Estimating forest biomass using repeat-pass polarimetric radar interferometry

Biomass is identified by the United Nations Framework Convention on Climate Change (UNFCCC) as an essential climate variable needed to reduce uncertainties in our knowledge of the climate system [1]. Radar remote sensing is the most suitable tool to measure and map Earth's forest biomass, but current methods are limited by saturation issues (backscatter-based methods) or by large uncertainties (interferometric volumetric correlation-based methods) [2]. Here, we developed a new method for estimating forest biomass, which overcomes these limitations. The method utilizes a repeat-pass polarimetric radar interferometer that measures the temporal-volumetric correlation between consecutive radar acquisitions. Using our physical model [3], we are able to relate a set of temporal-volumetric correlation samples (obtained for several combinations of wave polarizations) to important biophysical parameters of forests. We designed a model-based algorithm for parameters estimation that gives maps of forest tree height, using all available information returned by the polarimetric interferometer, including radar backscatter. Forest height estimated from simulated and actual radar UAVSAR data is found in agreement with forest height derived from lidar LVIS data. Height-biomass allometric equations, previously validated with ground observations, are used to estimate the aboveground biomass [4]. Our method allows quantifying the worldwide biomass distribution and monitoring biomass dynamic changes (e.g., deforestation). Future radar missions, such as the NASA/DESDynI, JAXA/ALOS-2 and ESA/BIOMASS can exploit this method [5]. Moreover, our theoretical modeling has unveiled new insights into the temporal decorrelation, such as the dependence on wave polarization and target structure [3], bringing benefits to all techniques exploiting radar time series, beyond the remote sensing of vegetated lands. [1] Second report on the Adequacy of the Global Observing System for Climate in Support of the UNFCCC. GCOS-82 (WMO/TD No. 1143): World Meteorological Organization, 2003. [2] Le Toan, T., et al., The BIOMASS mission: "Mapping global forest biomass to better understand the terrestrial carbon cycle", Remote Sensing of Environment, 2011. [3] Lavalle, M., Simard, M., Hensley, S., "A Temporal Decorrelation Model for Polarimetric Radar Interferometers", accepted for publication in IEEE Trans. on Geoscience and Remote Sensing, 2011. [4] Mette, T., Papathanassiou, K.P., Hajnsek, I., "Biomass estimation from Pol-InSAR over heterogeneous Terrain", IEEE Geoscience and Remote Sensing Symposium, 2004. [5] Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, National Research Council, 2007.