A Simple Data Assimilation Method for Improving Estimation of MODIS LAI Time-series Data Products Based on the 2-Dimensional LMS Adaptive Filter

Abstract

Leaf area index (LAI) is an important parameter for describing vegetation canopy structure in the terrestrial ecosystem on the global, continental and regional scales. In this paper, a simple data assimilation method for improving estimation of MODIS LAI time-series data products based on a new 2-D LMS (two-dimensional least mean square) adaptive filter was proposed. Firstly, The new 2-D LMS adaptive filter algorithm is introduced and analyzed. Secondly, A simple data assimilation method for improving estimation of MODIS LAI time-series data products based on the new 2-D LMS adaptive filter and quality control data of MODIS LAI is proposed. Finally, the experiments are performed based on the simple data assimilation method using MODIS LAI data products from 2000 to 2005 of southwestern China.