Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band
Identifieur interne : 005771 ( Main/Exploration ); précédent : 005770; suivant : 005772Wheat Canopy Structure and Surface Roughness Effects on Multiangle Observations at L-Band
Auteurs : Sandy Peischl [Australie] ; Jeffrey P. Walker [Australie] ; Dongryeol Ryu [Australie] ; Yann H. Kerr [France] ; Rocco Panciera [Australie] ; Christoph Rüdiger [Australie]Source :
- IEEE transactions on geoscience and remote sensing [ 0196-2892 ] ; 2012.
Descripteurs français
- Pascal (Inist)
- Wicri :
- geographic : Australie.
- topic : Simulation, Biosphère.
English descriptors
- KwdEn :
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 HR was too low, which was then adjusted as a function of the soil moisture, and 2) the vegetation structure parameters tth and ttv required optimization, yielding new values of tth = 0.2 and ttv = 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.
Affiliations:
- Australie, France
- Midi-Pyrénées, Occitanie (région administrative), Victoria (État)
- Melbourne, Toulouse
- Université de Melbourne
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Walker</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Civil Engineering, Faculty of Engineering, Monash University</s1><s2>Melbourne, Vic. 3800</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>6 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Melbourne, Vic. 3800</wicri:noRegion></affiliation></author><author><name sortKey="Ryu, Dongryeol" sort="Ryu, Dongryeol" uniqKey="Ryu D" first="Dongryeol" last="Ryu">Dongryeol Ryu</name><affiliation wicri:level="4"><inist:fA14 i1="02"><s1>Department of Infrastructure Engineering, Melbourne School of Engineering, The University of Melbourne</s1><s2>Melbourne, Vic. 3010</s2><s3>AUS</s3><sZ>3 aut.</sZ></inist:fA14><country>Australie</country><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author><author><name sortKey="Kerr, Yann H" sort="Kerr, Yann H" uniqKey="Kerr Y" first="Yann H." last="Kerr">Yann H. Kerr</name><affiliation wicri:level="3"><inist:fA14 i1="04"><s1>Centre d'Etudes Spatiales de la Biosphere</s1><s2>31401 Toulouse</s2><s3>FRA</s3><sZ>4 aut.</sZ></inist:fA14><country>France</country><placeName><region type="region" nuts="2">Occitanie (région administrative)</region><region type="old region" nuts="2">Midi-Pyrénées</region><settlement type="city">Toulouse</settlement></placeName></affiliation></author><author><name sortKey="Panciera, Rocco" sort="Panciera, Rocco" uniqKey="Panciera R" first="Rocco" last="Panciera">Rocco Panciera</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>Cooperative Research Centre for Spatial Information</s1><s2>Carlton, Vic. 3053</s2><s3>AUS</s3><sZ>5 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Cooperative Research Centre for Spatial Information</wicri:noRegion></affiliation></author><author><name sortKey="Rudiger, Christoph" sort="Rudiger, Christoph" uniqKey="Rudiger C" first="Christoph" last="Rüdiger">Christoph Rüdiger</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Civil Engineering, Faculty of Engineering, Monash University</s1><s2>Melbourne, Vic. 3800</s2><s3>AUS</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>6 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Melbourne, Vic. 3800</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on geoscience and remote sensing</title><title level="j" type="abbreviated">IEEE trans. geosci. remote sens.</title><idno type="ISSN">0196-2892</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on geoscience and remote sensing</title><title level="j" type="abbreviated">IEEE trans. geosci. remote sens.</title><idno type="ISSN">0196-2892</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Australia</term><term>biosphere</term><term>brightness</term><term>brightness temperature</term><term>calibration</term><term>depth</term><term>errors</term><term>experimental studies</term><term>incidence angle</term><term>inverse problem</term><term>microwaves</term><term>models</term><term>optimization</term><term>prediction</term><term>radiometry</term><term>roughness</term><term>salinity</term><term>simulation</term><term>soil moisture</term><term>soils</term><term>testing</term><term>vegetation</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Rugosité</term><term>Humidité sol</term><term>Salinité</term><term>Problème inverse</term><term>Sol</term><term>Hyperfréquence</term><term>Végétation</term><term>Profondeur</term><term>Température brillance</term><term>Angle incidence</term><term>Etude expérimentale</term><term>Brillance</term><term>Modèle</term><term>Simulation</term><term>Erreur</term><term>Optimisation</term><term>Etalonnage</term><term>Expérimentation</term><term>Prévision</term><term>Biosphère</term><term>Radiométrie</term><term>Australie</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Simulation</term><term>Biosphère</term></keywords></textClass></profileDesc></teiHeader><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><affiliations><list><country><li>Australie</li><li>France</li></country><region><li>Midi-Pyrénées</li><li>Occitanie (région administrative)</li><li>Victoria (État)</li></region><settlement><li>Melbourne</li><li>Toulouse</li></settlement><orgName><li>Université de Melbourne</li></orgName></list><tree><country name="Australie"><noRegion><name sortKey="Peischl, Sandy" sort="Peischl, Sandy" uniqKey="Peischl S" first="Sandy" last="Peischl">Sandy Peischl</name></noRegion><name sortKey="Panciera, Rocco" sort="Panciera, Rocco" uniqKey="Panciera R" first="Rocco" last="Panciera">Rocco Panciera</name><name sortKey="Rudiger, Christoph" sort="Rudiger, Christoph" uniqKey="Rudiger C" first="Christoph" last="Rüdiger">Christoph Rüdiger</name><name sortKey="Ryu, Dongryeol" sort="Ryu, Dongryeol" uniqKey="Ryu D" first="Dongryeol" last="Ryu">Dongryeol Ryu</name><name sortKey="Walker, Jeffrey P" sort="Walker, Jeffrey P" uniqKey="Walker J" first="Jeffrey P." last="Walker">Jeffrey P. Walker</name></country><country name="France"><region name="Occitanie (région administrative)"><name sortKey="Kerr, Yann H" sort="Kerr, Yann H" uniqKey="Kerr Y" first="Yann H." last="Kerr">Yann H. Kerr</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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