Petri Varvia, Lauri Korhonen, André Bruguière, Janne Toivonen, Petteri Packalen, Matti Maltamo, Svetlana Saarela, Sorin Popescu

Spaceborne lidar sensors have potential to improve the accuracy of forest above-ground biomass (AGB) estimates by providing direct measurements of 3D structure of forests over large spatial scales. The ICESat-2 (Ice, Cloud and land Elevation Satellite 2), launched in 2018, provides a good coverage of the boreal forest zone and has been previously shown to provide good estimates of forest canopy height and AGB. However, spaceborne lidar data are affected by various conditions, such as presence of snow, solar noise, and in the case of ICESat-2, the power difference between the so-called strong and weak beams.

The aim of this study was to explore the effects of these conditions on the performance of AGB modeling using ICESat-2 photon data in a boreal forest area. The framework of the study is multiphase modeling, where AGB field data and wall-to-wall airborne laser scanning (ALS) data are used to produce proxy ALS plots on ICESat-2 track positions. Models between the ALS-predicted AGB and the ICESat-2 photon data are then formulated and evaluated by subsets, such as only strong beam data captured in snowy conditions.

Our results indicate that, if possible, strong beam night data from snowless conditions should be used in AGB estimation, because our models showed clearly smallest RMSE (27.0%) for this data subset. If more data are needed, we recommend using only strong beam data and constructing separate models for the different data subsets. In the order of increasing RMSE\%, the next best options were snow/night/strong (30.5%), snow/day/strong (33.6%), and snowless/day/strong (34.2%). Weak beam data from snowy night conditions could also be used if necessary (31.1%).