Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence carbon storage. Estimation GSV at regional global scales depends on use satellite remote sensing data, although accuracies are generally lower over sparse boreal forest. This especially true forest in Russia, for which knowledge currently poor despite its importance. Here we develop new empirical method primary data source single summer Sentinel-2 MSI image, augmented by land-cover classification based same image trained using MODIS-derived data. In our work calibrated validated an extensive set field measurements from two contrasting regions Russian arctic. Results show that can be estimated with RMS uncertainty approximately 35–55%, comparable other spaceborne estimates low-GSV areas, 70% spatial correspondence between maps existing products derived MODIS Our approach requires somewhat laborious collection when used upscaling but could also downscale