UnivTrevesV1, PascalFrancis, Corpus, bibRecord, 000859

Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)

Identifieur interne : 000859 ( PascalFrancis/Corpus ); précédent : 000858; suivant : 000860

Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)

Auteurs : M. Vohland ; T. Jarmer

Source :

International journal of remote sensing : (Print) [ 0143-1161 ] ; 2008.

RBID : Pascal:08-0105931

Descripteurs français

Pascal (Inist)
- Télédétection, Végétation, Prairie, Transfert radiatif, Modèle, Croissance, Utilisation terrain, Occupation sol, Inventaire, Ecologie, Biochimie, Chlorophylle, Cycle carbone, Problème inverse, Humidité sol, Feuillage, Inversion, Teneur matière sèche, Indice foliaire, Paramètre, Spectroradiométrie, Prairie permanente, Analyse biochimique, LAI, Rhénanie Palatinat.

English descriptors

KwdEn :
- Biochemical analysis, Dry matter content, Foliage, Inversion, Leaf area index, Parameter, Permanent grassland, Rhineland-Palatinate Germany, Spectroradiometry, biochemistry, carbon cycle, chlorophyll, ecology, grasslands, growth, inventory, inverse problem, land cover, land use, models, radiative transfer, remote sensing, soil moisture, vegetation.

Abstract

As permanent grassland is a large-scale land-use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT + SAIL). Constraints were necessary to compensate the ill-posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min=0.10, max=5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT + SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 08-0105931 INIST
ET :	Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)
AU :	VOHLAND (M.); JARMER (T.)
AF :	Remote Sensing and Geoinformatics, Faculty of Geography and Geosciences, University of Trier, Campus II/54286 Trier/Allemagne (1 aut.); Remote Sensing and Geoinformatics, Institute of Geographical Sciences, Department of Earth Sciences, Free University of Berlin/12249 Berlin/Allemagne (2 aut.)
DT :	Publication en série; Niveau analytique
SO :	International journal of remote sensing : (Print); ISSN 0143-1161; Coden IJSEDK; Royaume-Uni; Da. 2008; Vol. 29; No. 1-2; Pp. 191-209; Bibl. 1 p.3/4
LA :	Anglais
EA :	As permanent grassland is a large-scale land-use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT + SAIL). Constraints were necessary to compensate the ill-posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min=0.10, max=5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT + SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.
CC :	002A14A03; 001E01M04; 225B04
FD :	Télédétection; Végétation; Prairie; Transfert radiatif; Modèle; Croissance; Utilisation terrain; Occupation sol; Inventaire; Ecologie; Biochimie; Chlorophylle; Cycle carbone; Problème inverse; Humidité sol; Feuillage; Inversion; Teneur matière sèche; Indice foliaire; Paramètre; Spectroradiométrie; Prairie permanente; Analyse biochimique; LAI; Rhénanie Palatinat
FG :	Allemagne; Europe Centrale; Europe
ED :	remote sensing; vegetation; grasslands; radiative transfer; models; growth; land use; land cover; inventory; ecology; biochemistry; chlorophyll; carbon cycle; inverse problem; soil moisture; Foliage; Inversion; Dry matter content; Leaf area index; Parameter; Spectroradiometry; Permanent grassland; Biochemical analysis; Rhineland-Palatinate Germany
EG :	Germany; Central Europe; Europe
SD :	Detección a distancia; Vegetación; Pradera; Modelo; Utilización terreno; Inventario; Ecología; Bioquímica; Clorofila; Problema inverso; Humedad suelo; Follaje; Inversión; Contenido materia seca; Indice foliar; Parámetro; Espectroradiometría; Pradera permanente; Análisis bioquímico; Renania Palatinado
LO :	INIST-19437.354000173952870110
ID :	08-0105931

