A Comparison of Kernel-Driven BRDF Parameters Between AHI and VIIRS for Surface Reflectance Characterization Masayuki Matsuoka (a*), Hiroki Yoshioka (b), Kazuhito Ichii (c)
a) Department of Information Engineering, Mie University
1577 Kurima-machiya, Tsu, 514-8507 Japan
* matsuoka[at]info.mie-u.ac.jp
b) Department of Information Science & Technology, Aichi Prefectural University
1522-3 Ibaragabasama, Nagakute, Aichi, 480-1198 Japan
c) Center for Environmental Remote Sensing, Chiba University
1-33 Yayoi, Inage, Chiba, 263-8522 Japan
Abstract
Geostationary Earth orbit (GEO) satellites provide Earth observation data with a temporal resolution of several minutes throughout the day and night from a fixed position. In contrast, low Earth orbit (LEO) satellites with wide-swath sensors observe nearly the same time of day, but from different viewing angles. A bidirectional reflectance distribution function (BRDF) is a mathematical model that represents the relationship between spectral reflectance and the geometry of the sun, target, and sensor. Comparing BRDF model parameters helps integrate GEO and LEO data to characterize land surfaces. Of particular interest is whether the same parameters can represent the reflectance of both types of sensors. This study compared the kernel-driven BRDF model parameters of the Advanced Himawari Imager (AHI) sensor on a GEO satellite and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on a LEO satellite. The target area was Japan. The AHI was analyzed using six hours of diurnal time series data (145 scenes). These data were corrected for the atmospheric effects and reprojected to a latitude-longitude projection. For VIIRS, the ^BRDF/Albedo Model Parameters^ products were used for both the National Polar-orbiting Partnership (Suomi-NPP) and the Joint Polar Satellite System 1 (JPSS-1). Comparing three BRDF parameters (fiso, fvol, and fgeo) revealed a remarkable terrain-dependent pattern in the AHI data. This pattern was caused by shadows resulting from the sun^s movement throughout the day. VIIRS showed a clear dependence on land cover. The different features of the GEO-LEO BRDF model parameters provide useful information for characterizing land surfaces, such as albedo and FPAR. This study also helps to develop an accurate BRDF model by integrating reflectance observed under different observation geometries.
