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 (R²) = 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: R² = 0.66; root-mean-square error (RMSE) = 58 m³/ha. This GSV estimation approach was validated with an independent dataset of field survey-based GSV measurements at the sample plot level. Validation showed R² 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.

本文介绍了一种基于持续积雪期间收集的红色和近红外（NIR）波段遥感光学数据的森林蓄积量（GSV）估算方法。该方法已应用于俄罗斯科斯特罗马地区东北部积雪覆盖的林下森林的Sentinel-2反射率测量。使用具有林分级别GSV数据的内部数据集，以基于幂函数的云杉为主，松树为主和桦木为主的森林来近似GSV反射关系。最高测定系数（R ²）= 0.84，对于以云杉为主的森林和红带而言。基于获得的GSV-反射关系模型和Sentinel-2数据，执行交叉验证以估计标准位GSV估计的准确性。对于以松树为主的森林和近红外波段，取得了最佳结果：R ²  = 0.66；均方根误差（RMSE）= 58 m ³ / ha。该GSV估算方法已通过独立的基于田间调查的GSV测量数据集在样本图级别进行了验证。验证显示R ²值可与交叉验证结果媲美，但RMSE更高。发现测试的Sentinel-2总体数据可为GSV估算提供参考；但是，所描述方法的性能因森林类型，光谱带，GSV值范围和空间聚集水平而异。