Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

作者：Hazuki Arakida, Takemasa Miyoshi, Takeshi Ise, Shin‐ichiro Shima, Shunji Kotsuki · 发表于：Nonlinear processes in geophysics · 年份：2017 · DOI：10.5194/npg-24-553-2017 · 被引用次数：11 · 研究领域：Remote Sensing in Agriculture、Plant Water Relations and Carbon Dynamics、Urban Heat Island Mitigation

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.