Forest growing stock volume estimation using optical remote sensing over snow-covered ground: a case study for Sentinel-2 data and the Russian Southern Taiga region

This paper describes an approach to forest growing stock volume (GSV) estimation based on remotely sensed optical data in red and near-infrared (NIR) bands collected during the period of persistent snow cover. The approach was applied to Sentinel-2 reflectance measurements over forest with snow-covered understory in the north-eastern part of Russian Kostroma region. An in-house dataset with a forest stand-level GSV data was used to approximate GSV-reflectance relationship based on a power function for spruce-dominated, pine-dominated and birch-dominated forests. Highest coefficient of determination (R-2) = 0.84 was obtained for spruce-dominated forest and red band. A cross-validation was performed to estimate the accuracy of a stand-level GSV estimation based on the obtained GSV-reflectance relationship model and Sentinel-2 data. Best results were achieved for pine-dominated forest and NIR band: R-2 = 0.66; root-mean-square error (RMSE) = 58 m(3)/ha. This GSV estimation approach was validated with an independent dataset of field survey-based GSV measurements at the sample plot level. Validation showed R-2 values comparable to cross-validation results but higher RMSE. Overall Sentinel-2 data tested was found to be informative for GSV estimation; however performance of the described approach varied significantly depending on forest type, spectral band, GSV values range and spatial aggregation level.