Nature-Related Risks and Opportunities

The objective of Pilot 3 is to develop EO-based algorithms capable of generating spatial estimates of soil degradation caused by water erosion and to assess how these changes may affect agricultural productivity and financial risk to investors and banks.

The pilot builds on the Revised Universal Soil Loss Equation (RUSLE) modelling framework, a widely used empirical model for estimating soil erosion by water. RUSLE combines several environmental factors to estimate the annual rate of soil loss per unit area.

The modelling framework integrates multiple datasets, including information on:

• Rainfall erosivity, representing the intensity and erosive power of rainfall
• Soil erodibility, describing the susceptibility of soil to erosion
• Land cover and cropping systems, which influence soil protection
• Topography, including slope length and steepness
• Land management and conservation practices

EO plays a key role in providing spatial inputs for several of these parameters, particularly those related to land cover, vegetation dynamics and land management conditions. EO data are combined with ancillary datasets such as soil characteristics, historical land-cover information and weather estimates.

The result is a modelling framework capable of generating regionally consistent maps of soil erosion, expressed as annual estimates of soil loss per unit area. These outputs provide spatially explicit indicators of soil degradation that can be used to analyse environmental and economic risks.

The new soil erosion risk data will be incorporated into the Nature Related Value at Risk (NVaR) framework of the London School of Economics and Political Science and piloted as part of collaborations with the European Central Bank, Norges Bank Investment Management (NBIM) and Aviva. The pilot also engages with a much wider range of financial institutions, spanning asset management, banks and insurers.

Soil erosion has been identified as one of the major threats to global soil health and ecosystem services. Monitoring systems that quantify soil degradation can therefore support broader frameworks concerned with sustainable land management, agricultural productivity and environmental risk assessment.

By combining EO observations with established soil erosion modelling approaches, the pilot helps strengthen the evidence base needed to assess how environmental degradation may translate into physical risks for agricultural systems and associated supply chains, consistent with standards frameworks such as TNFD (Taskforce on Nature-Related Financial Disclosures) and regulatory frameworks such as the CSRD (Corporate Sustainability Reporting Directive).

The methodology also supports the development of spatial indicators that can be integrated into environmental monitoring systems and risk assessment frameworks used by governments, researchers and financial institutions.

Pilot 3 responds to the need for reliable and spatially consistent indicators of soil degradation that can support both environmental management and financial risk analysis.

Key user requirements include:

• Spatially explicit monitoring of soil degradation processes across large geographic areas
• Consistent time series to assess long-term trends in soil erosion
• Integration with agricultural statistics and land-use information
• Scientific transparency, ensuring modelling approaches are reproducible and well documented
• Compatibility with decision-support tools used in environmental management and financial risk analysis

These requirements guide the development of EO algorithms and modelling approaches that can be applied at regional scales while maintaining scientific credibility and operational feasibility.

The outputs of Pilot 3 provide spatial indicators of soil erosion and land degradation that can inform decision-making across agriculture, land management and financial risk assessment.

The erosion maps generated through the EO-supported modelling framework allow stakeholders to identify areas where soil degradation is likely to affect agricultural productivity. This information can support land management decisions, including the adoption of conservation practices and improved land-use planning, financial risk management and investment decisions.

By linking soil erosion indicators with agricultural statistics, the pilot also enables analysis of how soil degradation may influence crop yields and supply-chain stability. This creates an opportunity to explore how environmental degradation translates into nature-related financial risks for agricultural producers, commodity supply chains and financial institutions exposed to these sectors.