Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests

Abstract

Bamboo forests, especially the Moso bamboo forest (MBF) and the Lei bamboo forest (LBF), have a strong carbon sequestration capability and play an important role in the global forest carbon cycle. The leaf area index (LAI) is an important structural parameter for simulating the spatiotemporal pattern of the carbon cycle in bamboo forests. However, current LAI products suffer from substantial noise and errors, and data assimilation methods are the most appropriate way to improve the accuracy of LAI data. In this study, two data assimilation methods (the Dual Ensemble Kalman filter (DEnKF) and Particle filter (PF) methods) were applied to improve the quality of MODIS LAI time-series data, which removed noises and smoothed the results using a locally adjusted cubic-spline capping method for the MBF and LBF during 2014–2015. The method with the highest correlation coefficient (r) and lowest root-mean-square error (RMSE) was used to generate highly accurate LAI products of bamboo forests in Zhejiang Province. The results show that the LAI assimilated using two methods saw greatly reduced fluctuations in the MODIS LAI product for both the MBF and the LBF. The LAI assimilated using DEnKF significantly correlated with the observed LAI, with an r value of 0.90 and 0.95, and an RMSE value of 0.42 and 0.42, for the MBF and the LBF, respectively. The PF algorithm achieved a better accuracy than the DEnKF algorithm, with an average increase in r of 8.78% and an average decrease in the RMSE of 33.33%. Therefore, the PF method was applied for LAI assimilation in Zhejiang Province, and the assimilated LAI of bamboo forests achieved a reasonable spatiotemporal pattern in Zhejiang Province. The PF algorithm greatly improves the accuracy of MODIS LAI products and provides a reliable structural parameter for the large-scale simulation of the carbon cycle in bamboo forest ecosystems.