Abstract
Vegetation canopy height is one of the important remote sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global Navigation Satellite System - Reflectometry (GNSS-R) has proven capable to retrieve vegetation information at a moderate resolution from space (20-65 km) using L1 C/A signals. In this study, data retrieved by the airborne Microwave Interferometric Reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest Canopy Height product, with a spatial resolution of 30 m. This work analyzes the waveforms measured at both bands, and the correlation of the waveform width and the reflectivity values to the canopy height product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the canopy height information at a very high resolution, with a root-mean-square error of 4.25 m and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual canopy height.
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