This study aimed at estimating total forest above-ground net change (ΔAGB; Gg) 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 Mha) 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 was 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 spaceborne optical 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 Gg). However, the decrease in precision when using Landsat data was small (SE = 1.92 Gg). We also found that ΔAGB could be precisely estimated when remotely sensed data were available only at the end of the monitoring period.

We conclude that satellite optical data can considerably improve ΔAGB estimates, when repeated and coincident field 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 consistent and durable monitoring of forest carbon dynamics.

本研究旨在利用免费提供的卫星图像，基于模型辅助估算，估算五年（2014-2019 年）森林地上净变化总量（ΔAGB；Gg）。该研究是针对挪威的一个北方森林地区（约 1.4 Mha）进行的，在那里可以获得双时态国家森林清单 (NFI)、Sentinel-2 和 Landsat 数据。生物量变化是基于直接方法建模的。将在基本扩展估计器中仅使用 NFI 数据的估计精度与使用 1) Sentinel-2 或 Landsat 数据和 2) 使用双时或单时遥感数据的四种不同替代模型辅助估计进行比较。

我们发现星载光学数据将纯粹基于场的估计的精度提高了三倍。使用双时态 Sentinel-2（标准误差；SE = 1.7 Gg）为模型辅助估计找到了最精确的估计。然而，使用 Landsat 数据时精度的下降很小（SE = 1.92 Gg）。我们还发现，当遥感数据仅在监测期结束时可用时，可以精确估计 ΔAGB。

我们得出的结论是，当重复和重合的现场数据可用时，卫星光学数据可以显着提高 ΔAGB 估计值。免费提供、全球覆盖、频繁更新和长期时间范围使来自 Sentinel-2 和 Landsat 等程序的数据成为对森林碳动态进行一致和持久监测的宝贵数据源。