A Strategy to Estimate High-Resolution Albedo Radiative Perturbations and Kernels of both Surface and TOA from Satellite Observations

Surface albedo variations exert an important influence on the Earth’s radiation budget, yet their long-term radiative impacts remain difficult to quantify at high spatial resolution using conventional radiative kernel techniques and partial radiative perturbation approaches. To address this limitation, this study presents a flexible framework that integrates high-resolution satellite observations with a precomputed look-up table to efficiently estimate radiative perturbations and associated radiative kernels induced by surface albedo changes at both the surface and the top-of-atmosphere (TOA). The framework enables rapid computation of radiative flux responses under varying atmospheric and surface conditions without relying on a fixed atmospheric base state. Validation against radiative transfer simulations shows excellent agreement, with a coefficient of determination ( R 2 ) of 0.99 and a relative bias below 3%. The estimated kernels remain robust under noisy input conditions and consistent across a wide range of atmospheric states and surface types. A case study using Himawari-8 observations over the Tibetan Plateau (TP) demonstrates strong spatial agreement with existing kernel datasets while resolving finer heterogeneity in complex terrain at 0.05° resolution. This framework provides a practical and scalable approach for generating long-term satellite-derived radiative kernel datasets at both TOA and surface levels and enables improved observation-based quantification of regional climate feedbacks.