Modeling approaches to assimilating L band passive microwave observations over land surfaces

Abstract

L band passive microwave remotely sensed data have great potential for providing estimates of soil moisture with high temporal sampling and on a regional scale. Several studies have shown the possibility of assessing the hydrological conditions deep down in soil (in the top 1 or 2 m) from these repetitive estimates of surface soil moisture. Water availability for plants, which is related to soil moisture in the root zone, is a key variable for estimating the evapotranspiration fluxes over land surfaces. This estimation is an important issue for meteorological and hydrological modeling, since it is a basic term of land surface forcing in mesoscale atmospheric circulations. However, at the present time the assimilation approach of remotely sensed brightness temperature data for operational use in the fields of meteorology and hydrology is poorly defined and important issues remain to be addressed in order to develop an operational assimilation approach. Two important issues are to identify (1) how vegetation variables describing vegetation development can be accounted for and (2) how the attenuation effects of L band microwave radiation within the canopy layer can be computed on large spatial scales. On the basis of an exhaustive data set including multiangular and dual‐polarization passive microwave measurements acquired over a wheat crop during a 3‐month period in 1993, two main modeling approaches are tested in this study. The principle of both approaches was based on the use of dual‐polarization and multiangular observations to discriminate between the effects of soil and vegetation on the crop microwave signature. For the two approaches, both the initial soil water reservoir R2 (at the beginning of the crop development) and parameterizations of the crop development could be retrieved simultaneously from the assimilation of the passive microwave measurements. From these results, promising assimilation strategies can be expected from the multiangular Soil Moisture and Ocean Salinity (SMOS) observations made over the land surface.