Discussion:Arlon/10-0391942
De Wicri Luxembourg
<record>
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<pA>
<fA01 i1="01" i2="1">
<s0>0016-7061</s0>
</fA01>
<fA02 i1="01">
<s0>GEDMAB</s0>
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<fA03 i2="1">
<s0>Geoderma : (Amst.)</s0>
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<fA05>
<s2>158</s2>
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<fA06>
<s2>1-2</s2>
</fA06>
<fA08 i1="01" i2="1" l="ENG">
<s1>Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy</s1>
</fA08>
<fA09 i1="01" i2="1" l="ENG">
<s1>Diffuse reflectance spectroscopy in soil science and land resource assessment</s1>
</fA09>
<fA11 i1="01" i2="1">
<s1>STEVENS (Antoine)</s1>
</fA11>
<fA11 i1="02" i2="1">
<s1>UDELHOVEN (Thomas)</s1>
</fA11>
<fA11 i1="03" i2="1">
<s1>DENIS (Antoine)</s1>
</fA11>
<fA11 i1="04" i2="1">
<s1>TYCHON (Bernard)</s1>
</fA11>
<fA11 i1="05" i2="1">
<s1>LIOY (Rocco)</s1>
</fA11>
<fA11 i1="06" i2="1">
<s1>HOFFMANN (Lucien)</s1>
</fA11>
<fA11 i1="07" i2="1">
<s1>VAN WESEMAEL (Bas)</s1>
</fA11>
<fA12 i1="01" i2="1">
<s1>GUERRERO (César)</s1>
<s9>ed.</s9>
</fA12>
<fA12 i1="02" i2="1">
<s1>VISCARRA ROSSEL (Raphael A.)</s1>
<s9>ed.</s9>
</fA12>
<fA12 i1="03" i2="1">
<s1>MOUAZEN (Abdul Mounem)</s1>
<s9>ed.</s9>
</fA12>
<fA14 i1="01">
<s1>Department of Geography, Université catholique de Louvain, 3 place Pasteur</s1>
<s2>1348 Louvain-La-Neuve</s2>
<s3>BEL</s3>
<sZ>1 aut.</sZ>
<sZ>7 aut.</sZ>
</fA14>
<fA14 i1="02">
<s1>Departement Environment and Agro-biotechnologies, Centre de Recherche Public-Gabriel Lippmann, 41 rue du Brill</s1>
<s2>4422 Belvaux</s2>
<s3>LUX</s3>
<sZ>2 aut.</sZ>
<sZ>6 aut.</sZ>
</fA14>
<fA14 i1="03">
<s1>Department of Environmental Sciences and Management, University of Liege, Campus of Arlon, 185 avenue de Longwy</s1>
<s2>6700 Arlon</s2>
<s3>BEL</s3>
<sZ>3 aut.</sZ>
<sZ>4 aut.</sZ>
</fA14>
<fA14 i1="04">
<s1>CONVIS Herdbuch Service Élevage et Génétique-Société coopérative, B.P. 313</s1>
<s2>9004 Ettelbruck</s2>
<s3>LUX</s3>
<sZ>5 aut.</sZ>
</fA14>
<fA15 i1="01">
<s1>Department of Agrochemistry and Environment, University Miguel Hernández, Avenida de la Universidad s/n</s1>
<s2>Elche, Alicante 3202</s2>
<s3>ESP</s3>
<sZ>1 aut.</sZ>
</fA15>
<fA15 i1="02">
<s1>CSIRO Land and Water, Bruce E. Butler Laboratory, GPO Box 1666</s1>
<s2>Canberra, ACT 2601</s2>
<s3>AUS</s3>
<sZ>2 aut.</sZ>
</fA15>
<fA15 i1="03">
<s1>National Soil Resources Institute, Natural Resources Department, Cranfield University</s1>
<s2>Cranfield, Bedfordshire, MK43 0AL</s2>
<s3>GBR</s3>
<sZ>3 aut.</sZ>
</fA15>
<fA20>
<s1>32-45</s1>
</fA20>
<fA21>
<s1>2010</s1>
</fA21>
<fA23 i1="01">
<s0>ENG</s0>
</fA23>
<fA43 i1="01">
<s1>INIST</s1>
<s2>3607</s2>
<s5>354000180786920040</s5>
</fA43>
<fA44>
<s0>0000</s0>
<s1>© 2010 INIST-CNRS. All rights reserved.</s1>
</fA44>
<fA45>
<s0>3/4 p.</s0>
</fA45>
<fA47 i1="01" i2="1">
<s0>10-0391942</s0>
</fA47>
<fA60>
<s1>P</s1>
<s3>PR</s3>
</fA60>
<fA61>
<s0>A</s0>
</fA61>
<fA64 i1="01" i2="1">
<s0>Geoderma : (Amsterdam)</s0>
</fA64>
<fA66 i1="01">
<s0>NLD</s0>
</fA66>
<fC01 i1="01" l="ENG">
<s0>Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible spectrometers could provide the large amount of spatial data needed. In particular, the ability of airborne imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, airborne imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions. Here, five hyperspectral images acquired with the AHS-160 sensor (430 nm-2540 nm) were analysed with the objective to map soil organic carbon (SOC) at a regional scale. The study area, covering a surface of ∼420 km<sup>2</sup>and located in Luxembourg, is characterized by different soil types and a high variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and support vector machine regression (SVMR). The performance of these statistical tools was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). Under global calibration, the Root Mean Square Error in the Predictions reached 5.3-6.2 g C kg<sup>-1</sup>. Under local calibrations, this error was reduced by a factor up to 1.9. SOC maps of bare agricultural fields were produced using the best calibration model. Two map excerpts were shown, which display intra- and inter-field variability of SOC contents possibly related to topography and land management.</s0>
</fC01>
<fC02 i1="01" i2="X">
<s0>002A32</s0>
</fC02>
<fC02 i1="02" i2="2">
<s0>001E01P03</s0>
</fC02>
<fC02 i1="03" i2="2">
<s0>001E01M04</s0>
</fC02>
<fC02 i1="04" i2="2">
<s0>226C03</s0>
</fC02>
<fC02 i1="05" i2="2">
<s0>225B04</s0>
</fC02>
<fC03 i1="01" i2="2" l="FRE">
<s0>Carbone organique</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="ENG">
<s0>organic carbon</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="SPA">
<s0>Carbono orgánico</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE">
<s0>Sol agricole</s0>
<s2>NT</s2>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG">
<s0>Agricultural soil</s0>
<s2>NT</s2>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA">
<s0>Suelo agrícola</s0>
<s2>NT</s2>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE">
<s0>Champ cultivé</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG">
<s0>Cultivated field</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA">
<s0>Campo cultivado</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="2" l="FRE">
<s0>Télédétection</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="ENG">
<s0>remote sensing</s0>
<s5>04</s5>
</fC03>
<fC03 i1="04" i2="2" l="SPA">
<s0>Detección a distancia</s0>
<s5>04</s5>
</fC03>
<fC03 i1="05" i2="2" l="FRE">
<s0>Méthode aéroportée</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="ENG">
<s0>airborne methods</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="SPA">
<s0>Método aerotransportado</s0>
<s5>05</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE">
<s0>Capteur imagerie hyperspectral</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG">
<s0>Hyperspectral imaging sensor</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA">
<s0>Sensor hiperespectral de formación de imágenes</s0>
<s5>06</s5>
</fC03>
<fC03 i1="07" i2="2" l="FRE">
<s0>Pouvoir réflecteur</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="2" l="ENG">
<s0>reflectance</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="2" l="SPA">
<s0>Poder reflector</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="2" l="FRE">
<s0>Spectrométrie</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="2" l="ENG">
<s0>spectroscopy</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="2" l="SPA">
<s0>Espectrometría</s0>
<s5>08</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE">
<s0>Donnée spatiale</s0>
<s2>563</s2>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG">
<s0>Spatial data</s0>
<s2>563</s2>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA">
<s0>Dato espacial</s0>
<s2>563</s2>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="2" l="FRE">
<s0>Modèle</s0>
<s5>11</s5>
</fC03>
<fC03 i1="10" i2="2" l="ENG">
<s0>models</s0>
<s5>11</s5>
</fC03>
<fC03 i1="10" i2="2" l="SPA">
<s0>Modelo</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE">
<s0>Modélisation</s0>
<s5>12</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG">
<s0>Modeling</s0>
<s5>12</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA">
<s0>Modelización</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE">
