Estimate LAI of crops using airborne multi-angular data

Usually we use multi-channel image data, such as TM, and empirical relationship, such as NDVI-LAI relation or SR-LAI relation, to estimate LAI. Multi-angular remote sensing data provide more information for canopy structure. This paper presents a method to estimate LAI using multi-angular data and model inversion method. The airborne multi-angular data were acquired by AMTIS (Airborne Multi-angle TIR/VNIR Imaging System), which was a prototype sensor designed by the Institute of Remote Sensing Applications of Chinese Academy of Science. Our study is based on two datasets: one was acquired in Beijing Shunyi in April 11, and the major crop is sparse winter wheat; another was acquired in Haerbin in August 24, and major crops are dense corn and soybean. Both datasets have been geometrically atmospherically corrected. Ground based measurements were carried out during the flight experiment. SAIL model is chosen to predict reflected radiance of a presumed LAI. Various view angles relate to the different components ratio in view field, and the reflected radiance is different accordingly. Hence, a certain LAI value was given, SAIL model predicts a set of reflected radiances of various angles. We compare the model predict radiance with the radiance viewed by an multi-angular sensor, to find the optimized LAI which can make the radiance predicted by the model be closest to the viewed radiance, then take this LAI value as the right value