High spatiotemporal resolution vegetation FAPAR estimation from Sentinel-2 based on the spectral invariant theory

Abstract

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key input parameter that drives photosynthesis in terrestrial ecosystem models. It plays an important role in estimating canopy gross primary production and, consequently, the regional terrestrial carbon sink. The growing focus on regional responses to global climate change has increased the demand for FAPAR with high spatiotemporal resolution across spatial heterogeneous landscapes. However, instantaneous FAPAR values from satellites are insufficient for monitoring FAPAR throughout the day under varying sky conditions, given that cloud disturbances pose a significant challenge to the generation of high spatiotemporal resolution FAPAR. We proposed a FAPAR-Pro model based on spectral invariant theory to address this challenge. This model distinguishes simulations under direct and diffuse radiation to suit clear and cloudy conditions. The FAPAR-Pro model was validated across various vegetation types and sky conditions. The model was also compared with the FAPAR-P model and the SAIL model, where it exhibited robust performance (R² = 0.875, RMSE = 0.065, and bias = −0.004). Consequently, an hourly FAPAR estimation algorithm based on the FAPAR-Pro model (HFP) was developed to derive hourly FAPAR at high spatial resolution. It incorporates daily leaf area index retrieved and reconstructed from Sentinel-2 data, the hourly ratio of diffuse radiation retrieved from Himawari-8, and the leaf single scattering albedo and the soil reflectance derived from Sentinel-2 data using the general spectral vector-leaf (GSV-L) model and the general spectral vector (GSV) model, respectively. The resulting estimations closely matched the hourly ground measurements at Huailai station under diverse sky conditions (R² = 0.828, RMSE = 0.070, and bias = −0.011). Furthermore, a set of spatially continuous FAPAR data at the 20 m resolution was generated at the Saihanba area in China in 2022. By contrast, FAPAR estimations from the Sentinel-2 Toolbox and MODIS were significantly affected by cloudy conditions or coarse resolution. Overall, the proposed HFP algorithm can provide blue-sky FAPAR values at high spatiotemporal resolution regardless of various sky conditions. This advancement offers great potential for ecological models and numerous other applications.