SMAP Global Model Calibration Using SMOS Time-Series Observations

Abstract:

Within the suite of SMAP’s standard data products is the Level 2 Passive Soil Moisture Product, which is derived primarily from SMAP’s brightness temperature (TB) observations. The baseline retrieval algorithm uses an established microwave emission model that had been extensively tested in many past field experiments.

One approach to applying the same model at a global scale with SMAP’s TB observations is to use the same calibration coefficients derived from past field experiments and apply them globally. Although this approach is a simplification of reality, it resulted in accurate retrieval in several geographically limited studies. Nevertheless, significant retrieval bias may occur in areas where land cover types had not been considered in past field experiments.

In this work, a time-series global model calibration approach is proposed and evaluated. One year of gridded L-band TB observations from the Soil Moisture and Ocean Salinity (SMOS) mission were used as the primary input. At each land pixel on the SMAP grid, the observed TBs were compared with the simulated TBs according to the model with unknown calibration coefficients to be determined. Because of the time-series nature of the input, the above comparison could be repeated for successive revisit dates as a system of equations until the number of known variables (TBs) exceeds the number of unknown variables (calibration coefficients and/or geophysical retrieval). Global nonlinear optimization techniques were then applied to the equations to solve for the optimal model calibration coefficients for that pixel.

Following global application of this approach, soil moisture estimates were extracted and compared with in-situ ground measurement. The resulting soil moisture estimates were shown to have an accuracy comparable to what was observed in past field experiments, confirming the versatility of this global model calibration approach.