ABOVEGROUND BIOMASS ESTIMATION USING NISAR SIMULATED ALOS-2 TIME SERIES DATA

Aboveground biomass (AGB) is a critical parameter for improving the understanding of global carbon cycle and to develop sustainable forest management. However, a large uncertainty prevails. L-band data demonstrates strong potential for accurate retrieval of low-biomass regions (<100 Mg ha-1). The upcoming NASA-ISRO Synthetic Aperture Radar will collect data at L- and S-band over earth’s landmass with a repeat period of 12 days, allowing us to have ample data for monitoring biomass and its dynamics. The one the key requirement of the mission is to produce annual AGB maps at 1-ha resolution with RMS accuracy of 20 Mg/ha for 80 percentage of area over low-biomass regions. The NISAR biomass algorithm will generate AGB map based on the parameterization of semi-empirical model along with NISAR time-series dual pol data (HH and HV). To calibrate and validate the model for mission requirements, reference estimates of AGB over the ground plots and LiDAR data collected over the selected sites distributed across different global ecoregions are used. This paper presents the initial results of the calibration/validation of NISAR AGB retrieval algorithm over the Lenoir Landing (LENO), Alabama, USA using NISAR simulated ALOS-2 time series data. Five multi-temporal dual-pol HH and HV NISAR Simulated ALOS 2 data was used to assess the performance of the model. The model AGB retrieval results shows that the NISAR model was able to achieve the RMS accuracy within 20 Mg/ha.