Leaf area index (LAI) is an important driving factor in forest ecosystems carbon cycle, the acquisition of multiscale LAI can illustrate the carbon cycle at different scales and its dynamic response to environmental changes. Moso bamboo forest (MBF) has strong carbon sequestration capability and unique phenological characteristics of on-years and off-years. In this study, based on the MODIS LAI and Sentinel-2 reflectance products in 2018–2019, we coupled the Hierarchical Bayesian Network (HBN) algorithm, LAI dynamic model and PROSAIL model to acquire multiscale spatiotemporal assimilated LAI (500-m, 100-m, 20-m) of MBF. First, the results showed that band7 (B7) and band8a (B8a) of Sentinel-2 were highly sensitive to LAI; the simulated leaf and canopy reflectance by PROSAIL demonstrated higher accuracies and lower errors in these two bands. Second, after applying the Savitzky-Golay smoothing, compared with the observed LAI (Obs_eLAI), the accuracy of MODIS LAI was improved by 121%, and the error was reduced by 24%. Third, there was a significant correlation between the multiscale assimilated LAI (HBN_LAIs) and Obs_eLAI ($R_{500\mathrm{m}\_\mathrm{H}\mathrm{B}\mathrm{N}\_\mathrm{L}\mathrm{A}\mathrm{I}}^2=0.80,R_{100\mathrm{m}\_\mathrm{H}\mathrm{B}\mathrm{N}\_\mathrm{L}\mathrm{A}\mathrm{I}}^2=0.82,R_{20\mathrm{m}\_\mathrm{H}\mathrm{B}\mathrm{N}\_\mathrm{L}\mathrm{A}\mathrm{I}}^2=0.80$). Finally, the assimilated results in 2018 (off-year) were better than those in 2019 (on-year); the variations in multiscale spatiotemporal assimilated LAI were consistent with the actual growth trends of MBF. This study provides a feasible way to accurately obtain multiscale high-resolution LAI for the carbon cycle simulation of bamboo forests.

叶面积指数（LAI）是森林生态系统碳循环的重要驱动因子，多尺度 LAI 的获取可以说明不同尺度的碳循环及其对环境变化的动态响应。毛竹林（MBF）具有很强的固碳能力和独特的顺年和逆年的物候特征。本研究基于2018-2019年MODIS LAI和Sentinel-2反射率产品，结合层次贝叶斯网络（HBN）算法、LAI动态模型和PROSAIL模型，获得多尺度时空同化LAI（500-m，100- m, 20-m) 的 MBF。首先，结果表明Sentinel-2的band7（B7）和band8a（B8a）对LAI高度敏感；PROSAIL 模拟的叶片和冠层反射率在这两个波段中表现出更高的精度和更低的误差。第二，应用Savitzky-Golay平滑后，与观察到的LAI（Obs_eLAI）相比，MODIS LAI的精度提高了121%，误差降低了24%。第三，多尺度同化LAI（HBN_LAI）与Obs_eLAI（${\mathrm{电阻}}_{500\mathrm{米}\_\mathrm{H}\mathrm{乙}\mathrm{N}\_\mathrm{升}\mathrm{一种}\mathrm{一世}}^2=0.80,{\mathrm{电阻}}_{100\mathrm{米}\_\mathrm{H}\mathrm{乙}\mathrm{N}\_\mathrm{升}\mathrm{一种}\mathrm{一世}}^2=0.82,{\mathrm{电阻}}_{20\mathrm{米}\_\mathrm{H}\mathrm{乙}\mathrm{N}\_\mathrm{升}\mathrm{一种}\mathrm{一世}}^2=0.80$）。最后，2018年（off-year）同化结果好于2019年（on-year）；多尺度时空同化LAI的变化与MBF的实际增长趋势一致。该研究为准确获取多尺度高分辨率LAI用于竹林碳循环模拟提供了一种可行的方法。