<s0>Spectromètre</s0>
<s5>13</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG">
<s0>Spectrometer</s0>
<s5>13</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA">
<s0>Espectrómetro</s0>
<s5>13</s5>
</fC03>
<fC03 i1="13" i2="2" l="FRE">
<s0>Analyse statistique</s0>
<s5>14</s5>
</fC03>
<fC03 i1="13" i2="2" l="ENG">
<s0>statistical analysis</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="2" l="FRE">
<s0>Distribution spatiale</s0>
<s5>15</s5>
</fC03>
<fC03 i1="14" i2="2" l="ENG">
<s0>spatial distribution</s0>
<s5>15</s5>
</fC03>
<fC03 i1="14" i2="2" l="SPA">
<s0>Distribución espacial</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE">
<s0>Répartition spatiale</s0>
<s5>16</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG">
<s0>Spatial distribution</s0>
<s5>16</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA">
<s0>Distribución espacial</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE">
<s0>Caractéristique sol</s0>
<s5>17</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG">
<s0>Property of soil</s0>
<s5>17</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA">
<s0>Característica suelo</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="2" l="FRE">
<s0>Résolution spatiale</s0>
<s5>18</s5>
</fC03>
<fC03 i1="17" i2="2" l="ENG">
<s0>spatial resolution</s0>
<s5>18</s5>
</fC03>
<fC03 i1="18" i2="2" l="FRE">
<s0>Carte</s0>
<s5>19</s5>
</fC03>
<fC03 i1="18" i2="2" l="ENG">
<s0>maps</s0>
<s5>19</s5>
</fC03>
<fC03 i1="18" i2="2" l="SPA">
<s0>Mapa</s0>
<s5>19</s5>
</fC03>
<fC03 i1="19" i2="2" l="FRE">
<s0>Matière organique</s0>
<s5>20</s5>
</fC03>
<fC03 i1="19" i2="2" l="ENG">
<s0>organic materials</s0>
<s5>20</s5>
</fC03>
<fC03 i1="19" i2="2" l="SPA">
<s0>Materia orgánica</s0>
<s5>20</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE">
<s0>Couche arable</s0>
<s2>NT</s2>
<s5>21</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG">
<s0>Top soil</s0>
<s2>NT</s2>
<s5>21</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA">
<s0>Capa arable</s0>
<s2>NT</s2>
<s5>21</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE">
<s0>Imagerie hyperspectrale</s0>
<s5>24</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG">
<s0>Hyperspectral imagery</s0>
<s5>24</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA">
<s0>Imaginería hiperespectral</s0>
<s5>24</s5>
</fC03>
<fC03 i1="22" i2="2" l="FRE">
<s0>Capteur mesure</s0>
<s5>25</s5>
</fC03>
<fC03 i1="22" i2="2" l="ENG">
<s0>measurement sensor</s0>
<s5>25</s5>
</fC03>
<fC03 i1="23" i2="2" l="FRE">
<s0>Luxembourg</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="23" i2="2" l="ENG">
<s0>Luxembourg</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="23" i2="2" l="SPA">
<s0>Luxemburgo</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC07 i1="01" i2="2" l="FRE">
<s0>Europe Ouest</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="ENG">
<s0>Western Europe</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="SPA">
<s0>Europa del Oeste</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="FRE">
<s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="02" i2="2" l="ENG">
<s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="02" i2="2" l="SPA">
<s0>Europa</s0>
<s2>564</s2>
</fC07>
<fN21>
<s1>256</s1>
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<server>
<NO>: PASCAL 10-0391942 INIST</NO>
<ET>Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy</ET>
<AU>STEVENS (Antoine); UDELHOVEN (Thomas); DENIS (Antoine); TYCHON (Bernard); LIOY (Rocco); HOFFMANN (Lucien); VAN WESEMAEL (Bas); GUERRERO (César); VISCARRA ROSSEL (Raphael A.); MOUAZEN (Abdul Mounem)</AU>
