Semi-empirical models for polarized reflectance of land surfaces: Intercomparison using space-borne POLDER measurements

- Six semi-empirical polarized reflectance models of land surfaces are intercompared.
- High-quality database of surface polarized reflectance generated from POLDER is used.
- Nadal-Bréon model and Litvinov model provide best fits to the POLDER measurements.
- Xie-Cheng model performs best for priori modeling of surface polarization.
- Impact of negative polarized reflectance on fitting and priori modeling is negligible.

In this paper, six semi-empirical bidirectional polarization distribution functions (BPDF) models for polarized reflectance of land surfaces (Nadal-Bréon model, Waquet model, Maignan model, Litvinov model, Diner model and Xie-Cheng model) were quantitatively intercompared using the recently released database of representative BPDFs generated from POLDER measurements over a wide range of surface types at global scale. The intercomparison technique involved two strategies: one for fitting and the other for priori modeling. Our results suggest that (1) Nadal-Bréon model and Litvinov model provide best fits to the POLDER measurements with average RMSEs equal to 0.174% and 0.173%, respectively; (2) as for priori modeling, Xie-Cheng model performs best among these models as its average RMSE is 0.249%, indicating the corresponding surface-type-based free parameters can be used for a priori model of surface polarized reflectance; (3) despite the semi-empirical models cannot estimate negative polarized reflectance at backward scattering direction, impact of negative polarized reflectance on fitting and priori modeling is negligible. The results provide a priori knowledge of the model performances over various surface types and can be applied to future researches of land optical properties and aerosol parameters retrieval over land surfaces.