A Method for Consistent Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data

Most methods for generating global land surface products from satellite data are parameter specific and do not use multiple temporal observations, which often results in spatial and temporal discontinuity and physical inconsistency among different products. This paper proposes a data assimilation (DA) scheme to simultaneously estimate five land surface parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) time series reflectance data under clear and cloudy conditions. A coupled land surface-atmosphere radiative transfer model is developed to simulate TOA reflectance, and an ensemble Kalman filter technique is used to retrieve the most influential surface parameters of the coupled model, such as leaf area index, by combining predictions from dynamic models and the MODIS TOA reflectance data whether under clear or cloudy conditions. Then, the retrieved surface parameters are input to the coupled model to calculate four other parameters: 1) land surface reflectance; 2) incident photosynthetically active radiation (PAR); 3) land surface albedo; and 4) the fraction of absorbed PAR (FAPAR). The estimated parameters are compared with those of the corresponding MODIS, the Global LAnd Surface Satellite, and the Geoland2/BioPar version 1 (GEOV1) products. Validation of the estimated parameters against ground measurements from several sites with different vegetation types demonstrates that this method can estimate temporally complete land surface parameter profiles from MODIS TOA time series reflectance data, with accuracy comparable to that of existing satellite products over the selected sites. The retrieved leaf area index profiles are smoother than the existing satellite products, and unlike the MOD09GA product, the retrieved surface reflectance values do not have the high peak values influenced by clouds. The use of the coupled land surface-atmosphere model and the DA technique ensures physical connections between the land surface parameters and makes it possible to calculate radiation-related parameters for clear and cloudy atmospheric conditions, which is an improvement for FAPAR retrieval compared with the MODIS and GEOV1 products. The retrieved FAPAR and PAR values can reveal the significant differences in them under clear and cloudy atmospheric conditions.