CISAR

CISAR (Combined Inversion of Surface and AeRosol) is an advanced mathematical algorithm developed by Rayference that jointly retrieves surface reflectance, aerosol optical thickness, and cloud single-scattering properties from observations acquired by passive space-based and drone-borne imagers operating in the visible and near-infrared.

Built on the inversion of the FASTRE fast radiative transfer model using an Optimal Estimation framework, it provides pixel-level uncertainty propagation without requiring pre-computed look-up tables or an external cloud mask. The algorithm is sensor-agnostic — applicable to any passive optical sensor acquiring at least one observation every 1–2 days — and has been operationally applied to Sentinel-3A/SLSTR and PROBA-V data within ESA projects (Aerosol_cci+, PV-LAC ATMO).

Key capabilities:

• Joint Surface & Atmospheric Retrieval: Simultaneously retrieves surface reflectance parameters (RPV model), aerosol optical thickness, and aerosol/cloud single-scattering properties as Essential Climate Variables.
• Rigorous Uncertainty Quantification: Optimal estimation inversion delivers per-pixel retrieval uncertainties propagated from both input and inversion-related error sources.
• Sensor & Surface Agnostic: Works over any surface type (land, water, bright or dark surfaces) and has been extended to drone measurements for field-scale surface reflectance retrieval.
• Solar Resource Assessment Integration: Retrievals feed into 3D scene simulation pipelines to improve solar irradiance estimation for complex photovoltaic applications (e.g., SATRISE project).
• Project & Consultancy Delivery: Deployed alongside technical support and integration into ESA and industry projects, with outputs tailored to customer workflows in climate monitoring, atmospheric correction, and energy applications.