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Le document en format XML

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<front><div type="abstract" xml:lang="en">As permanent grassland is a large-scale land-use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT + SAIL). Constraints were necessary to compensate the ill-posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min=0.10, max=5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT + SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.</div>
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<fC01 i1="01" l="ENG"><s0>As permanent grassland is a large-scale land-use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT + SAIL). Constraints were necessary to compensate the ill-posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min=0.10, max=5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT + SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002A14A03</s0>
</fC02>
<fC02 i1="02" i2="2"><s0>001E01M04</s0>
</fC02>
<fC02 i1="03" i2="2"><s0>225B04</s0>
</fC02>
<fC03 i1="01" i2="2" l="FRE"><s0>Télédétection</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="ENG"><s0>remote sensing</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="2" l="SPA"><s0>Detección a distancia</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="2" l="FRE"><s0>Végétation</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="ENG"><s0>vegetation</s0>
<s5>02</s5>
</fC03>
<fC03 i1="02" i2="2" l="SPA"><s0>Vegetación</s0>
<s5>02</s5>
</fC03>
<fC03 i1="03" i2="2" l="FRE"><s0>Prairie</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="ENG"><s0>grasslands</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="2" l="SPA"><s0>Pradera</s0>
<s5>03</s5>
</fC03>
<fC03 i1="04" i2="2" l="FRE"><s0>Transfert radiatif</s0>
<s5>05</s5>
</fC03>
<fC03 i1="04" i2="2" l="ENG"><s0>radiative transfer</s0>
<s5>05</s5>
</fC03>
<fC03 i1="05" i2="2" l="FRE"><s0>Modèle</s0>
<s5>06</s5>
</fC03>
<fC03 i1="05" i2="2" l="ENG"><s0>models</s0>
<s5>06</s5>
</fC03>
<fC03 i1="05" i2="2" l="SPA"><s0>Modelo</s0>
<s5>06</s5>
</fC03>
<fC03 i1="06" i2="2" l="FRE"><s0>Croissance</s0>
<s5>08</s5>
</fC03>
<fC03 i1="06" i2="2" l="ENG"><s0>growth</s0>
<s5>08</s5>
</fC03>
<fC03 i1="07" i2="2" l="FRE"><s0>Utilisation terrain</s0>
<s5>09</s5>
</fC03>
<fC03 i1="07" i2="2" l="ENG"><s0>land use</s0>
<s5>09</s5>
</fC03>
<fC03 i1="07" i2="2" l="SPA"><s0>Utilización terreno</s0>
<s5>09</s5>
</fC03>
<fC03 i1="08" i2="2" l="FRE"><s0>Occupation sol</s0>
<s5>10</s5>
</fC03>
<fC03 i1="08" i2="2" l="ENG"><s0>land cover</s0>
<s5>10</s5>
</fC03>
<fC03 i1="09" i2="2" l="FRE"><s0>Inventaire</s0>
<s5>12</s5>
</fC03>
<fC03 i1="09" i2="2" l="ENG"><s0>inventory</s0>
<s5>12</s5>
</fC03>
<fC03 i1="09" i2="2" l="SPA"><s0>Inventario</s0>
<s5>12</s5>
</fC03>
<fC03 i1="10" i2="2" l="FRE"><s0>Ecologie</s0>
<s5>13</s5>
</fC03>
<fC03 i1="10" i2="2" l="ENG"><s0>ecology</s0>
<s5>13</s5>
</fC03>
<fC03 i1="10" i2="2" l="SPA"><s0>Ecología</s0>
<s5>13</s5>
</fC03>
<fC03 i1="11" i2="2" l="FRE"><s0>Biochimie</s0>
<s5>14</s5>
</fC03>
<fC03 i1="11" i2="2" l="ENG"><s0>biochemistry</s0>
<s5>14</s5>
</fC03>
<fC03 i1="11" i2="2" l="SPA"><s0>Bioquímica</s0>
<s5>14</s5>
</fC03>
<fC03 i1="12" i2="2" l="FRE"><s0>Chlorophylle</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="2" l="ENG"><s0>chlorophyll</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="2" l="SPA"><s0>Clorofila</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="2" l="FRE"><s0>Cycle carbone</s0>
<s5>20</s5>
</fC03>
<fC03 i1="13" i2="2" l="ENG"><s0>carbon cycle</s0>
<s5>20</s5>
</fC03>
<fC03 i1="14" i2="2" l="FRE"><s0>Problème inverse</s0>
