Design and prototyping of the SPECTRA simulator architecture

SPECTRA (Surface Processes and Ecosystem Changes through Response Analysis) is a planned spaceborne multiangular hyperspectral and thermal imaging spectrometer in phase A early design led by ESA's earth observation group. Its mission is to describe, understand and model the role of terrestrial vegetation in the global carbon cycle and its response to climate variability. Even though the project has been terminated in November 2005, many results of the phase A studies are considered to be useful as input to future missions. The SPECTRA end-to-end simulator is intended to be used to test different aspects of the SPECTRA mission concept and for tuning the retrieval algorithms as well as assessing their performances. The intention of this ESA-commissioned study was not to build an actually working simulator, but to conceive an architecture for a simulator to be built during phase B of the SPECTRA design, as well as perform a limited validation of this architecture. The software architecture for the future SPECTRA end-to-end simulator has been designed to be modular, flexible and distributed. It consists of a central control unit with associated database, which is controlled and monitored via an internet-accessible web interface, and a flexible number of modules performing the actual calculations. The list of simulator modules currently includes but is not limited to state-of-the-art developments in radiative transfer (Onera), instrument modelling (ESA), atmospheric correction (Onera), and various level 2 algorithms (Alterra). Assimilation models and global carbon flux models are linked to the simulator via the SPECTRA field segment database (RSL and Princeton), for which a high level schema has been defined. The simulator structure has been validated using full end-to-end simulations from ground data to top-of-atmosphere, through the SPECTRA instrument simulator provided by industry, and back again. Test data from the Barrax field site are used for this purpose (University of Valencia)

Vertical variability of near-surface salinity in the tropics: Consequences for L-band radiometer calibration and validation

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An iterative convergence algorithm to retrieve sea surface salinity from SMOS L-band radiometric measurements

4 pages, 1 figureThe European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large scale and climatic studies. It uses an L-band (1400-1427 MHz) microwave interferometric radiometer by aperture synthesis (MIRAS) to measure brightness temperature at the Earth surface at horizontal and vertical polarizations (Th and Tv). These two parameters will be used together to retrieve the geophysical variables. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements, due to the narrow range of ocean brightness temperatures and the strong impact in the measured values of different geophysical parameters (as sea state) other than salinity. Here we present the baseline approach chosen by ESA to retrieve sea surface salinity from MIRAS data, as it has been developed and implemented by the joint team of scientists and engineers responsible for the SMOS ocean salinity level 2 prototype processorThe SMOS Ocean Salinity Level 2 Prototype Processor development is funded by ESA under ESTEC contract No.18933/05/NL/FF. Other contracts and projects supported by ESA and other national agencies are contributing to this development. Spanish authors also acknowledge funding from the National Program on Space through projects ESP2004-00671 and ESP2005-06823-C05. French authors acknowledge funding from the CNES/TOSCA programPeer reviewe

An algorithm to retrieve sea surface salinity from SMOS L-band radiometric measurements

2nd Recent Advances in Quantitative Remote Sensing (RAQRS'II), 24-29 September 2006, Torrent, Valencia, Spain.-- 6 pages, 2 figuresThe European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission, the second of the ESA's Living Planet Program Earth Explorer Opportunity Missions, aims at obtaining global maps of soil moisture and sea surface salinity from space for large scale and climatic studies. This mission, with launch scheduled for early 2008, uses and L-band (1400-1427 MHz protected to human emissions) Microwave Interferometric Radiometer by Aperture Synthesis (MIRAS) to measure brightness temperature at the Earth surface at horizontal Th and vertical Tv polarizations (a fully polarized mode is also implemented and will be tested during the commissioning phase). These radiometric parameters will be be used together to retrieve the two geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean surfaces. The retrieval of salinity is a complex process that requires the knowledge of the other evironmental information and an accurate processing of the radiometer measurements, due to the narrow range of ocean brightness temperatures and the strong impact in the measured values of different gephysical parameters (as sea state) other than salinity. Here we present the baseline approach chosen by ESA to retrieve sea surface salinity from MIRAS data, as it has been developed and implemented by the joint team of scientists and engineers responsible for the SMOS Ocean Salinity Level 2 Prototype ProcessorThe SMOS Ocean Salinity Level 2 Prototype Processor development is funded by ESA under and projects supported by ESA and other national agencies are contributing to this development. Spanish authors also acknowledge funding from the National Program on Space through projects ESP2004-00671 and ESP2005-06823-C05. French authors acknowledge funding from the CNES/TOSCA programPeer reviewe

