Application of Multi-mission Satellite Remote Sensing Data for Land Surface Data Assimilation

The abundance of satellite remotely sensed data in the past few decades has provided a great opportunity to improve land surface models. This study aims to apply terrestrial water storage (TWS) estimated from the Gravity Recovery And Climate Experiment (GRACE) mission as well as soil moisture from Soil Moisture and Ocean Salinity Mission (SMOS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) to calibrate a land surface model via data assimilation. This is done using a dual scheme to separately update the state and parameters using two interactive ensemble Kalman filters (EnKFs). The performance of multivariate data assimilation is evaluated against various independent data over different time periods and different basins. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period. The proposed approach also offers better performance in capturing high-frequency water storage variations imposed by climatic events at various spatiotemporal scales.