<s5>22</s5>
</fC03>
<fC03 i1="14" i2="2" l="ENG"><s0>inverse problem</s0>
<s5>22</s5>
</fC03>
<fC03 i1="14" i2="2" l="SPA"><s0>Problema inverso</s0>
<s5>22</s5>
</fC03>
<fC03 i1="15" i2="2" l="FRE"><s0>Humidité sol</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="2" l="ENG"><s0>soil moisture</s0>
<s5>25</s5>
</fC03>
<fC03 i1="15" i2="2" l="SPA"><s0>Humedad suelo</s0>
<s5>25</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Feuillage</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Foliage</s0>
<s5>26</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Follaje</s0>
<s5>26</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Inversion</s0>
<s5>27</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Inversion</s0>
<s5>27</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Inversión</s0>
<s5>27</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Teneur matière sèche</s0>
<s5>28</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Dry matter content</s0>
<s5>28</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Contenido materia seca</s0>
<s5>28</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Indice foliaire</s0>
<s5>29</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Leaf area index</s0>
<s5>29</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Indice foliar</s0>
<s5>29</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Paramètre</s0>
<s5>30</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Parameter</s0>
<s5>30</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Parámetro</s0>
<s5>30</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Spectroradiométrie</s0>
<s5>32</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Spectroradiometry</s0>
<s5>32</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Espectroradiometría</s0>
<s5>32</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>Prairie permanente</s0>
<s5>33</s5>
</fC03>
<fC03 i1="22" i2="X" l="ENG"><s0>Permanent grassland</s0>
<s5>33</s5>
</fC03>
<fC03 i1="22" i2="X" l="SPA"><s0>Pradera permanente</s0>
<s5>33</s5>
</fC03>
<fC03 i1="23" i2="X" l="FRE"><s0>Analyse biochimique</s0>
<s5>34</s5>
</fC03>
<fC03 i1="23" i2="X" l="ENG"><s0>Biochemical analysis</s0>
<s5>34</s5>
</fC03>
<fC03 i1="23" i2="X" l="SPA"><s0>Análisis bioquímico</s0>
<s5>34</s5>
</fC03>
<fC03 i1="24" i2="2" l="FRE"><s0>LAI</s0>
<s4>INC</s4>
<s5>52</s5>
</fC03>
<fC03 i1="25" i2="2" l="FRE"><s0>Rhénanie Palatinat</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="25" i2="2" l="ENG"><s0>Rhineland-Palatinate Germany</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC03 i1="25" i2="2" l="SPA"><s0>Renania Palatinado</s0>
<s2>NG</s2>
<s5>61</s5>
</fC03>
<fC07 i1="01" i2="2" l="FRE"><s0>Allemagne</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="ENG"><s0>Germany</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="01" i2="2" l="SPA"><s0>Alemania</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="FRE"><s0>Europe Centrale</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="ENG"><s0>Central Europe</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="02" i2="2" l="SPA"><s0>Europa central</s0>
<s2>NG</s2>
</fC07>
<fC07 i1="03" i2="2" l="FRE"><s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="03" i2="2" l="ENG"><s0>Europe</s0>
<s2>564</s2>
</fC07>
<fC07 i1="03" i2="2" l="SPA"><s0>Europa</s0>
<s2>564</s2>
</fC07>
<fN21><s1>056</s1>
</fN21>
<fN44 i1="01"><s1>PSI</s1>
</fN44>
<fN82><s1>PSI</s1>
</fN82>
</pA>
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<server><NO>PASCAL 08-0105931 INIST</NO>
<ET>Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)</ET>
<AU>VOHLAND (M.); JARMER (T.)</AU>
