[Seminar] Dr. Chang-Hwan Park

New approach for estimating soil moisture and soil organic matter from microwave brightness temperature

Soil moisture (SM) plays a critical role in weather and climate by effecting to atmospheric variables via evapotranspiration. For example, the near-surface air temperature can change by evapotranspiration of surface and root zone soil moisture. Therefore, its correlation with the near surface temperature is usually considered as an effective indicator of the coupling strength between land surface and the atmosphere (Seneviratne et al., 2013) . Especially, soil moisture anomaly in dry regime has been reported as a main cause of the strong land-atmosphere coupling able to trigger droughts and heat waves (Miralles et al., 2011) . Soil moisture also influences precipitation formation and storm tracks via its coupling with the atmosphere (Santanello et al., 2019 ) . Consequently, inaccurate SM in the land-surface-model initialization hinders us from better predictions of extreme climate and weather because of unrealistic land-atmosphere interactions through uncertainties in air temperature, moisture, dynamics, cloud formation and precipitation. Therefore, the data assimilation (DA) of global soil moisture estimation from remote sensing measurements becomes more and more critical in weather and climate prediction. In this seminar, I would like to introduce new microwave radiative transfer model (RTM) required in DA system improved by considering variability of soil organic matter and vegetation scattering albedo. As a result, the improved RTM can provide more accurate soil moisture estimation from SMAP brightness temperature with uncertainty information to DA system. Soil organic matter (SOM) plays also a critical role for the parametrization for surface run-off, infiltration, evapotranspiration and soil respiration in hydrological modeling. It means that more accurate estimates of SOM from remote sensing measurements will allow us to predict more realistic SM and ST ultimately effecting on surface air temperature and relating weather and climate events. We are the novel RTM capable of capturing the unrecognized global variability of SOM with well synchronized with SM from SMAP microwave brightness, which will be key in proper reflection of hydrological impact on climate prediction model. In this seminar, I will provide a preliminary result about whether it is possible to estimate not only soil moisture, but also soil SOM simultaneous from microwave.