ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-V-3-2022-369-2022 BIOMASS ESTIMATION ALONG A CLIMATIC GRADIENT USING MULTI-FREQUENCY POLARIMETRIC RADAR VEGETATION INDEX Chang G. J. 1 Oh Y. 2 Shoshany M. 1 Faculty of Civil and Environmental Engineering Department, Technion, Israel Institute of Technology, Haifa, 32000, Israel School of Electronic and Electrical Engineering, Hongik University, South Korea 17 05 2022 V-3-2022 369 374 Copyright: © 2022 G. J. Chang et al. 2022 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/V-3-2022/369/2022/isprs-annals-V-3-2022-369-2022.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/V-3-2022/369/2022/isprs-annals-V-3-2022-369-2022.pdf

Monitoring biomass changes in the Mediterranean ecosystems is important for better understanding their responses to climatic and anthropogenic changes. Multi-frequency SAR data (L-band PASLAR and C-band Sentinel-1) is investigated for developing a biomass estimation model along a climatic gradient in Israel. First, the relationships between biomass and transmissivity at various frequencies are discussed. Multi-frequency polarimetric radar vegetation index (MPRVI) is then proposed utilizing the ensemble average of degree of polarization and cross-polarized backscattering coefficients. After randomly partitioning the data for training set and testing set, the new MPRVI-based biomass model is evaluated. It shows a good agreement with reference biomass data with an r-square of 0.899 and a root-mean-squared error (RMSE) of 0.381 kg/m<sup>2</sup> and with a relative RMSE (RRMSE) of 10.8%.