Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region
Identifieur interne : 000E81 ( Istex/Corpus ); précédent : 000E80; suivant : 000E82Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region
Auteurs : J. Qi ; Y. H Kerr ; M. S Moran ; M. Weltz ; A. R Huete ; S. Sorooshian ; R. BryantSource :
- Remote Sensing of Environment [ 0034-4257 ] ; 2000.
Abstract
The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.
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DOI: 10.1016/S0034-4257(99)00113-3
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S Moran</name><affiliation><mods:affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Weltz, M" sort="Weltz, M" uniqKey="Weltz M" first="M" last="Weltz">M. Weltz</name><affiliation><mods:affiliation>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</mods:affiliation></affiliation></author><author><name sortKey="Huete, A R" sort="Huete, A R" uniqKey="Huete A" first="A. R" last="Huete">A. R Huete</name><affiliation><mods:affiliation>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Sorooshian, S" sort="Sorooshian, S" uniqKey="Sorooshian S" first="S" last="Sorooshian">S. 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Qi</name><affiliation><mods:affiliation>E-mail: qi@pilot.msu.edu</mods:affiliation></affiliation><affiliation><mods:affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Kerr, Y H" sort="Kerr, Y H" uniqKey="Kerr Y" first="Y. H" last="Kerr">Y. H Kerr</name><affiliation><mods:affiliation>CESBIO, CNES, Toulouse, France</mods:affiliation></affiliation></author><author><name sortKey="Moran, M S" sort="Moran, M S" uniqKey="Moran M" first="M. S" last="Moran">M. S Moran</name><affiliation><mods:affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Weltz, M" sort="Weltz, M" uniqKey="Weltz M" first="M" last="Weltz">M. Weltz</name><affiliation><mods:affiliation>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</mods:affiliation></affiliation></author><author><name sortKey="Huete, A R" sort="Huete, A R" uniqKey="Huete A" first="A. R" last="Huete">A. R Huete</name><affiliation><mods:affiliation>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Sorooshian, S" sort="Sorooshian, S" uniqKey="Sorooshian S" first="S" last="Sorooshian">S. Sorooshian</name><affiliation><mods:affiliation>Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ USA</mods:affiliation></affiliation></author><author><name sortKey="Bryant, R" sort="Bryant, R" uniqKey="Bryant R" first="R" last="Bryant">R. Bryant</name><affiliation><mods:affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Remote Sensing of Environment</title><title level="j" type="abbrev">RSE</title><idno type="ISSN">0034-4257</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000">2000</date><biblScope unit="volume">73</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="18">18</biblScope><biblScope unit="page" to="30">30</biblScope></imprint><idno type="ISSN">0034-4257</idno></series><idno type="istex">E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0</idno><idno type="DOI">10.1016/S0034-4257(99)00113-3</idno><idno type="PII">S0034-4257(99)00113-3</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0034-4257</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>J Qi</name><affiliations><json:string>E-mail: qi@pilot.msu.edu</json:string><json:string>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</json:string></affiliations></json:item><json:item><name>Y.H Kerr</name><affiliations><json:string>CESBIO, CNES, Toulouse, France</json:string></affiliations></json:item><json:item><name>M.S Moran</name><affiliations><json:string>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</json:string></affiliations></json:item><json:item><name>M Weltz</name><affiliations><json:string>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</json:string></affiliations></json:item><json:item><name>A.R Huete</name><affiliations><json:string>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</json:string></affiliations></json:item><json:item><name>S Sorooshian</name><affiliations><json:string>Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ USA</json:string></affiliations></json:item><json:item><name>R Bryant</name><affiliations><json:string>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>Full-length article</json:string></originalGenre><abstract>The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.</abstract><qualityIndicators><score>8</score><pdfVersion>1.2</pdfVersion><pdfPageSize>594 x 792 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>0</keywordCount><abstractCharCount>2629</abstractCharCount><pdfWordCount>6352</pdfWordCount><pdfCharCount>38673</pdfCharCount><pdfPageCount>13</pdfPageCount><abstractWordCount>397</abstractWordCount></qualityIndicators><title>Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</title><pii><json:string>S0034-4257(99)00113-3</json:string></pii><genre><json:string>research-article</json:string></genre><host><volume>73</volume><pii><json:string>S0034-4257(00)X0066-1</json:string></pii><pages><last>30</last><first>18</first></pages><issn><json:string>0034-4257</json:string></issn><issue>1</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Remote Sensing of Environment</title><publicationDate>2000</publicationDate></host><categories><wos><json:string>science</json:string><json:string>remote sensing</json:string><json:string>imaging science & photographic technology</json:string><json:string>environmental sciences</json:string></wos><scienceMetrix><json:string>applied sciences</json:string><json:string>engineering</json:string><json:string>geological & geomatics engineering</json:string></scienceMetrix></categories><publicationDate>2000</publicationDate><copyrightDate>2000</copyrightDate><doi><json:string>10.1016/S0034-4257(99)00113-3</json:string></doi><id>E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0</id><score>0.03780877</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2000 Elsevier Science Inc.