Integrating Landsat and Sentinel-2 time series for enhancing the estimation of soil erosion in the Alps

Soil erosion is a naturally dynamic phenomenon, but human activities, human-induced forces and climate change have further accelerated this process. As a consequence, the mean annual soil loss rate in Europe exceeds the average soil formation rate. That means soil can be actually considered as a non-renewable resource. Therefore, understanding spatial patterns and temporal trends of soil loss could support government land use policies and strategies for reducing this overlooked natural hazard. Among the factors driving soil erosion dynamics, meteorological forcings and land cover/land use vary in time and space, while other factors like soil properties (i.e. their pedology) and topography can be considered constant over time. Soil erosion can be reduced acting on land cover/land use, but, this factor is the most complex and expensive to be continuatively monitored by national or regional agencies through field campaign or ad hoc surveys. However, satellites for Earth Observation can help a lot in monitoring spatial and temporal land cover changes and the cover management factor can be effectively estimated through spectral indices (e.g. NDVI). This paper describes an ad hoc implementation of the Revised Universal Soil Loss Equation (RUSLE) model to provide dynamic maps of soil erosion. In this work, we evaluate the benefits of integrating Landsat and Sentinel-2 time series to study soil erosion in an alpine river basin of Italy. Thus, reducing the revisit time up to 5 days allows to consider the effects on soil erosion of land use/land cover modification caused by extreme meteorological events, that otherwise would be missed if estimating soil erosion through institutional product of land use/land cover. Besides, the high revisit time provided by the combination of the two sensors could provide snow cover maps of the study area, useful for quantifying the contribution to erosion of soil covered by snow. Results demonstrate that the ESA’ Sentinel-2 twin satellites could effectively enhance the estimate of the cover management factor, both spatially and temporally