The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R² = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R² = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R² = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R² = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

Background: Traditional field-based methods of measurement of biomass and carbon storage face difficulty in collecting time-consuming and expensive, suggests the use of remote sensing-based techniques. It estimates the economic value of the aboveground biomass (AGB) using satellite remote sensing across the Hyrcanian forests of Iran. Methods: The Landsat-8 OLI sensor data were combined with field-based allometric information of 186 circular sample plots. The AGB was calculated at the plot level using the collected data and specific volumetric mass for species in the studied area. It was followed by calculating the carbon storage using a 50% carbon coefficient and the photosynthesis equation at the forest parcel level. Model results using the random forest and support vector machines. The carbon sequestration value was calculated with USD 25.3 as a shadow value of carbon in 2014 and using the replacement cost approach. Results: The highest performances achieved by RF for biomass, carbon storage and the carbon storage value (Iranian Rials of 0.67% and 16%, respectively). The value was derived once at the plot level of 12.22 million IRR (370.43 USD) per ha. In addition, at the parcel level, which resulted in an estimated value of 12.87 million IRR (390.24 USD) per ha.