AustralieFrV1, PascalFrancis, Corpus, bibRecord, 001300

Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band

Identifieur interne : 001300 ( PascalFrancis/Corpus ); précédent : 001299; suivant : 001301

Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band

Auteurs : Sandy Peischl ; Jeffrey P. Walker ; Dongryeol Ryu ; Yann H. Kerr ; Rocco Panciera ; Christoph Rüdiger

Source :

IEEE transactions on geoscience and remote sensing [ 0196-2892 ] ; 2012.

RBID : Pascal:12-0243945

Descripteurs français

Pascal (Inist)
- Rugosité, Humidité sol, Salinité, Problème inverse, Sol, Hyperfréquence, Végétation, Profondeur, Température brillance, Angle incidence, Etude expérimentale, Brillance, Modèle, Simulation, Erreur, Optimisation, Etalonnage, Expérimentation, Prévision, Biosphère, Radiométrie, Australie.

English descriptors

KwdEn :
- Australia, biosphere, brightness, brightness temperature, calibration, depth, errors, experimental studies, incidence angle, inverse problem, microwaves, models, optimization, prediction, radiometry, roughness, salinity, simulation, soil moisture, soils, testing, vegetation.

Abstract

The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m³/m³. Further analysis found the following: 1) The roughness value H_R was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt_h and tt_v required optimization, yielding new values of tt_h = 0.2 and tt_v = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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C01	`01`		`ENG`	@0 The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m³/m³. Further analysis found the following: 1) The roughness value H_R was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt_h and tt_v required optimization, yielding new values of tt_h = 0.2 and tt_v = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.
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Format Inist (serveur)

NO :	PASCAL 12-0243945 INIST
ET :	Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band
AU :	PEISCHL (Sandy); WALKER (Jeffrey P.); RYU (Dongryeol); KERR (Yann H.); PANCIERA (Rocco); RÜDIGER (Christoph); KERR (Yann H.); FONT (Jordi); MARTIN-NEIRA (Manuel); MECKLENBURG (Susanne)
AF :	Department of Civil Engineering, Faculty of Engineering, Monash University/Melbourne, Vic. 3800/Australie (1 aut., 2 aut., 6 aut.); Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne/Melbourne, Vic. 3010/Australie (3 aut.); Cooperative Research Centre for Spatial Information/Carlton, Vic. 3053/Australie (5 aut.); Centre d'Etudes Spatiales de la Biosphere/31401 Toulouse/France (4 aut.); CESBIO (CNES/CNRS/UPS/IRD)/31401 Toulouse/France (1 aut.); Institut de Ciències del Mar (ICM-CSIC)/08003 Barcelona/Espagne (2 aut.); ESA-ESTEC/2200 AG Noordwijk/Pays-Bas (3 aut.); ESA-ESRIN/00044 Frascati/Italie (4 aut.)
DT :	Publication en série; Niveau analytique
SO :	IEEE transactions on geoscience and remote sensing; ISSN 0196-2892; Coden IGRSD2; Etats-Unis; Da. 2012; Vol. 50; No. 5 p. 1; Pp. 1498-1506; Bibl. 33 ref.
LA :	Anglais
EA :	The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m³/m³. Further analysis found the following: 1) The roughness value H_R was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt_h and tt_v required optimization, yielding new values of tt_h = 0.2 and tt_v = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.
CC :	001E01M04; 225B04
FD :	Rugosité; Humidité sol; Salinité; Problème inverse; Sol; Hyperfréquence; Végétation; Profondeur; Température brillance; Angle incidence; Etude expérimentale; Brillance; Modèle; Simulation; Erreur; Optimisation; Etalonnage; Expérimentation; Prévision; Biosphère; Radiométrie; Australie
FG :	Australasie
ED :	roughness; soil moisture; salinity; inverse problem; soils; microwaves; vegetation; depth; brightness temperature; incidence angle; experimental studies; brightness; models; simulation; errors; optimization; calibration; testing; prediction; biosphere; radiometry; Australia
EG :	Australasia
SD :	Rugosidad; Humedad suelo; Salinidad; Problema inverso; Suelo; Vegetación; Profundidad; Modelo; Simulación; Error; Optimización; Contraste; Previsión; Biosfera; Australia
LO :	INIST-222H5.354000506948360120
ID :	12-0243945