<AF>Department of Geography, Université catholique de Louvain, 3 place Pasteur/1348 Louvain-La-Neuve/Belgique (1 aut., 7 aut.); Departement Environment and Agro-biotechnologies, Centre de Recherche Public-Gabriel Lippmann, 41 rue du Brill/4422 Belvaux/Luxembourg (2 aut., 6 aut.); Department of Environmental Sciences and Management, University of Liege, Campus of Arlon, 185 avenue de Longwy/6700 Arlon/Belgique (3 aut., 4 aut.); CONVIS Herdbuch Service Élevage et Génétique-Société coopérative, B.P. 313/9004 Ettelbruck/Luxembourg (5 aut.); Department of Agrochemistry and Environment, University Miguel Hernández, Avenida de la Universidad s/n/Elche, Alicante 3202/Espagne (1 aut.); CSIRO Land and Water, Bruce E. Butler Laboratory, GPO Box 1666/Canberra, ACT 2601/Australie (2 aut.); National Soil Resources Institute, Natural Resources Department, Cranfield University/Cranfield, Bedfordshire, MK43 0AL/Royaume-Uni (3 aut.)</AF>
<DT>Publication en série; Papier de recherche; Niveau analytique</DT>
<SO>Geoderma : (Amsterdam); ISSN 0016-7061; Coden GEDMAB; Pays-Bas; Da. 2010; Vol. 158; No. 1-2; Pp. 32-45; Bibl. 3/4 p.</SO>
<LA>Anglais</LA>
<EA>Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible spectrometers could provide the large amount of spatial data needed. In particular, the ability of airborne imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, airborne imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions. Here, five hyperspectral images acquired with the AHS-160 sensor (430 nm-2540 nm) were analysed with the objective to map soil organic carbon (SOC) at a regional scale. The study area, covering a surface of ∼420 km<sup>2</sup>and located in Luxembourg, is characterized by different soil types and a high variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and support vector machine regression (SVMR). The performance of these statistical tools was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). Under global calibration, the Root Mean Square Error in the Predictions reached 5.3-6.2 g C kg<sup>-1</sup>. Under local calibrations, this error was reduced by a factor up to 1.9. SOC maps of bare agricultural fields were produced using the best calibration model. Two map excerpts were shown, which display intra- and inter-field variability of SOC contents possibly related to topography and land management.</EA>
<CC>002A32; 001E01P03; 001E01M04; 226C03; 225B04</CC>
<FD>Carbone organique; Sol agricole; Champ cultivé; Télédétection; Méthode aéroportée; Capteur imagerie hyperspectral; Pouvoir réflecteur; Spectrométrie; Donnée spatiale; Modèle; Modélisation; Spectromètre; Analyse statistique; Distribution spatiale; Répartition spatiale; Caractéristique sol; Résolution spatiale; Carte; Matière organique; Couche arable; Imagerie hyperspectrale; Capteur mesure; Luxembourg</FD>
<FG>Europe Ouest; Europe</FG>
<ED>organic carbon; Agricultural soil; Cultivated field; remote sensing; airborne methods; Hyperspectral imaging sensor; reflectance; spectroscopy; Spatial data; models; Modeling; Spectrometer; statistical analysis; spatial distribution; Spatial distribution; Property of soil; spatial resolution; maps; organic materials; Top soil; Hyperspectral imagery; measurement sensor; Luxembourg</ED>
<EG>Western Europe; Europe</EG>
<SD>Carbono orgánico; Suelo agrícola; Campo cultivado; Detección a distancia; Método aerotransportado; Sensor hiperespectral de formación de imágenes; Poder reflector; Espectrometría; Dato espacial; Modelo; Modelización; Espectrómetro; Distribución espacial; Distribución espacial; Característica suelo; Mapa; Materia orgánica; Capa arable; Imaginería hiperespectral; Luxemburgo</SD>
<LO>INIST-3607.354000180786920040</LO>
<ID>10-0391942</ID>
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