</p></availability><date>2000</date></publicationStmt><notesStmt><note type="content">Figure 1: Graphic presentation of the proposed approach to estimating leaf area index with remote sensing imagery</note><note type="content">Figure 2: LAI–NDVI relationships [Eq. (2) in the text and a linear fit] used in this study</note><note type="content">Figure 3: Performance of the neural fuzzy inference system. The x-axis is the training LAI values, while the y-axis is the output of the fuzzy system</note><note type="content">Figure 4: LAI maps generated with Landsat TM image acquired on April 22 (a) and September 7(b) 1990, using the proposed approach. These two dates represent vegetation status in thedry (a) and wet (b) seasons</note><note type="content">Figure 5: Comparison of LAI maps generated using neural fuzzy inference system (a) and LAI–SVI (b) techniques with TM image of 7 September 1990</note><note type="content">Figure 6: LAI distribution estimated using the proposed approach with AVHRR image composited for May (a) and September (b) 1992 over the Hapex-Sahel experimental site.</note><note type="content">Figure 7: Comparison of estimated LAI from remote-sensing images with ground measurements collected at a) Walnut Gulch Experimental Watershed and b) the Audubon ranch</note><note type="content">Figure 8: Comparison of temporal LAI values estimated using AVHRR data with ground measurements for a) Fallow (at the Central West and South Super sites) and b) Millet at the South Super site during the Hapex-Sahel experiment. Note that the LAI data for the Millet site (b) were measured in 1993, while the AVHRR data were acquired in 1992 at the same site</note><note type="content">Figure 9: Comparison of LAI maps derived using Eq. (4) and linear fit equation (5) and TM image acquired on 7 September 1990</note><note type="content">Figure 10: Comparison of polynomial fit obtained from this study with a linear fit from Asrar et al. (1985b)</note><note type="content">Table 1: Vegetation and Soil Optical Properties Obtained by Inversion of SAIL Model and Subsequently Used in This Study for LAI Mapping</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</title><author xml:id="author-1"><persName><forename type="first">J</forename><surname>Qi</surname></persName><email>qi@pilot.msu.edu</email><affiliation>Address correspondence to J. Qi, Dept. of Geography, Michigan State Univ. East Lansing, MI 48824-1115.or</affiliation><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation></author><author xml:id="author-2"><persName><forename type="first">Y.H</forename><surname>Kerr</surname></persName><affiliation>CESBIO, CNES, Toulouse, France</affiliation></author><author xml:id="author-3"><persName><forename type="first">M.S</forename><surname>Moran</surname></persName><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation></author><author xml:id="author-4"><persName><forename type="first">M</forename><surname>Weltz</surname></persName><affiliation>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</affiliation></author><author xml:id="author-5"><persName><forename type="first">A.R</forename><surname>Huete</surname></persName><affiliation>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</affiliation></author><author xml:id="author-6"><persName><forename type="first">S</forename><surname>Sorooshian</surname></persName><affiliation>Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ USA</affiliation></author><author xml:id="author-7"><persName><forename type="first">R</forename><surname>Bryant</surname></persName><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation></author></analytic><monogr><title level="j">Remote Sensing of Environment</title><title level="j" type="abbrev">RSE</title><idno type="pISSN">0034-4257</idno><idno type="PII">S0034-4257(00)X0066-1</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2000"></date><biblScope unit="volume">73</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="18">18</biblScope><biblScope unit="page" to="30">30</biblScope></imprint></monogr><idno type="istex">E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0</idno><idno type="DOI">10.1016/S0034-4257(99)00113-3</idno><idno type="PII">S0034-4257(99)00113-3</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2000</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.</p></abstract></profileDesc><revisionDesc><change when="1999-11-12">Modified</change><change when="2000">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr5" NDATA="IMAGE" name="gr5"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity><istex:entity SYSTEM="gr6" NDATA="IMAGE" name="gr6"></istex:entity><istex:entity SYSTEM="gr7" NDATA="IMAGE" name="gr7"></istex:entity><istex:entity SYSTEM="gr8" NDATA="IMAGE" name="gr8"></istex:entity><istex:entity SYSTEM="gr9" NDATA="IMAGE" name="gr9"></istex:entity><istex:entity SYSTEM="gr10" NDATA="IMAGE" name="gr10"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>RSE</jid><aid>5369</aid><ce:pii>S0034-4257(99)00113-3</ce:pii><ce:doi>10.1016/S0034-4257(99)00113-3</ce:doi><ce:copyright type="full-transfer" year="2000">Elsevier Science Inc.