPD) more than 2.4. The results of this work suggest that laboratory reflectance spectroscopy in theVis–NIR range coupled with a geostatistical analysis can be used as tools for predicting spectrally and mappingSOM. The relationship between water erosion processes and the spatial distribution of SOM, showedthat: (i) zones with low content of SOM are affected by water erosion processes and (ii) water erosion affectsmore than 21% of the study area.
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