BRDF Effect on the Estimation of Canopy Chlorophyll Content in Paddy Rice from UAV-Based Hyperspectral Imagery

Abstract

The bidirectional reflectance distribution function (BRDF) effect due to the surface reflectance anisotropy and variations in the solar and viewing geometry has been studied in the remote sensing community for several decades, and most attention was paid to the satellite sensors with large field of view (FOV), such as MODIS with a 110° FOV. With the development of unmanned aerial vehicle (UAV) technique, the imagery acquired at UAV platform provides important information about crop growth status, which is a promising and efficient approach for precise agriculture. However, few studies explored the BRDF effect in UAV images, especially for the sensors with small FOVs. This study investigated the BRDF effect on the estimation of canopy chlorophyll content (CCC) with the UHD 185 hyperspectral imagery (27° FOV) acquired at a UAV platform. Our results from a rice field-plot experiment demonstrated that the CCC was highly correlated to the red-edge chlorophyll index derived at five different view angles. However, the regression models were significantly different among these view angles. This implied that no single CCC estimation model can be applied to the whole image for CCC mapping. The findings suggest the BRDF effect should be considered for providing reliable and consistent CCC estimation.