Phenology plays an important role in revealing the spatiotemporal evolution of forest ecosystem carbon cycles. The accuracy of vegetation phenology estimates based on remote sensing has improved in temperate zones. However, subtropical vegetation is complex, and the corresponding phenology estimates using remote sensing face great challenges. Bamboo forests are subtropical unique forest types and exhibit on– and off-years, fast growth, high productivity and carbon sequestration capability. In this study, we propose a new method to improve phenology estimates of bamboo forests by coupling the particle filter (PF) assimilation algorithm and a logistic model. The phenological metrics are estimated using high-precision leaf area index (LAI) assimilation products and a logistic model from 2001 to 2018, and the results are compared to those extracted from Moderate-Resolution Imaging Spectroradiometer (MODIS) LAI and the enhanced vegetation index (EVI) calculated based on the MODIS reflectance data. The results reveal that the R² values between the start of the growing season (SOS) and end of the growing season (EOS) estimated by the assimilated LAI and ground-observed values are the highest (>0.50) and the root mean square errors (RMSEs) are the smallest (<6.35 days). A negative correlation occurs between the EVI-simulated and ground-observed SOS and EOS values, which indicates that EVI products cannot be adopted to estimate the phenology of bamboo forests. Compared to the MODIS LAI, the R² values of the predicted SOS and EOS by the assimilated LAI data are improved by 3.67 times and 12.50%, respectively, and the RMSEs are reduced by 58.91% and 41.13%, respectively. Therefore, the new method solves the problem whereby the phenology of subtropical bamboo forests cannot be accurately extracted from MODIS LAI and EVI products. The temporal and spatial patterns of the SOS and EOS of bamboo forests are estimated with the new method from 2001 to 2018, and the SOS exhibits obvious spatial heterogeneity during on– and off-years, and the SOS during the on-years occurs slightly earlier than that during the off-years. A total of 70.13% of all pixels exhibit a SOS advance trend, while more than half of the areas (58.42%) present an EOS delay trend. The results indicate that coupling the data assimilation algorithm and phenology method greatly improves the estimation precision and reduces the estimation errors of the SOS and EOS of bamboo forests.

物候学在揭示森林生态系统碳循环的时空演变中起着重要作用。在温带地区，基于遥感的植被物候估计的准确性有所提高。但是，亚热带植被非常复杂，使用遥感进行相应的物候估计面临巨大挑战。竹林是亚热带独特的森林类型，具有常年和常年，快速增长，高生产力和固碳能力。在这项研究中，我们提出了一种新的方法，通过结合粒子过滤器（PF）同化算法和逻辑模型来改善竹林的物候估计。使用2001年至2018年的高精度叶面积指数（LAI）同化产品和logistic模型估算物候指标，并将结果与​​从中分辨率成像光谱仪（MODIS）LAI提取的结果以及基于MODIS反射率数据计算出的增强植被指数（EVI）进行比较。结果表明，R根据同化LAI估计的生长季节开始（SOS）和生长季节结束（EOS）之间的^2个值是最高的（> 0.50），并且均方根误差（RMSEs）最小（<6.35天）。EVI模拟的和地面观测的SOS和EOS值之间存在负相关关系，这表明EVI产品不能用于估计竹林的物候。与MODIS LAI相比，R ²LAI数据对SOS和EOS的预测值分别提高了3.67倍和12.50％，RMSE分别降低了58.91％和41.13％。因此，新方法解决了无法从MODIS LAI和EVI产品中准确提取亚热带竹林物候的问题。2001年至2018年采用新方法估算了竹林SOS和EOS的时空格局，且SOS在上，下年均表现出明显的空间异质性，而在上一年中的SOS则出现得较早比往年少。共有70.13％的所有像素呈现SOS提前趋势，而超过一半的区域（58.42％）呈现EOS延迟趋势。