Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data

- We proposed an iterative method to improve the estimation of LAI climatology.
- We incorporate MODIS, VEGETATION and MISR data to provide better LAI estimation.
- We test the performance of temporal, spectral and angular information in the retrieval.

The leaf area index (LAI) is one of the most critical structural parameters of the vegetation canopy in regional and global biogeochemical, ecological, and meteorological applications. Data gaps and spatial and temporal inconsistencies exist in most of the existing global LAI products derived from single-satellite data because of their limited information content. Furthermore, the accuracy of current LAI products may not meet the requirements of certain applications. Therefore, LAI retrieval from multiple satellite data is becoming popular. An existing LAI inversion scheme using the ensemble Kalman filter (EnKF) technique is further extended in this study to integrate temporal, spectral, and angular information from Moderate Resolution Imaging Spectroradiometer (MODIS), SPOT/VEGETATION, and Multi-angle Imaging Spectroradiometer (MISR) data. The recursive update of LAI climatology with the retrieved LAI and the coupling of a canopy radiative-transfer model and a dynamic process model using the EnKF technique can fill in missing data and produce a consistent accurate time-series LAI product. During each iteration, we defined a 5 ∗ 1 sliding window and compared the RMSEs in the selected window to determine the minimum. Validation results at six sites demonstrate that the combination of temporal information from multiple sensors, spectral information provided by red and near-infrared (NIR) bands, and angular information from MISR bidirectional reflectance factor (BRF) data can provide a more accurate estimate of LAI than previously available.