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Pascal:12-0243945

Le document en format XML

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<keywords scheme="Pascal" xml:lang="fr"><term>Rugosité</term>
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<front><div type="abstract" xml:lang="en">The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m<sup>3</sup>
/m<sup>3</sup>
. Further analysis found the following: 1) The roughness value H<sub>R</sub>
 was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt<sub>h</sub>
 and tt<sub>v</sub>
 required optimization, yielding new values of tt<sub>h</sub>
 = 0.2 and tt<sub>v</sub>
 = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.</div>
</front>
</TEI>
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<fA05><s2>50</s2>
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<fA06><s2>5</s2>
<s3>p. 1</s3>
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<fA08 i1="01" i2="1" l="ENG"><s1>Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band</s1>
</fA08>
<fA09 i1="01" i2="1" l="ENG"><s1>Special Issue on ESA's Soil Moisture and Ocean Salinity Mission (SMOS)</s1>
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<s9>ed.</s9>
</fA12>
<fA12 i1="02" i2="1"><s1>FONT (Jordi)</s1>
<s9>ed.</s9>
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<fA12 i1="03" i2="1"><s1>MARTIN-NEIRA (Manuel)</s1>
<s9>ed.</s9>
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<fA12 i1="04" i2="1"><s1>MECKLENBURG (Susanne)</s1>
<s9>ed.</s9>
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<sZ>2 aut.</sZ>
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</fA14>
<fA14 i1="03"><s1>Cooperative Research Centre for Spatial Information</s1>
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<fA14 i1="04"><s1>Centre d'Etudes Spatiales de la Biosphere</s1>
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<sZ>4 aut.</sZ>
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<fA15 i1="01"><s1>CESBIO (CNES/CNRS/UPS/IRD)</s1>
<s2>31401 Toulouse</s2>
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<fA15 i1="02"><s1>Institut de Ciències del Mar (ICM-CSIC)</s1>
<s2>08003 Barcelona</s2>
<s3>ESP</s3>
<sZ>2 aut.</sZ>
</fA15>
<fA15 i1="03"><s1>ESA-ESTEC</s1>
<s2>2200 AG Noordwijk</s2>
<s3>NLD</s3>
<sZ>3 aut.</sZ>
</fA15>
<fA15 i1="04"><s1>ESA-ESRIN</s1>
<s2>00044 Frascati</s2>
<s3>ITA</s3>
<sZ>4 aut.</sZ>
</fA15>
<fA20><s1>1498-1506</s1>
</fA20>
<fA21><s1>2012</s1>
</fA21>
<fA23 i1="01"><s0>ENG</s0>
</fA23>
<fA43 i1="01"><s1>INIST</s1>
<s2>222H5</s2>
<s5>354000506948360120</s5>
</fA43>
<fA44><s0>0000</s0>
<s1>© 2012 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45><s0>33 ref.</s0>
</fA45>
<fA47 i1="01" i2="1"><s0>12-0243945</s0>
</fA47>
<fA60><s1>P</s1>
</fA60>
<fA61><s0>A</s0>
</fA61>
<fA64 i1="01" i2="1"><s0>IEEE transactions on geoscience and remote sensing</s0>
</fA64>
<fA66 i1="01"><s0>USA</s0>
</fA66>
<fC01 i1="01" l="ENG"><s0>The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m<sup>3</sup>
/m<sup>3</sup>
. Further analysis found the following: 1) The roughness value H<sub>R</sub>
 was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt<sub>h</sub>
 and tt<sub>v</sub>
 required optimization, yielding new values of tt<sub>h</sub>
 = 0.2 and tt<sub>v</sub>
 = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.</s0>
</fC01>
<fC02 i1="01" i2="2"><s0>001E01M04</s0>
</fC02>
<fC02 i1="02" i2="2"><s0>225B04</s0>
</fC02>
<fC03 i1="01" i2="2" l="FRE"><s0>Rugosité</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="ENG"><s0>roughness</s0>
<s5>01</s5>
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<s5>01</s5>
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<s5>02</s5>
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<s5>02</s5>
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<s5>02</s5>
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<fC03 i1="03" i2="2" l="FRE"><s0>Salinité</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="ENG"><s0>salinity</s0>
<s5>03</s5>
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<fC03 i1="03" i2="2" l="SPA"><s0>Salinidad</s0>
