Evaluation of multifrequency SAR data for estimating tropical above-ground biomass by employing radiative transfer modeling

Environ Monit Assess. 2023 Aug 29;195(9):1102. doi: 10.1007/s10661-023-11715-7.

Abstract

The retrieval of the biophysical parameters and subsequent estimation of the above-ground biomass (AGB) of vegetation stands are made possible by the simulation of the extinction and scattering components from the canopy layer using vector radiative transfer (VRT) theory-based scattering models. With the use of such a model, this study aims to evaluate and compare the potential of dual-pol, multi-frequency SAR data for estimating above-ground biomass. The data selected for this work are L-band dual polarized (HH/HV) ALOS-2 data, S-band dual polarized (HH/HV) NovaSAR data, and C-band dual polarized (VV/VH) Sentinel-1 data. The two key biophysical parameters, tree height, and trunk radius are retrieved using the proposed methodology, applying the frequencies independently. A general allometric equation with vegetation-specific coefficients is used to estimate the AGB from the retrieved biophysical parameters. The retrieval results are validated using ground truth measurements collected from the study area. The L-band, with the coefficient of determination ([Formula: see text]) of 0.73 and the root mean square error (RMSE) of 35.90 t/ha, has the best correlation between the modeled and field AGBs, followed by the S-band with an [Formula: see text] of 0.37 and an RMSE of 63.37 t/ha, and the C-band with an [Formula: see text] of 0.25 and an RMSE of 72.32 t/ha. The L-band has yielded improved estimates of AGB in regression analysis as well, with an [Formula: see text] of 0.48 and an RMSE of 50.02 t/ha, compared to the S- and C-bands, which have the [Formula: see text] of 0.12 and 0.03 and the RMSE of 70.98 t/ha and 80.84 t/ha, respectively.

Keywords: Above-ground biomass; Backscatter; Biophysical parameters; Scattering model; Vector radiative transfer.

MeSH terms

  • Biomass
  • Computer Simulation
  • Environmental Monitoring*
  • Research*
  • Trees