A Data Assimilation Method for Simultaneously Estimating the Multiscale Leaf Area Index From Time-Series Multi-Resolution Satellite Observations

Abstract

Current global leaf area index (LAI) products are generally produced from single-temporal satellite observations acquired by a single sensor. These LAI products are usually spatiotemporally discontinuous and inaccurate for some vegetation types in many areas, which limit the applications of these LAI products to the understanding of land dynamics. In this paper, a new data assimilation method was proposed to estimate multiscale and temporally continuous LAI values from multi-sensor time-series satellite observations with different spatial resolutions. An ensemble multiscale tree (EnMsT) was used to establish the conversion relationships between different spatial resolution LAI values, and dynamic models of the LAI at different spatial scales were constructed to evolve LAI at the corresponding spatial scales over time. At each time step, a multiscale Kalman filter (MKF) was introduced to fuse the predicted LAI values from the dynamic models at different spatial scales and to construct a forecasted EnMsT. When satellite observations were available, an ensemble multiscale filter (EnMsF) technique was applied to update the LAI values at each node of the EnMsT. The method was applied to estimate temporally continuous multiscale LAI values from the time series of Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data at several sites with different vegetation types. The estimated multiscale LAI values were compared with the MODIS and GEOV2 LAI products, and the reference LAI values at the corresponding scales aggregated from the high-resolution LAI surface images. The estimated LAI values with the finest spatial resolution were also validated by ground measurements from the selected sites. The results show that the new method is able to simultaneously estimate temporally continuous multiscale LAI values by assimilating satellite observations with different spatial resolutions, and the estimated multiscale LAI values are well consistent with the reference LAI values at the corresponding scales over the selected sites. The root-mean-square error (RMSE) and coefficient of determination of the retrieved LAI values at the finest spatial scale against the ground measurements over the selected sites are 0.539 and 0.788, respectively.