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Research article - Peer-reviewed, 2022

How to consider the effects of time of day, beam strength, and snow cover in ICESat-2 based estimation of boreal forest biomass?

Varvia, P.; Korhonen, L.; Bruguiere, A.; Toivonen, J.; Packalen, P.; Maltamo, M.; Saarela, S.; Popescu, S. C.


The objective of this study was to explore the effects of (1) the presence/absence of snow and snow depth, (2) solar noise, i.e., day/night and sun angle observations, and (3) strong/weak beam differences on ICESat2 data in the context of data utility for forest AGB estimation. The framework of the study is multiphase modeling, where AGB field data and wall-to-wall airborne laser scanning (ALS) and Sentinel-2 data are used to produce proxy ALS plots on ICESat-2 track positions. Models between the predicted proxy 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 (26.9%) 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 (30.4%), snow/day/strong (33.5%), and snowless/day/strong (34.1%). Weak beam data from snowy night conditions could also be used if necessary (31.0%).


ICESat-2; Above-ground biomass; Boreal forest; Mixed-effect models; Lidar

Published in

Remote Sensing of Environment
2022, volume: 280, article number: 113174

Authors' information

Varvia, P.
University of Eastern Finland
Korhonen, L.
University of Eastern Finland
Bruguiere, A.
University of Eastern Finland
Bruguiere, A.
The National Land Survey of Finland
Toivonen, J.
University of Eastern Finland
Packalen, P.
University of Eastern Finland
Maltamo, M.
University of Eastern Finland
Swedish University of Agricultural Sciences, Department of Forest Resource Management
Norwegian University of Life Sciences (NMBU)
Popescu, S. C.
Texas AandM University College Station

UKÄ Subject classification

Remote Sensing
Forest Science

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