<s5>03</s5>
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<fC03 i1="04" i2="2" l="FRE"><s0>Problème inverse</s0>
<s5>04</s5>
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<fC03 i1="04" i2="2" l="ENG"><s0>inverse problem</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="SPA"><s0>Problema inverso</s0>
<s5>04</s5>
</fC03>
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<s2>NT</s2>
<s5>05</s5>
</fC03>
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<s2>NT</s2>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="SPA"><s0>Suelo</s0>
<s2>NT</s2>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="2" l="FRE"><s0>Hyperfréquence</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="2" l="ENG"><s0>microwaves</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="2" l="FRE"><s0>Végétation</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="2" l="ENG"><s0>vegetation</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="2" l="SPA"><s0>Vegetación</s0>
<s5>07</s5>
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<s5>08</s5>
</fC03>
<fC03 i1="08" i2="2" l="ENG"><s0>depth</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="2" l="SPA"><s0>Profundidad</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="2" l="FRE"><s0>Température brillance</s0>
<s5>09</s5>
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<fC03 i1="09" i2="2" l="ENG"><s0>brightness temperature</s0>
<s5>09</s5>
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<fC03 i1="10" i2="2" l="FRE"><s0>Angle incidence</s0>
<s5>10</s5>
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<fC03 i1="10" i2="2" l="ENG"><s0>incidence angle</s0>
<s5>10</s5>
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<s5>11</s5>
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<s5>11</s5>
</fC03>
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<s5>12</s5>
</fC03>
<fC03 i1="12" i2="2" l="ENG"><s0>brightness</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="2" l="FRE"><s0>Modèle</s0>
<s5>13</s5>
</fC03>
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<s5>13</s5>
</fC03>
<fC03 i1="13" i2="2" l="SPA"><s0>Modelo</s0>
<s5>13</s5>
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<s5>14</s5>
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</fC03>
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<s5>14</s5>
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<fC03 i1="15" i2="2" l="FRE"><s0>Erreur</s0>
<s5>15</s5>
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<fC03 i1="15" i2="2" l="ENG"><s0>errors</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="2" l="SPA"><s0>Error</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="2" l="FRE"><s0>Optimisation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="2" l="ENG"><s0>optimization</s0>
<s5>16</s5>
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<s5>16</s5>
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<s5>17</s5>
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<s5>18</s5>
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<s5>19</s5>
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<s5>19</s5>
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<s5>20</s5>
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<fC03 i1="20" i2="2" l="ENG"><s0>biosphere</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="2" l="SPA"><s0>Biosfera</s0>
<s5>20</s5>
</fC03>
<fC03 i1="21" i2="2" l="FRE"><s0>Radiométrie</s0>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="2" l="ENG"><s0>radiometry</s0>
<s5>21</s5>
</fC03>
<fC03 i1="22" i2="2" l="FRE"><s0>Australie</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="22" i2="2" l="ENG"><s0>Australia</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="22" i2="2" l="SPA"><s0>Australia</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC07 i1="01" i2="2" l="FRE"><s0>Australasie</s0>
</fC07>
<fC07 i1="01" i2="2" l="ENG"><s0>Australasia</s0>
</fC07>
<fC07 i1="01" i2="2" l="SPA"><s0>Australasia</s0>
</fC07>
<fN21><s1>184</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
