The objective of this research is to test Sentinel-1 SAR multitemporal data, supported by multispectral and SAR data at other wavelengths, for fine-scale mapping of above-ground biomass (AGB) at the provincial level in a Mediterranean forested landscape. The regression results indicate good accuracy of prediction (R2=0.7) using integrated sensors when an upper bound of 400Mg ha-1 is used in modeling. Multitemporal SAR information was relevant, allowing the selection of optimal Sentinel-1 data, as broadleaf forests showed a different response in backscatter throughout the year. Similar accuracy in predictions was obtained when using SAR multifrequency data or joint SAR and optical data. Predictions based on SAR data were more conservative, and in line with those from an independent sample from the National Forest Inventory, than those based on joint data types. The potential of S1 data in predicting AGB can possibly be improved if models are developed per specific groups (deciduous or evergreen species) or forest types and using a larger range of ground data. Overall, this research shows the usefulness of Sentinel-1 data to map biomass at very high resolution for local study and at considerable carbon density. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The interest in tree phenology monitoring is increasing because this trait is a robust indicator of the impacts of climate change on natural and managed ecosystems. Different approaches to monitor phenology at different spatial scales, from in situ monitoring to remote sensing, are used to investigate spring and/or autumn phenological changes. In Mediterranean area, most of phenological changes occur during cloudy periods (spring and autumn), leading to a loss of information also for very high temporal resolution satellites. Instead, cloud-uninfluenced sensors, such as radar sensors, can allow to bypass this problem and produce a temporally continuous coverage. In this paper, we analyzed the spring phenology of two European beech (Fagus sylvatica L.) populations, located at different latitudes in Mediterranean area. Weekly in situ monitoring of leaf-out has been correlated with data collected by Synthetic Aperture Radar. Spring phenological phases were monitored in situ following a modified BBCH-code with a 5-scores scale (from 1 - buds closed and covered by scales, to 5 - leaf completely unfolded). The score 3 (young leaves starting to emerge from the bud) was considered the bud break. Different site conditions based on aspect (northern and southern) and altitudinal gradient (high and low altitude) have been considered. The aim was to test and implement a new methodology able to decrease the frequency of the field sampling, using remote data, to extend more detailed information on geographical scale, and to reconstruct past phenology. Results showed a statistically significant different length of the vegetative spring period, spanning from dormant buds, up to leaves completely unfolded, between sites. Through Synthetic Aperture Radar estimation, this study demonstrates that leaf-out can be monitored with an extreme accuracy. The phenophase score 4 and 5 estimation showed the best performance (RMSE < of 4 days), phenophases score 2 and 3 showed promising performances (4 days < RMSE <5 days), while phenophases score 1 seems to be not easily detectable, although it can be extrapolated with an RMSE <6 days. This radar approach fixes the cloud problem typical of multispectral approach and very frequent in phenophase change periods in Mediterranean climate. This study promotes the proposed remote sensing approach as a very useful tool to monitor growing season starting in remote areas, helping to reduce in situ observations and allowing past phenology reconstruction. © 2020 Elsevier Inc.