|Title||SMOS brightness temperatures angular noise: characterization, filtering and validation|
|Year of Publication||2013|
|Authors||Sabater, JM, De Rosnay, P, Jimenez, C, Isaksen, L, Albergel, C|
|Secondary Title||Technical Memorandum|
The 2D-interferometric radiometer on board SMOS has been providing a continuous dataset of brightness temperatures, at different viewing geometries, containing information of the Earth's surface microwave emission. This dataset is affected by several sources of noise, which are a combination of the noise associated with the radiometer itself and the different views under which a heterogeneous target, such as continental surfaces, is observed. As a result, the SMOS dataset is affected by a significant amount of noise. For many applications, as soil moisture retrieval, reducing noise from the observations while keeping the signal is necessary and the accuracy of the retrievals depends on the quality of the observed dataset. This paper investigates the averaging of SMOS brightness temperatures in angular bins of different size as a simple method to reduce noise. All the observations belonging to a single pixel and satellite overpass were fitted to a polynomial regression model, with the objective of characterizing and evaluating the associated noise. Then the observations were averaged in angular bins of different size and the potential benefit of this process to reduce noise from the data was quantified. It was found that if a 2-degree angular bin is used to average the data, the noise is reduced by up to 3 K. Furthermore, this method complements necessary data thinning approaches when a large volume of data is used in data assimilation systems.