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<server><NO>PASCAL 12-0243945 INIST</NO>
<ET>Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band</ET>
<AU>PEISCHL (Sandy); WALKER (Jeffrey P.); RYU (Dongryeol); KERR (Yann H.); PANCIERA (Rocco); RÜDIGER (Christoph); KERR (Yann H.); FONT (Jordi); MARTIN-NEIRA (Manuel); MECKLENBURG (Susanne)</AU>
<AF>Department of Civil Engineering, Faculty of Engineering, Monash University/Melbourne, Vic. 3800/Australie (1 aut., 2 aut., 6 aut.); Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne/Melbourne, Vic. 3010/Australie (3 aut.); Cooperative Research Centre for Spatial Information/Carlton, Vic. 3053/Australie (5 aut.); Centre d'Etudes Spatiales de la Biosphere/31401 Toulouse/France (4 aut.); CESBIO (CNES/CNRS/UPS/IRD)/31401 Toulouse/France (1 aut.); Institut de Ciències del Mar (ICM-CSIC)/08003 Barcelona/Espagne (2 aut.); ESA-ESTEC/2200 AG Noordwijk/Pays-Bas (3 aut.); ESA-ESRIN/00044 Frascati/Italie (4 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>IEEE transactions on geoscience and remote sensing; ISSN 0196-2892; Coden IGRSD2; Etats-Unis; Da. 2012; Vol. 50; No. 5 p. 1; Pp. 1498-1506; Bibl. 33 ref.</SO>
<LA>Anglais</LA>
<EA>The multiangle observation capability of the Soil Moisture and Ocean Salinity mission is expected to significantly improve the inversion of soil microwave emissions for soil moisture, by enabling the simultaneous retrieval of the vegetation optical depth and other surface parameters. Consequently, this paper investigates the relationship between soil moisture and brightness temperature at multiple incidence angles using airborne L-band data from the National Airborne Field Experiment in Australia in 2005. A forward radio brightness model was used to predict the passive microwave response at a range of incidence angles, given the following inputs: 1) ground-measured soil and vegetation properties and 2) default model parameters for vegetation and roughness characterization. Simulations were made across various dates and locations with wheat cover and evaluated against the available airborne observations. The comparison showed a significant underestimation of the measured brightness temperatures by the model. This discrepancy subsequently led to soil moisture retrieval errors of up to 0.3 m<sup>3</sup>
/m<sup>3</sup>
. Further analysis found the following: 1) The roughness value H<sub>R</sub>
 was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tt<sub>h</sub>
 and tt<sub>v</sub>
 required optimization, yielding new values of tt<sub>h</sub>
 = 0.2 and tt<sub>v</sub>
 = 1.4 from calibration to a single flight. Testing the optimized parameterization for different moisture conditions and locations found that the root-mean-square simulation error between the forward model predictions and the airborne observations was improved from 31.3 K (26.5 K) to 2.3 K (5.3 K) for wet (dry) soil moisture condition.</EA>
<CC>001E01M04; 225B04</CC>
<FD>Rugosité; Humidité sol; Salinité; Problème inverse; Sol; Hyperfréquence; Végétation; Profondeur; Température brillance; Angle incidence; Etude expérimentale; Brillance; Modèle; Simulation; Erreur; Optimisation; Etalonnage; Expérimentation; Prévision; Biosphère; Radiométrie; Australie</FD>
<FG>Australasie</FG>
<ED>roughness; soil moisture; salinity; inverse problem; soils; microwaves; vegetation; depth; brightness temperature; incidence angle; experimental studies; brightness; models; simulation; errors; optimization; calibration; testing; prediction; biosphere; radiometry; Australia</ED>
<EG>Australasia</EG>
<SD>Rugosidad; Humedad suelo; Salinidad; Problema inverso; Suelo; Vegetación; Profundidad; Modelo; Simulación; Error; Optimización; Contraste; Previsión; Biosfera; Australia</SD>
<LO>INIST-222H5.354000506948360120</LO>
<ID>12-0243945</ID>
</server>
</inist>
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Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band

Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band

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