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Soil moisture retrieval algorithms: the SMOS case

hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorKERR, Yann H.
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorMAHMOODI, Ali
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorMIALON, Arnaud
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorAL BILTAR, Ali
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRODRIGUEZ‐FERNANDEZ, Nemesio
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorRICHAUME, Philippe
hal.structure.identifierCentre d'études spatiales de la biosphère [CESBIO]
dc.contributor.authorCABOT, François
hal.structure.identifierInteractions Sol Plante Atmosphère [UMR ISPA]
dc.contributor.authorWIGNERON, Jean-Pierre
hal.structure.identifierLaboratoire Atmosphères, Milieux, Observations Spatiales [LATMOS]
dc.contributor.authorWALDTEUFEL, Philippe
hal.structure.identifierUniversità degli Studi di Roma Tor Vergata [Roma, Italia] = University of Rome Tor Vergata [Rome, Italy] = Université de Rome Tor Vergata [Rome, Italie]
dc.contributor.authorFERRAZZOLI, Paolo
hal.structure.identifierSwiss Federal Institute for Forest, Snow and Landscape Research WSL
dc.contributor.authorSCHWANK, M.
hal.structure.identifierEuropean Space Research Institute [ESRIN]
dc.contributor.authorDELWART, Steven
dc.contributor.editorShunlin Liang (eds.)
dc.date.accessioned2024-04-08T11:56:26Z
dc.date.available2024-04-08T11:56:26Z
dc.date.issued2018
dc.identifier.isbn9780128032213
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/195755
dc.description.abstractEnAfter the successful acquisition by a coarse L-band radiometer on board Skylab in the early seventies, the potential of L-band radiometry was made clear in spite of a strict limitation linked to minimum antenna dimensions required for appropriate spatial resolution. More than 20 years later new antenna concepts emerged to mitigate this physical constraint. The first to emerge, in 1997, and to become a reality, was the Soil Moisture and Ocean Salinity (SMOS) mission (Kerr, 1997, Kerr, 1998). It is European Space Agency’s (ESA’s) second Earth Explorer Opportunity mission (Kerr et al., 2001), launched in November 2009. It is a joint program 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. This wavelength penetrates well through the atmosphere, and hence, the instrument probes the Earth surface emissivity from space. Surface emissivity can 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.Soil moisture retrieval from SMOS observations with a required accuracy of 0.04 m3/m3 is challenging and involves many steps. The retrieval algorithms are developed and implemented in the ground segment, which processes level 1 and level 2 data. Level 1 consists mainly of directional brightness temperatures, while level 2 consists of geophysical products in swath mode, i.e., for successive imaging snapshots acquired by the sensor during a half orbit from pole to pole. Level 3 consists in composites of brightness temperatures, or geophysical products over time and space, i.e., global maps over given temporal periods from 1 day to 1 month. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis Documents (ATBD), used to in operational soil moisture and sea salinity retrieval algorithms (Kerr et al., 2010a).The principle of the level 2 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 modeled brightness temperature (TB) at horizontal and vertical polarizations, for a variety of incidence angles. The algorithm finds the best set of parameters, e.g., soil moisture (SM) and vegetation characteristics, which drive the TB model and minimizes the cost function. From this algorithm, a more sophisticated one was developed to take into account multiorbit retrievals (i.e., level 3). Subsequently, after several years of data acquisition and algorithm improvements, a neural network approach was developed so as to be able to infer soil moisture fields in near-real time. In parallel, several simplified algorithms were tested, the goal being to achieve a seamless transition with other sensors, along with other studies targeted on specific targets such as dense forests, organic rich soils, or frozen and snow-covered grounds. Finally, it may be noted that most of these approaches deliver not only the surface soil moisture but also other quantities of interest such as vegetation optical depth, surface roughness, and surface dielectric constant. The goal of this article is to give an overview of these different approaches and corresponding results and adequate references for those wishing to go further. Sea surface salinity is not covered in this article, while the focus is SMOS data.
dc.language.isoen
dc.publisherElsevier
dc.publisher.locationOxford (united kingdom)
dc.source.titleComprehensive Remote Sensing
dc.subject.enL-band
dc.subject.enMicrowave radiometer
dc.subject.enSMOS
dc.subject.enSoil moisture
dc.title.enSoil moisture retrieval algorithms: the SMOS case
dc.typeChapitre d'ouvrage
dc.identifier.doi10.1016/B978-0-12-409548-9.10355-0
dc.subject.halPlanète et Univers [physics]/Océan, Atmosphère
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Hydrologie
bordeaux.page156-190
bordeaux.volume4
bordeaux.hal.laboratoriesInteractions Soil Plant Atmosphere (ISPA) - UMR 1391*
bordeaux.institutionBordeaux Sciences Agro
bordeaux.institutionINRAE
bordeaux.title.proceedingComprehensive Remote Sensing
hal.identifierhal-02789851
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02789851v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Comprehensive%20Remote%20Sensing&rft.date=2018&rft.volume=4&rft.spage=156-190&rft.epage=156-190&rft.au=KERR,%20Yann%20H.&MAHMOODI,%20Ali&MIALON,%20Arnaud&AL%20BILTAR,%20Ali&RODRIGUEZ%E2%80%90FERNANDEZ,%20Nemesio&rft.isbn=9780128032213&rft.genre=unknown


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