UC5 focuses on five administrative regions in southern Italy, an area experiencing among the highest levels of soil loss by water in Europe and crossed by several transport infrastructures that alter soil dynamics and might be posed at risk by erosion. Accordingly, UC5 involves stakeholders and potential users from the land management and infrastructure sectors. 

UC5 primarily relies on data from Copernicus services and space components, ensuring maximum consistency and homogeneity. Building on an existing pilot within the Copernicus Climate Change Service, CMCC developed an updated, high-resolution, and cutting-edge service for evaluating soil erosion thus unveiling the potential of the most updated Earth Observation (EO) data, and the tools provided by the EO4EU platform. UC5 provides soil erosion assessments in current and alternative scenarios, including future precipitation projections and transport infrastructure. The estimates are carried out at a sub-annual temporal scale, allowing users to properly consider the influence of plant phenology and cropland and forest management practices on soil loss. These features make the tool developed within UC5 valuable for land planning and management.

Challenge

Soil erosion by water occurs when soil particles are detached, transported, and deposited away due to rainfall, runoff, snow melting, or irrigation. When the soil erosion rate is higher than the soil formation rate, the soil becomes depleted and the potential of the land to be used productively is reduced. The direct and indirect economic costs of soil erosion are extremely high, as a variety of sectors are negatively impacted. Soil erosion may lead to a decrease in the yield of agricultural areas, physical damage to cultivated fields, and deterioration of water and air quality due to suspended soil particles. As a consequence, reducing soil erosion can provide benefits for several ecosystem services and improve overall socio-economic conditions. For these reasons, soil erosion has raised great attention at the EU level, and soil erosion-related indicators have started to be included in policies to track progress towards sustainability goals. However, such indicators need large-scale, repeatable, high-frequency assessments, which are not easily carried out due to methodological inconsistency and data scarcity.

Solution

Through the developed service, UC5 provides modules for assessing soil loss by water through a repeatable and transparent approach applied to fine spatiotemporal data. These modules, implemented in the EO4EU platform, allow users to explore different precipitation scenarios, account for land cover variability over the year, and evaluate the impact on/of transport infrastructures.
UC5 can support increasing awareness of soil erosion patterns and severity. It can assist decision-makers, like land management actors and territorial planners, in assessing the ecological and socio-economic impact of soil erosion and evaluating mitigation strategies.
Finally, UC5 can support climate-informed design and management of infrastructures, ensuring a more robust evaluation of hazards and risks posed by water-induced soil erosion. Soil erosion estimates for future horizons, provided by UC5 building on the most updated and fine-resolution climate projections, allow for the design of more resilient infrastructures, as well as an appropriate and climate-informed allocation of investments for maintenance, update, and retrofitting.

Current status

A preliminary, embryonic-stage service for the evaluation of water-induced soil erosion in Italy is already available as a Demo Case (dataset and applications) on the Climate Data Store of the Copernicus Climate Change Service. However, it relies on state-of-the-art and less region-specific empirical methods and data, especially for the quantification of rainfall erosivity, and on low-resolution datasets for the evaluation of soil- related features.

The proposed UC provides significant enhancements in the robustness of soil erosion estimates, using novel, cutting-edge approaches, tools, and data for all the key parameters of the erosion process. 

According to the adopted Revised Universal Soil Loss Equation (RUSLE), the potential soil loss due to erosion depends on rainfall erosivity and soil susceptibility. The EO4EU platform provides rainfall erosivity datasets on current and projected precipitation scenarios computed through an empirical model recently calibrated across Italy. Soil susceptibility to erosion is computed considering three different aspects. First, two different datasets are available on the EO4EU platform regarding the influence of morphological features, such as the terrain slope. The first dataset considers only terrain morphology and was computed by implementing a state-of-the-art workflow, while the second innovatively accounted also for the effects of transport infrastructure in interrupting sediment movement. Second, a dataset representing the influence of soil textural and chemical properties is also available, computed by applying consolidated literature empirical models to the most recent gridded soil property datasets. Third, the EO4EU platform offers a cutting-edge algorithm to consider the influence of land cover and plant phenological dynamics in inhibiting or reducing erosion. This algorithm is based on an artificial neural network trained on Earth Observation multispectral images and a state-of-the-art lookup table to assign spatially distributed erosion correction factors depending on dynamical cover properties. A prototype of this algorithm is already implemented in the EO4EU platform, while a more robust version will be deployed soon.

Sentinel-2 multispectral images underlie the determination of land cover and management impact on soil erosion. All available 10 and 20 m bands are used to train an artificial neural network algorithm accounting for diverse land cover spectral response in different cover type and phase of the year.

Example: land cover and management (RUSLE C-factor) computation
The user can select an area and period of interest in the EO4EU platform to retrieve Sentinel-2 multispectral image bands. Then, the user can draw the workflow to merge the bands and run the trained artificial neural network algorithm, which will yield C-factor values all over the selected area. The user can visualize the result as a color map. Finally, the user can combine the C-factor with other relevant factors of the RUSLE model to obtain an assessment of soil loss by water.