Hybrid regression method to predict forest variables from Earth observation data in boreal forests

Satellite remote sensing is essential for monitoring the boreal forest, the largest land biome on Earth. With the growing volume of Earth observation (EO) data and increasing demand for actionable information, more efficient and robust monitoring methods are needed. Machine learning-based approaches offer flexibility but rely on extensive training data, which can be generated with reflectance models. This study introduces a hybrid regression method, integrating the forest reflectance and transmittance model FRT with a random forest regressor. Using a representative dataset from Finland (24 081 plots), the method was trained to predict structural boreal forest variables: mean height, mean diameter at breast height (DBH) and basal area from EO data. The prediction performance was evaluated using three independent test areas, two from Finland and one from Sweden. In Finland, the most accurate predictions had root-mean-square errors of 3.6 m (19.1%) for height, 6.3 cm (27.3%) for DBH and 9.9 m²/ha (31.6%) for basal area. In Sweden, low R² values (< 0.1) indicated limitations in transferability. The results suggest that combining reflectance modelling with machine learning can advance environmental monitoring methodologies in the boreal forest but also demonstrate the challenges of applying these methods across different geographical regions.