<AF>Remote Sensing and Geoinformatics, Faculty of Geography and Geosciences, University of Trier, Campus II/54286 Trier/Allemagne (1 aut.); Remote Sensing and Geoinformatics, Institute of Geographical Sciences, Department of Earth Sciences, Free University of Berlin/12249 Berlin/Allemagne (2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>International journal of remote sensing : (Print); ISSN 0143-1161; Coden IJSEDK; Royaume-Uni; Da. 2008; Vol. 29; No. 1-2; Pp. 191-209; Bibl. 1 p.3/4</SO>
<LA>Anglais</LA>
<EA>As permanent grassland is a large-scale land-use type in Central Europe, grassland inventories are relevant for ecological and agrarian issues. The objective of this study was to assess structural and biochemical grassland parameters (LAI, chlorophyll, water and dry matter contents) from field spectroradiometer data (ASD FieldSpec II) by radiative transfer modelling (PROSPECT + SAIL). Constraints were necessary to compensate the ill-posed nature of model inversion for accurate parameter retrieval. In this context, we found the foliage moisture content to play an important role. After coupling the equivalent water thickness and the dry matter content in a ratio of 4:1, the estimation accuracy for the LAI clearly improved. In terms of LAI, the RMSE decreased from 0.86 to 0.74, and the range of LAI values measured in the field (min=0.10, max=5.88) was reproduced exactly with estimates ranging from 0.05 to 5.46. The spectra reconstructed by PROSPECT + SAIL using the inverted parameter estimates coincided well with the measured spectra. Nonetheless, obtained canopy chlorophyll contents tended to be too high. When using ground measured chlorophyll data for spectra generation, simulated reflectances were clearly higher in the visible domain than the measured ones. This is partly attributed to the chlorophyll absorption coefficients of PROSPECT that may not be true for the majority of grassland plant species in reality. Neverthelesss, the obtained results prove the potential of PROSPECT+SAIL for retrieving structural and biochemical grassland parameters; results may be appropriate for assimilation in the modelling of plant growth or carbon cycle, for example.</EA>
<CC>002A14A03; 001E01M04; 225B04</CC>
<FD>Télédétection; Végétation; Prairie; Transfert radiatif; Modèle; Croissance; Utilisation terrain; Occupation sol; Inventaire; Ecologie; Biochimie; Chlorophylle; Cycle carbone; Problème inverse; Humidité sol; Feuillage; Inversion; Teneur matière sèche; Indice foliaire; Paramètre; Spectroradiométrie; Prairie permanente; Analyse biochimique; LAI; Rhénanie Palatinat</FD>
<FG>Allemagne; Europe Centrale; Europe</FG>
<ED>remote sensing; vegetation; grasslands; radiative transfer; models; growth; land use; land cover; inventory; ecology; biochemistry; chlorophyll; carbon cycle; inverse problem; soil moisture; Foliage; Inversion; Dry matter content; Leaf area index; Parameter; Spectroradiometry; Permanent grassland; Biochemical analysis; Rhineland-Palatinate Germany</ED>
<EG>Germany; Central Europe; Europe</EG>
<SD>Detección a distancia; Vegetación; Pradera; Modelo; Utilización terreno; Inventario; Ecología; Bioquímica; Clorofila; Problema inverso; Humedad suelo; Follaje; Inversión; Contenido materia seca; Indice foliar; Parámetro; Espectroradiometría; Pradera permanente; Análisis bioquímico; Renania Palatinado</SD>
<LO>INIST-19437.354000173952870110</LO>
<ID>08-0105931</ID>
</server>
</inist>
</record>

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Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)

Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT + SAIL)

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