Publications

Li, XJ; Du, HQ; Mao, FJ; Zhou, GM; Han, N; Xu, XJ; Liu, YL; Zhu, D; Zheng, JL; Dong, LF; Zhang, M (2019). Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystemsyy. SCIENCE OF THE TOTAL ENVIRONMENT, 694, UNSP 133803.

Abstract
Bamboo forests are an important part of the forest ecosystem, which has strong carbon sequestration potential and plays an important role in the global carbon cycle. As a key parameter for simulating the carbon cycle using forest ecosystem models, the quality of leaf area index (LAI) data has a direct influence on the accuracy of modelling results. Here, we used the particle filter (PF) algorithm and PROSAIL model to assimilate MODIS LAI products, which were then used to drive a boreal ecosystem productivity simulator model to simulate the bamboo forest carbon cycle. The results showed that the relationship between the assimilated and observed LAI values was very significant, with an R-2 of 0.95 and an RMSE of 0.28, greatly improving the precision of MODIS LAI products. The R-2 values for the gross primary productivity (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) simulated by the assimilated LAI values and observed carbon fluxes were 0.65, 0.45 and 0.70, respectively, and the RMSE values were 1.10 g C m(-2) day(-1), 1.00 g C m(-2) day(-1) and 0.35 g C m(-2) day(-1), respectively. Compared with the results of the carbon cycle simulated by nonassimilated LAI, the R-2 values of the GPP, NEE and TER values that were simulated by assimilated LAI increased by 27.5%, 45.2% and 6.1%, and the RMSE values decreased by 29.9%, 23.7% and 22.2%, respectively. Therefore, coupling the PF and PROSAIL models can greatly improve the simulation precision for the large-scale bamboo forest carbon cycle. This study laid the foundation for simulating the carbon cycle over a large-scale bamboo forest based on low-resolution data in the future. (C) 2019 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.scitotenv.2019.133803

ISSN:
0048-9697