Using hypertemporal Sentinel-1 data to predict forest growing stock volume

In this study, we assess the potential of long time series of Sentinel-1 SAR data to predict forest growing stock volume and evaluate the temporal dynamics of the predictions. The boreal coniferous forests study site is located near the Hyytiala forest station in central Finland and covers an area of 2,500 km2 with nearly 17,000 stands. We considered several prediction approaches (linear, support vector and random forests regression) and fine-tuned them to predict growing stock volume in several evaluation scenarios. The analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate considerable decrease in RMSEs of growing stock volume as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE (relative RMSE 50-53%), RMSE with combined images decreased to 75.6 m3/ha (relative RMSE 44%). Feature extraction and dimension reduction techniques facilitated achieving the near-optimal prediction accuracy using only 8-10 images. When using assemblages of eight consecutive images, the GSV was predicted with the greatest accuracy when initial acquisitions started between September and January. HighlightsO_LITime series of 96 Sentinel-1 images is analysed over study area with 17,762 forest stands. C_LIO_LIRigorous evaluation of tools for SAR feature selection and GSV prediction. C_LIO_LIImproved periodic seasonality using assemblages of consecutive Sentinel-1 images. C_LIO_LIAnalysis of combining images acquired in "frozen" and "dry summer" conditions. C_LIO_LICompetitive estimates using calculation of prediction errors with stand-area weighting. C_LI