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Satellite-based cover management factor assessment for soil water erosion in the Alps
Author(s): Marco Gianinetto; Martina Aiello; Renata Vezzoli; Francesco Rota Nodari; Francesco Polinelli; Federico Frassy; Maria Cristina Rulli; Giovanni Ravazzani; Daniele Bocchiola; Andrea Soncini; Davide Danilo Chiarelli; Corrado Passera; Chiara Corbari
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Paper Abstract

Soil water erosion is one of the challenges that the European Union should deal with in the next years, due to its significant impacts on agriculture and natural hazards. In this work, a RUSLE (Revised Universal Soil Loss Equation)-like model has been applied to estimate soil water erosion in a Northern Italian Alpine basin (Val Camonica) by combining meteorological forcing with topography, soil properties and land cover. In the traditional formulation, land cover classes are assigned categorized cover management factor (Cfactor) value retrieved from existing literature (C-Land Cover formulation). However, Earth observation data have been proven effective in tuning the protective effect of vegetation on soil erosion dynamics. Thus, this method has been compared with two approaches (C-Satellite and C-Land Cover+Satellite) based on satellite-derived NDVI values to discretize C-factor values at a pixel scale. The C-Satellite formulation is based on an exponential law for correlating observed NDVI and C-factor values, irrespective of land cover classes. The C-Land Cover+Satellite method is based on the integration of land cover classification with NDVI maps. NDVI values have been retrieved from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI time series imaged from 2000 to 2017. Results of the application of the RUSLE-like proposed approach to estimate soil water erosion in an Italian alpine basin, have shown that integrating satellite-derived spectral information within the land-cover based C-factor estimate can generate a more reliable soil loss estimate related to seasonal and long-term land cover changes.

Paper Details

Date Published: 10 October 2018
PDF: 11 pages
Proc. SPIE 10783, Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 107830T (10 October 2018); doi: 10.1117/12.2325536
Show Author Affiliations
Marco Gianinetto, Politecnico di Milano (Italy)
Martina Aiello, Politecnico di Milano (Italy)
Renata Vezzoli, Politecnico di Milano (Italy)
Francesco Rota Nodari, Politecnico di Milano (Italy)
Francesco Polinelli, Politecnico di Milano (Italy)
Federico Frassy, Politecnico di Milano (Italy)
Maria Cristina Rulli, Politecnico di Milano (Italy)
Giovanni Ravazzani, Politecnico di Milano (Italy)
Daniele Bocchiola, Politecnico di Milano (Italy)
Andrea Soncini, Politecnico di Milano (Italy)
Davide Danilo Chiarelli, Politecnico di Milano (Italy)
Corrado Passera, Politecnico di Milano (Italy)
Chiara Corbari, Politecnico di Milano (Italy)


Published in SPIE Proceedings Vol. 10783:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XX
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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