Analysis and prediction of MODIS LAI time series with Dynamic Harmonic Regression model
运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测

Leaf Area Index (LAI) is one of the most important parameters in describing the dynamics of vegetation on land surfaces. LAI products have been produced from data of many remote sensing satellite sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we used the Dynamic Harmonic Regression (DHR) model to analyze the LAI time series products. The model can decompose the trend, seasonal and residuals components from the original time series, and predict the short-time LAI values. We use the DHR model to extract the time change information from the MODIS LAI time series products. The results show this method to be very effective in predicting the short-term LAI on the pixel basis.