Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data

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.