BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

M Matsuoka,A Nonomura,H Moriya,M Takagi,S Akatsuka,H Yoshioka,R Honda

doi:10.5194/isprsannals-iii-7-3-2016

Abstract

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

Highlights

The surface reflectance of the earth observed by a remote sensor varies depending on the target itself and on the position of the sun and sensor relative to the target; it is called bidirectional reflectance
We investigated the performance of six combinations of bidirectional reflectance distribution function (BRDF) kernels
A kernel-driven BRDF model was applied to an 8-hour reflectance time series from Himawari-8/Advanced Himawari Imager (AHI) to model intraday changes in bidirectional reflectance

Summary

Introduction

The surface reflectance of the earth observed by a remote sensor varies depending on the target itself and on the position of the sun and sensor relative to the target; it is called bidirectional reflectance. This sometime causes problems in remote-sensing applications for land cover classification and change detection because the same target has different reflectance values, or different land surfaces produce the same signal. Gao et al (2003) investigated the relations of vegetation structure, which is derived from BRDF model parameters, and land surface type. BRDF is a crucial factor in the accurate derivation of land surface albedo, since it is derived by directional integration of the reflectance (Lucht et al, 2000, Pokrovsky et al, 2002, Pokrovsky et al, 2003). Gao et al (2003) investigated the relations of vegetation structure, which is derived from BRDF model parameters, and land surface type. Tang et al (2007) developed a leaf area index (LAI) retrieval method by combining the BRDF model and the normalized difference vegetation index (NDVI)

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