Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran

In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand Fagus Orientalis located in Hyrcanian forest Iran selected as study area. The performance a parametric approach, i.e., Multiple Linear Regression (MLR) model non-parametric approaches, k-Nearest Neighbor (k-NN), Random Forest (RF), Support Vector (SVR), were also evaluated AGB estimations. Our results indicated that among S2 metrics, FAPAR canopy biophysical index NDVI based on red-edge band (NIR-b8a) have highest correlation coefficient (r) 0.420 0.417, respectively. estimation showed S1 datasets using k-NN algorithm had best accuracy (R2 0.57 rRMSE 14.68%). L8, S2, 18.95, 16.99, 19.17% k-NN, MLR algorithms, L8 with dataset improved relative to separately by 0.96 1.18%, We concluded optical data (L8 or S2) SAR (S1) improves broadleaved estimation.