Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval

In microwave remote sensing of the Earth's surface, the satellite signal holds information on both soil moisture and vegetation. This necessitates a correction for vegetation effects when retrieving soil moisture. This paper assesses the strengths and weaknesses of the existing vegetation correction as part of the Vienna University of Technology (TU-Wien) method for soil moisture retrieval from coarse-scale active microwave observations. In this method, vegetation is based on a multiyear climatology of backscatter variations related to phenology. To assess the plausibility of the correction method, we first convert the correction terms for retrievals from the Advanced Scatterometer (ASCAT) into estimates of vegetation optical depth τ_a using a water-cloud model. The spatial and temporal behaviors of the newly developed τ_a are compared with the optical depth retrieved from passive microwave observations with the land parameter retrieval model τ_p. Spatial patterns correspond well, although low values for τ_a are found over boreal forests. Temporal correlation between the two products is high (R = 0.5), although negative correlations are observed in drylands. This comparison shows that τ_a and thus the vegetation correction method are sensitive to vegetation dynamics. Effects of the vegetation correction on soil moisture retrievals are investigated by comparing retrieved soil moisture before and after applying the correction term to modeled soil moisture. The vegetation correction increases the quality of the soil moisture product. In areas of high interannual variability in vegetation dynamics, we observed a negative impact of the vegetation correction on the soil moisture, with a decrease in correlation up to 0.4. It emphasizes the need for a dynamic vegetation correction in areas with high interannual variability.