The SMOS Soil Moisture Retrieval Algorithm

International audienceThe Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5$^{circ}$, flags and quality indices, and other parameters o- interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises