Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Abstract Active radar backscatter ( σ °) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable σ ° and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0–10 cm) soil moisture simulations over the period 2010–2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating σ ° or TB alone improves the average correlation of seasonal anomalies (R an ) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves R an to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves R an from 0.502 to 0.536 and 0.533, respectively for σ ° and TB, whereas the best performance is observed for the joint assimilation (R an  = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

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