Recent Advances On Soil Moisture Data Assimilation

Abstract

This study reviews recent progress on soil moisture data assimilation. Data assimilation is a process of merging observations with a system dynamic model to provide an improved estimate of the states of the environment. The application of data assimilation in hydrology is relatively new, however, rapid progress has been made in the last decade or so with the available remotely sensed soil moisture data. After briefing the history of soil moisture data assimilation, the review focuses on the most common data assimilation methods and recent progress made in soil moisture data assimilation through a case study of the soil moisture initialization activities for NASA's seasonal and interannual climate prediction. The example demonstrates that soil moisture data assimilation has made great progress in the last decade, however is still in its infancy. Good quality remotely sensed soil moisture data with accurate uncertainty information at continental and global scale are needed to ensure the success of the operational use of soil moisture data assimilation technique. Further advancement on the current soil moisture data assimilation methods is necessary to be able to assimilate multisource hydrological remote sensing data into land surface models for the best use of various remote sensing data sources at continental and global scales.