Being vital to many of the Earth’s ecosystems, forests provide a variety of functions, such as providing habitat for animals and plants, protecting watersheds and preventing soil erosion. Reliable and frequently updated information on forest resources and it’s condition is needed for analysis of patterns and trends, sustainable forest management as well as for a large number of other applications. Nowadays, terrestrial in-situ observations are complemented by remote sensing techniques. Airborne campaigns with multispectral cameras or Light Detection and Ranging (LiDAR) are carried out and provide area-wide spatial data of many forest parameters such as forest cover, forest type and composition or above-ground biomass. Due to the high cost of these campaigns, the temporal resolution is still in the range of 3 to 10 years or maps are not regularly updated at all. Spaceborne remote sensing data help to bridge the gaps in temporal resolution and spatial coverage. Bruggisser et al., 2021 [1] showed the potential of complementing the ALS data with sparse temporal resolution with Sentinel-1 to regularly update the ALS based forest structure information. They used Sentinel-1 to derive forest structural parameters, namely the stand height and fractional cover, using a random forest (RF) model trained on LiDAR-derived estimates with statistical parameters based on Sentinel-1 backscatter and interferometric coherence time series as input features. The study was conducted for a temperate deciduous forest in a hilly study area near Vienna, Austria, and the model was trained and validated using airborne laser scanning (ALS) data In this study, we tested the viability of this approach in coniferous forests and very challenging environment in an alpine study region located in Tyrol, Western Austria. A RF model was trained to predict forest height and gap fraction from Sentinel-1 at a spatial resolution of 100 m. The results yielded Pearson correlation coefficients (r) of 0.77 for forest height and 0.82 for gap fraction (compared to 0.88 for forest height and 0.94 for gap fraction in case of the results published by Bruggisser et al., 2021 in broadleaf forests and hilly terrain). Optimisation of the model predictors to better compensate for the strong terrain effects in the alpine study region and to better fit the seasonality of the predominantly coniferous forests can further increase r to 0.83 and 0.90 for forest height and gap fraction, respectively, in case of the Tyrol study area. The results imply, that even in such a challenging environment, Sentinel-1 data can complement sparse ALS acquisitions and provide yearly estimates of forest structural [1] Bruggisser, Moritz, Wouter Dorigo, Alena Dostálová, Markus Hollaus, Claudio Navacchi, Stefan Schlaffer, and Norbert Pfeifer. "Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests." Remote Sensing 13, no. 4 (2021): 798.