Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint

Abstract

ABSTRACT The reflectance anisotropy of land surface serves as an important bridge between surface biophysical parameters and remote sensing observations. It can characterize by the linear kernel-driven bidirectional reflectance distribution function (BRDF), which is the combination of several kernel functions and kernel weights. These kernel weights can be estimated by remote sensing; however, the stability of current kernel weights products is still challenging, especially in urban areas with complex aerosol properties and heterogeneous surfaces. In this paper, we propose a method for robust estimation of kernel weights from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface spectral reflectance products (MxD09GA) data based on the constrained least-squares method (CLSM) and a priori knowledge. The kernel weights data were obtained by the CLSM from 2014 to 2017 in Beijing region of China. Validations were carried out using the MxD09GA and BRDF/Albedo products (MCD43A1). The results show that the time series of kernel weights by the CLSM show small variability over different land cover types. The kernel weights estimated by the CLSM can clearly show the phenological signal and fitting ability of surface spectral reflectance is better than that of the MCD43A1 products in Beijing urban area. Experimental results demonstrate that the CLSM has the potential for the robust estimation of kernel weights in urban areas.