  Accurate mapping of forests is critical for forest management and carbon
stocks monitoring. Deep learning is becoming more popular in Earth Observation
(EO), however, the availability of reference data limits its potential in
wide-area forest mapping. To overcome those limitations, here we introduce
contrastive regression into EO based forest mapping and develop a novel
semisupervised regression framework for wall-to-wall mapping of continuous
forest variables. It combines supervised contrastive regression loss and
semi-supervised Cross-Pseudo Regression loss. The framework is demonstrated
over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery
for mapping forest tree height. Achieved prediction accuracies are strongly
better compared to using vanilla UNet or traditional regression models, with
relative RMSE of 15.1% on stand level. We expect that developed framework can
be used for modeling other forest variables and EO datasets.
