In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m³/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson's correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m³/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m³/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

中文翻译：

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利用SAR和机器学习绘制地中海森林木本植物数量-以意大利中部为例。

在本文中，使用了L和C波段的多频合成孔径雷达（SAR）数据（ALOS PALSAR和Envisat / ASAR）来估算意大利中部托斯卡纳的森林生物量。木质体积的地面测量值（WV，以m ^{3为单位）}/ ha）（可以视为森林生物量的代理）从意大利国家森林清单（NFI）中检索。在初步评估以评估C波段和L波段的反向散射对森林生物量的敏感性之后，实施了一种基于人工神经网络（ANN）的方法。使用L波段（ALOS PALSAR，HH和HV极化）和C波段（Envisat ASAR处于HH极化）的后向散射系数作为输入来训练ANN。整个测试区域的空间分布WV值是通过从冰，云和土地高程地球科学激光测高仪系统（ICESat GLAS）和NFI数据得出的冠层高度图的积分（融合）得出的（融合）。训练阶段的重要地面真相数据集。对WV的背向散射灵敏度的分析表明，在L波段具有中等相关性，而在C波段几乎可以忽略不计。尽管如此，ANN算法仍能够利用SAR频率和极化的协同作用，用平均Pearson相关系数（R）= 0.96和均方根误差（RMSE）≃39 m估算WV³ /公顷时施加到测试数据集和平均R = 0.86和RMSE≃75米³ /当从NFI直接测量验证公顷。考虑到情景的异质性（丘陵地貌中的地中海混交林）以及相对于不同地块的空间变异性可用的少量地面测量，可以认为获得的结果令人满意。此外，成功使用全局地图中的WV来实现算法表明了将算法应用于更广泛的区域甚至全球范围的可能性。