Deep learning (DL) models are gaining popularity in forest variable
prediction using Earth Observation images. However, in practical forest
inventories, reference datasets are often represented by plot- or stand-level
measurements, while high-quality representative wall-to-wall reference data for
end-to-end training of DL models are rarely available. Transfer learning
facilitates expansion of the use of deep learning models into areas with
sub-optimal training data by allowing pretraining of the model in areas where
high-quality teaching data are available. In this study, we perform a "model
transfer" (or domain adaptation) of a pretrained DL model into a target area
using plot-level measurements and compare performance versus other machine
learning models. We use an earlier developed UNet based model (SeUNet) to
demonstrate the approach on two distinct taiga sites with varying forest
structure and composition. Multisource Earth Observation (EO) data are
represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2
multispectral images, JAXA ALOS-2 PALSAR-2 SAR mosaic and TanDEM-X bistatic
interferometric radar data. The training study site is located in Finnish
Lapland, while the target site is located in Southern Finland. By leveraging
transfer learning, the prediction of SeUNet achieved root mean squared error
(RMSE) of 2.70 m and R$^2$ of 0.882, considerably more accurate than
traditional benchmark methods. We expect such forest-specific DL model transfer
can be suitable also for other forest variables and other EO data sources that
are sensitive to forest structure.

使用深度学习模型进行森林变量预测具有越来越大的流行度，本研究通过模型转换方法将预训练的深度学习模型应用于目标区域，使用地球观测数据进行森林结构预测，结果表明相较于传统基准方法，利用迁移学习的 SeUNet 模型具有更高的准确性。