Sentinel-1 and Sentinel-2 data for predicting forest stand variables in GEE: A case study of timber plantations in northern Spain

Aim of study: In this study we developed machine learning models for estimating five forest stand variables, including total over bark volume, number of trees per hectare, dominant height, basal area and aboveground biomass, from remote sensing data and ancillary variables in forest plantations of Eucalyptus globulus Labill, Pinus pinaster Aiton and Pinus radiata D. Don. Area of study: The study was conducted in northern Spain (Autonomous Communities of Asturias, Cantabria, Basque Country and Galicia) where plantations of the species of interest are mainly concentrated due to the high productivity of forest stands in these areas. Material and methods: We used ground-truth data from Spanish National Forest Inventory plots, two sources of remote sensing data and images from the Sentinel-1 constellation (radar) and from the Sentinel-2 constellation (optical) combined with terrain and climate data. We used the Google Earth Engine platform using Random Forest algorithm with 10-fold cross-validation to obtain forest stand variables, including density, size and yield variables. Main results: Evaluation of the model accuracy and variable importance in estimating forest variables showed that the total volume models performed best, yielding R² values between 0.39 and 0.45 for the different tree species. Optical bands were very important in all cases, while radar bands were less important. Research highlights: The use of synthetic aperture radar, although promising, is limited in platforms like GEE, especially for complex terrains where shadowing and angles affect the image quality. The research contributions include the automation of preprocessing steps, and the findings highlight the need to develop more robust, adaptable models using AI and new radar sensors.