MODIS NBAR Time Series Modeling With Two Statistical Methods and Application to Leaf Area Index Recursive Estimation

The inconsistent data quality of remote sensing observation, which is largely a result of atmospheric conditions, presents problems in the application of these data. Pixel reflectance in remote sensing observation varies with the type of land cover and the observation time. For land cover types that cycle yearly, such as vegetation, the variations in surface reflectance usually have dynamic periodic characteristics. In this study, we modeled the temporal feature of Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir BRDF-adjusted reflectance (NBAR) time series data of typical forest and cropland areas using two statistical methods: season-trend and seasonal autoregressive (AR) integrated moving average (SARIMA). The fitting values of these models were used in the recursive estimation of leaf area index (LAI) time series based on a nonlinear AR exogenous (NARX) neural network. This suppressed interferences from observational data noise and missing values. The results from 6 years (2008–2013) of MODIS NBAR modeling indicate that the two statistical methods are effective to model the NBAR time series of the vegetation surface; the season-trend model can extract both seasonal and trend components of long time series, and the SARIMA model has a good fitting capacity for general time series data. The NARX neural network performs well with the improved NBAR time series input, and the estimated LAI time series is more continuous than the MODIS LAI. Comparison with field data reveals the reliability of the estimated LAI values.