Worldwide performance evaluation of ground surface reflectance models

To improve the accuracy of bifacial gain estimation, recent radiative models of solar energy systems have abandoned the traditional assumption of isotropic ground-reflected radiance. However, surface reflectance itself is still commonly considered as a constant — partly because of the recommendations of previous evaluations of reflectance models. This paper presents the findings of a new model evaluation based on a large database of measurements from 26 sites, which are representative of major land covers and climates. Both novel and previously reviewed formulations are validated with the data. On a global average, data-based estimation reduces mean absolute error by 22%, 29%, and 39% with constant, univariate, and bivariate models, respectively, compared to literature-based estimates. Only at the urban and snow-and-ice sites does time-variant estimation not notably improve accuracy. Arid sites tend to favour univariate models based on solar elevation, and diffuse fraction adds little value as the second predictor. By contrast, bivariate estimation clearly improves accuracy at vegetated and water sites. When considering the best-performing model for each site, the global average mean absolute error is 11%. Two novel formulations, univariate and bivariate, provide superior performance at many sites. The proposed 3-parameter bivariate model is one of the top performers at 19 out of the 26 considered sites.