Joint Assimilation of GLASS Leaf Area Index and SMAP Soil Moisture in a Land Surface Model at the Global Scale

Satellite observations offer a valid alternative to models for estimating land surface variables. Data assimilation (DA) offers a method to address the limitations, gaps, and uncertainties that characterize both models and satellite products. In this work, satellite-based leaf area index (LAI) and soil moisture products are assimilated in a land surface model, first separately, and then, within a joint assimilation framework using an Ensemble Kalman Filter (EnKF). Specifically, Global Land Surface Satellite (GLASS) LAI and Soil moisture Active Passive (SMAP) soil moisture data are merged with the Noah-MP model on a global scale. Preliminary results based on synthetic experiments have proven that LAI DA alone cannot improve the estimation of surface and root zone soil moisture, whereas the assimilation of SMAP observations has shown promising results to reduce errors in such estimates when compared to ground observations. On the other hand, LAI DA was shown to improve a set of water fluxes and storages, such as canopy water storage, canopy interception evaporation, as well as energy and carbon variables. Thus, the joint assimilation of LAI and soil moisture has the potential to enhance our characterization of the land surface. The efficiency of an EnKF that jointly assimilates GLASS LAI and SMAP soil moisture is investigated globally based on different land cover types, which have shown to impact the performance of the DA system. Validation is performed against a set of ground measurements that include soil temperature, net ecosystem exchange, and gross primary productivity from the FLUXCOM project and the International Soil Moisture Network (ISMN).