Sensitivity Analysis of Net Primary Production Estimation using a Semi-empirical BRDF Model and Reflectance Observed by RC Helicopter for Japanese Cedar Forest

Abstract

Among the existing global observing sensors, such as Terra/MODIS, NOAA/AVHRR, ADEOS-II/ GLI, with a large field of view, there is a need to take Bidirectional Reflectance Distribution Function (BRDF) effect into account when using data of these sensors to analyze. For estimating the global NPP (Net Primary Production) from the spectral data of global observing sensors, and improving the accuracy of this estimation method simultaneously, it is necessary to know how this method was affected by BRDF effect, especially in the sensor's observation conditions. Using a Japanese cedar forest as the objective and a semi-empirical kernel-driven BRDF model (the RossThick-LiSparse model), this study analyzed BRDF effects on NPP estimation. The BRDF data of the forest used in this study were measured by a sensor onboard a radio-controlled helicopter with bidirectional reflectance factors (BRF) observations in July 2002. After validating the application of Ross-Li BRDF model to Japanese cedar forest, parameters of this model for the Japanese cedar forest were obtained, and used to retrieve reflectance for the nadir view and nadir illumination. With the retrieved reflectance, for the cedar forest, NPP estimations were affected by BRDF effects of approximately 11% under the GLI simulated observation conditions. This study also sought to validate the NPP estimation algorithm based on the pattern-decomposition-based vegetation index (VIPD) and photosynthetically active radiation (PAR) for multi-spectral sensor data. From the retrieved reflectance, the NPP of the study forest was estimated to be 0.36 kgCO2/m2/month. For comparison, forest surveys at the same study site have been conducted since the BRF observation. Using the ground-measured data, NPP was estimated to be 0.32 kgCO2/m2/month, which is in agreement with the preliminary result.