The SMOS level2 soil moisture algorithm. Principles and future trends
FERRAZZOLI, Paolo
Università degli Studi di Roma Tor Vergata [Roma, Italia] = University of Rome Tor Vergata [Rome, Italy] = Université de Rome Tor Vergata [Rome, Italie]
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Università degli Studi di Roma Tor Vergata [Roma, Italia] = University of Rome Tor Vergata [Rome, Italy] = Université de Rome Tor Vergata [Rome, Italie]
Language
en
Communication dans un congrès
This item was published in
2. SMOS Science Conference, 2015-05-25, Madrid. 2015
English Abstract
The Soil Moisture and Ocean Salinity (SMOS) mission is ESA’s (European Space Agency) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint programme between ESA CNES (Centre National d’Etudes ...Read more >
The Soil Moisture and Ocean Salinity (SMOS) mission is ESA’s (European Space Agency) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint programme between ESA CNES (Centre National d’Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-Band 2D interferometric radiometer in the 1400-1427 MHz protected band. The goal of the level 2 algorithm is to deliver global soil moisture 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. In this context, a group of institutes prepared the soil moisture Algorithm Theoretical Basis document (ATBD) to be used to produce the operational algorithm. The principle of the soil moisture 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 modelled brightness temperature (TB) data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g. soil moisture (SM) and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains soil moisture, vegetation opacity, and estimated dielectric constant of any surface, brightness temperatures computed at 42.5°, flags and quality indices, and other parameters of interest. Based on recent study and analysis (see paper by JP Wigneron on LMEB, P Ferrazzoli on forest etc.. and results acquired since launch we have revisited the algorithm(flags, retrievals outputs, aux data). The goal of this presentation is to give an update on the algorithm changes and trends. Validation will be presented in other papers.Read less <
Origin
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