Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation

Abstract

Above-ground Biomass (AGB) represents the largest amount of biomass found on earth. Passive and active remote sensors have been a useful tool in estimating AGB for this purpose; nevertheless, both data sources suffer from saturation problems in dense vegetation. A combination of optical and radar data could potentially increase the accuracy of AGB estimation. In this study we evaluate the synergistic use of Sentinel-1 and Sentinel-2 for assessing AGB in a private forest in Yogyakarta, Indonesia. Forty five sample plots of 20 m x 20 m were used as ground truth data. AGB correlated with Sentinel-1 backscatter and Sentinel-2 derived variables with R² = 0.34 and R² = 0.82, respectively; nevertheless, the synergistic use of Sentinel-1 and Sentinel-2 yielded the highest accuracy (i.e., R² = 0.84). The results indicate that AGB in Yogyakarta is most accurately estimated based on the synergy of optical and radar satellite images.

Keywords:

• Above-ground biomass
• private forest
• image fusion
• Sentinel-1
• Sentinel-2

Disclosure statement

No potential conflict of interest was reported by the author(s).

Acknowledgements

The authors deeply appreciate the support received from the Faculty of Environmental Management, Prince of Songkla University. We would also like to thank the groups of farmers in Girisekar and Jetis village for their assistance during the field data collection.