Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis

A study is performed to analyze the daily retrieval of soil moisture from the synergy of active and passive microwave data using observations from the ASCAT scatterometer and AMSR-E radiometer. The objective is to identify the information provided by each sensor and to analyze preprocessing methods - such as the day/night average, diurnal difference and microwave polarization difference index for AMSR-E and the incidence angle normalization and backscatter temporal index for ASCAT - to maximize the amount of soil moisture information extracted. Additionally, the data fusion and a posteriori synergy methodologies are compared to determine how to optimally exploit this combined information. This study is performed using a neural network (NN) to estimate soil moisture from a set of AMSR-E and ASCAT observations. ERA-interim/Land surface soil moisture fields are used to train the NN as well as to evaluate the performance of the different retrieval input datasets. It is shown that using the AMSR-E 7 GHz, 11 GHz, 19 GHz and 37 GHz channels with the three preprocessing methods highlights various surface contributions in the signal, in particular the soil moisture and surface temperature information, and greatly helps the retrieval in disentangling them. For ASCAT, the synergy effect is less significant and data preprocessed with the two methods analyzed yields very similar information. The information provided by the active and passive microwave sensors is found to be very complementary, such that a soil moisture retrieval using the combined active and passive information shows a significant improvement between 5% and 19% in the temporal correlation and a reduction of the retrieval uncertainty by 7%. The improvement in the spatial structure is smaller with a correlation increase of 2%. It is demonstrated that the choice of synergy method strongly impacts the retrieval improvement that can be achieved. Data fusion methods are shown to be better suited than a posteriori combination methods, due to their ability to exploit information complementarity. These results could help improve future active/passive soil moisture retrievals, such as from the Soil Moisture Active/Passive (SMAP) mission, through the application of similar preprocessing and synergy methods in order to better extract the soil moisture information provided.