Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest

Abstract Accurate growing stock volume (GSV) estimation is essential for forest inventory updating, terrestrial carbon stocks reporting, and ecosystem services assessment. This study investigates the potential of spectral and spatial features derived from single-date and multi-seasonal Sentinel-2 Multi Spectral Instrument (Sentinel-2 MSI) images, for GSV estimation in a Mediterranean region of Northeastern Greece. Original spectral bands, spectral indices, first-order statistics, Gray-Level Co-Occurrence Matrix (GLCM) texture measures, and Local Indicators of Spatial Association (LISA), based on the multi-seasonal and single-date Sentinel-2 MSI imagery, were used for GSV model development using the bagging LASSO algorithm. For both single and multi-date approaches, the spectral indices models were more accurate compared to the respective ones developed with the original Sentinel-2 MSI bands. Also, models based on texture were more efficient than the spectral models. The GLCM measures derived from July image, provided the most accurate single-date estimate of GSV (R 2  = 0.89, RMSE = 35.21), while their multi-seasonal counterparts improved slightly the accuracy (R 2  = 0.91, RMSE = 32.77). Fusion of spatial and textural information resulted in marginal or no-improvement on the texture model accuracy, however the fused models yielded higher predictive results than the spectral models alone.