  This study aimed at estimating total forest above-ground net change (Delta
AGB, Mt) over five years (2014-2019) based on model-assisted estimation
utilizing freely available satellite imagery. The study was conducted for a
boreal forest area (approx. 1.4 Mill hectares) in Norway where bi-temporal
national forest inventory (NFI), Sentinel-2, and Landsat data were available.
Biomass change was modelled based on a direct approach. The precision of
estimates using only the NFI data in a basic expansion estimator were compared
to four different alternative model-assisted estimates using 1) Sentinel-2 or
Landsat data, and 2) using bi- or uni-temporal remotely sensed data. We found
that the use of remotely sensed data improved the precision of the purely
field-based estimates by a factor of up to three. The most precise estimates
were found for the model-assisted estimation using bi-temporal Sentinel-2
(standard error; SE= 1.7 Mt). However, the decrease in precision when using
Landsat data was small (SE= 1.92 Mt). In addition, we found that Delta AGB
could be precisely estimated also when remotely sensed data were available only
at the end of the monitoring period. We conclude that satellite optical data
can considerably improve Delta AGB estimates, even in those cases where
repeated and coincident NFI data are available. The free availability, global
coverage, frequent update, and long-term time horizon make data from programs
such as Sentinel-2 and Landsat a valuable data source for a consistent and
durable monitoring of forest carbon dynamics.
