A visualization tool for the kernel-driven model with improved ability in data analysis and kernel assessment. (October 2016) - Dong, Yadong
Abstract: The semi-empirical, kernel-driven Bidirectional Reflectance Distribution Function (BRDF) model has been widely used for many aspects of remote sensing. With the development of the kernel-driven model, there is a need to further assess the performance of newly developed kernels. The use of visualization tools can facilitate the analysis of model results and the assessment of newly developed kernels. However, the current version of the kernel-driven model does not contain a visualization function. In this study, a user-friendly visualization tool, named MaKeMAT, was developed specifically for the kernel-driven model. The POLDER-3 and CAR BRDF datasets were used to demonstrate the applicability of MaKeMAT. The visualization of inputted multi-angle measurements enhances understanding of multi-angle measurements and allows the choice of measurements with good representativeness. The visualization of modeling results facilitates the assessment of newly developed kernels. The study shows that the visualization tool MaKeMAT can promote the widespread application of the kernel-driven model. Highlights: We present a visualization tool for the kernel-driven model. We utilize the visualization tool to assess the newly developed RossthickMaignan and RossthickChen kernels using the POLDER-3 and CAR BRDF datasets. The RossthickChen–LiSparseR kernel combination is the best-fitting model if information corresponding to the hotspot is desired.