Estimation of Vegetation Parameters of Water Cloud Model for Global Soil Moisture Retrieval Using Time-Series L-Band Aquarius Observations

Abstract

Using Aquarius middle beam scatterometer observations, the vegetation parameters of the water cloud model at large scale are estimated and applied to global soil moisture retrieval. Vegetation backscattering is derived using two models: Oh model is used to describe the scattering from bare soil surface, while the water cloud model is implemented to account for the effect of vegetation canopy. The vegetation parameters are estimated by minimizing the deviations between the Aquarius scatterometer observations and backscatter coefficients simulated by the water cloud model. The RMSE is less than 2 dB for both copolarizations and correlation is strong (CC > 0.6) in most areas. The vegetation parameters were used to retrieve global soil moisture from Aquarius radar data. The comparisons with the Aquarius soil moisture product derived from the Aquarius radiometer observations show low ubRMSE (0.06cm 3 /cm 3 ) and strong correlation (CC > 0.6) in most parts of the world. The impact of errors in input parameters of the water cloud model on the vegetation parameter estimation was assessed by using a Monte-Carlo simulation. The algorithm converges to the true values of the parameters when the input data is noise-free or only the radar measurement error is introduced. It was found that the errors in vegetation parameter are sensitive to the errors in input soil moisture. The errors in two vegetation parameters counteract each other to decrease the error of backscattering simulation. This study demonstrates that the water cloud model could be applied to global scatterometer observations to retrieve soil moisture if the vegetation parameters are appropriately set.