An iterative convergence algorithm to retrieve sea surface salinity from SMOS L−band radiometric measurements

4 pages, 1 figureThe European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large scale and climatic studies. It uses an L-band (1400-1427 MHz) microwave interferometric radiometer by aperture synthesis (MIRAS) to measure brightness temperature at the Earth surface at horizontal and vertical polarizations (Th and Tv). These two parameters will be used together to retrieve the geophysical variables. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements, due to the narrow range of ocean brightness temperatures and the strong impact in the measured values of different geophysical parameters (as sea state) other than salinity. Here we present the baseline approach chosen by ESA to retrieve sea surface salinity from MIRAS data, as it has been developed and implemented by the joint team of scientists and engineers responsible for the SMOS ocean salinity level 2 prototype processorThe SMOS Ocean Salinity Level 2 Prototype Processor development is funded by ESA under ESTEC contract No.18933/05/NL/FF. Other contracts and projects supported by ESA and other national agencies are contributing to this development. Spanish authors also acknowledge funding from the National Program on Space through projects ESP2004-00671 and ESP2005-06823-C05. French authors acknowledge funding from the CNES/TOSCA programPeer reviewe

Overview of SMOS Level 2 Ocean Salinity processing and first results

International audienceSMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm

SMOS first data analysis for sea surface salinity determination

International audienceSoil Moisture and Ocean Salinity (SMOS), launched on 2 November 2009, is the first satellite mission addressing sea surface salinity (SSS) measurement from space. Its unique payload is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at the L-band frequency. This article presents a summary of SSS retrieval from SMOS observations and shows initial results obtained one year after launch. These results are encouraging, but also indicate that further improvements at various data processing levels are needed and hence are currently under investigation

Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces

Thermal Infra Red (TIR) Remote sensing allow spatializing various land surface temperatures: ensemble brightness, radiometric and aerodynamic temperatures, soil and vegetation temperatures optionally sunlit and shaded, and canopy temperature profile. These are of interest for monitoring vegetated land surface processes: heat and mass exchanges, soil respiration and vegetation physiological activity. TIR remote sensors collect information according to spectral, directional, temporal and spatial dimensions. Inferring temperatures from measurements relies on developing and inverting modeling tools. Simple radiative transfer equations directly link measurements and variables of interest, and can be analytically inverted. Simulation models allow linking radiative regime to measurements. They require indirect inversions by minimizing differences between simulations and observations, or by calibrating simple equations and inductive learning methods. In both cases, inversion consists of solving an ill posed problem, with several parameters to be constrained from few information. Brightness and radiometric temperatures have been inferred by inverting simulation models and simple radiative transfer equations, designed for atmosphere and land surfaces. Obtained accuracies suggest refining the use of spectral and temporal information, rather than innovative approaches. Forthcoming challenge is recovering more elaborated temperatures. Soil and vegetation components can replace aerodynamic temperature, which retrieval seems almost impossible. They can be inferred using multiangular measurements, via simple radiative transfer equations previously parameterized from simulation models. Retrieving sunlit and shaded components or canopy temperature profile requires inverting simulation models. Then, additional difficulties are the influence of thermal regime, and the limitations of spaceborne observations which have to be along track due to the temperature fluctuations. Finally, forefront investigations focus on adequately using TIR information with various spatial resolutions and temporal samplings, to monitor the considered processes with adequate spatial and temporal scales. 10.1 Introduction Using TIR remote sensing for environmental issues have been investigated the last three decades. This is motivated by the potential of the spatialized information for documenting the considered processes within and between the Earth system components: cryosphere [1–2], atmosphere [3–6], oceans [7–9], and land surfaces [10]. For the latter, TIR remote sensing is used to monitor forested areas [11–14], urban areas [15–17], and vegetated areas. We focus here on vegetated areas, natural and cultivated. The monitored processes are related to climatology, meteorology, hydrology and agronomy: (1) radiation, heat and water transfers at the soil–vegetation–atmosphere interface [18–24]; (2) interactions between land surface and atmospheric boundary layer [25]; (3) vegetation physiological processes such as transpiration and water consumption, photosynthetic activity and CO2 uptake, vegetation growth an