</ce:copyright></item-info><head><ce:title>Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</ce:title><ce:author-group><ce:author><ce:given-name>J</ce:given-name><ce:surname>Qi</ce:surname><ce:cross-ref refid="AFF1">*</ce:cross-ref><ce:cross-ref refid="CORR1">*</ce:cross-ref><ce:e-address>qi@pilot.msu.edu</ce:e-address><ce:e-address>qi@tucson.ars.ag.gov</ce:e-address></ce:author><ce:author><ce:given-name>Y.H</ce:given-name><ce:surname>Kerr</ce:surname><ce:cross-ref refid="AFF2">†</ce:cross-ref></ce:author><ce:author><ce:given-name>M.S</ce:given-name><ce:surname>Moran</ce:surname><ce:cross-ref refid="AFF1">*</ce:cross-ref></ce:author><ce:author><ce:given-name>M</ce:given-name><ce:surname>Weltz</ce:surname><ce:cross-ref refid="AFF3">‡</ce:cross-ref></ce:author><ce:author><ce:given-name>A.R</ce:given-name><ce:surname>Huete</ce:surname><ce:cross-ref refid="AFF4">§</ce:cross-ref></ce:author><ce:author><ce:given-name>S</ce:given-name><ce:surname>Sorooshian</ce:surname><ce:cross-ref refid="AFF5">¶</ce:cross-ref></ce:author><ce:author><ce:given-name>R</ce:given-name><ce:surname>Bryant</ce:surname><ce:cross-ref refid="AFF1">*</ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>*</ce:label><ce:textfn>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>†</ce:label><ce:textfn>CESBIO, CNES, Toulouse, France</ce:textfn></ce:affiliation><ce:affiliation id="AFF3"><ce:label>‡</ce:label><ce:textfn>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF4"><ce:label>§</ce:label><ce:textfn>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</ce:textfn></ce:affiliation><ce:affiliation id="AFF5"><ce:label>¶</ce:label><ce:textfn>Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ USA</ce:textfn></ce:affiliation><ce:correspondence id="CORR1"><ce:label>*</ce:label><ce:text>Address correspondence to J. Qi, Dept. of Geography, Michigan State Univ. East Lansing, MI 48824-1115.or</ce:text></ce:correspondence></ce:author-group><ce:date-received day="1" month="6" year="1999"></ce:date-received><ce:date-revised day="12" month="11" year="1999"></ce:date-revised><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.</ce:simple-para></ce:abstract-sec></ce:abstract></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region</title></titleInfo><name type="personal"><namePart type="given">J</namePart><namePart type="family">Qi</namePart><affiliation>E-mail: qi@pilot.msu.edu</affiliation><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation><description>Address correspondence to J. Qi, Dept. of Geography, Michigan State Univ. East Lansing, MI 48824-1115.or</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Y.H</namePart><namePart type="family">Kerr</namePart><affiliation>CESBIO, CNES, Toulouse, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M.S</namePart><namePart type="family">Moran</namePart><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M</namePart><namePart type="family">Weltz</namePart><affiliation>USDA-ARS Great Plains System Research, Ft. Collins, CO USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">A.R</namePart><namePart type="family">Huete</namePart><affiliation>Department of Soil, Water, and Environmental Sciences, University of Arizona, Tucson, AZ USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">S</namePart><namePart type="family">Sorooshian</namePart><affiliation>Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">R</namePart><namePart type="family">Bryant</namePart><affiliation>USDA-ARS Water Conservation Laboratory, Phoenix, AZ USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2000</dateIssued><dateModified encoding="w3cdtf">1999-11-12</dateModified><copyrightDate encoding="w3cdtf">2000</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI–SVI relation between remotely sensed variables such as SVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set of coefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI using an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varying surface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In this study, we present a strategy that combines BRDF models and conventional LAI–SVI approaches to circumvent these limitations. The proposed strategy was implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of data points or pixels to produce a training data set consisting of leaf area index and associated pixel values. In the second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step, the training data set was used either to fit an LAI–SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy system was then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach produced reasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements.</abstract><note type="content">Figure 1: Graphic presentation of the proposed approach to estimating leaf area index with remote sensing imagery</note><note type="content">Figure 2: LAI–NDVI relationships [Eq. (2) in the text and a linear fit] used in this study</note><note type="content">Figure 3: Performance of the neural fuzzy inference system. The x-axis is the training LAI values, while the y-axis is the output of the fuzzy system</note><note type="content">Figure 4: LAI maps generated with Landsat TM image acquired on April 22 (a) and September 7(b) 1990, using the proposed approach. These two dates represent vegetation status in thedry (a) and wet (b) seasons</note><note type="content">Figure 5: Comparison of LAI maps generated using neural fuzzy inference system (a) and LAI–SVI (b) techniques with TM image of 7 September 1990</note><note type="content">Figure 6: LAI distribution estimated using the proposed approach with AVHRR image composited for May (a) and September (b) 1992 over the Hapex-Sahel experimental site.</note><note type="content">Figure 7: Comparison of estimated LAI from remote-sensing images with ground measurements collected at a) Walnut Gulch Experimental Watershed and b) the Audubon ranch</note><note type="content">Figure 8: Comparison of temporal LAI values estimated using AVHRR data with ground measurements for a) Fallow (at the Central West and South Super sites) and b) Millet at the South Super site during the Hapex-Sahel experiment. Note that the LAI data for the Millet site (b) were measured in 1993, while the AVHRR data were acquired in 1992 at the same site</note><note type="content">Figure 9: Comparison of LAI maps derived using Eq. (4) and linear fit equation (5) and TM image acquired on 7 September 1990</note><note type="content">Figure 10: Comparison of polynomial fit obtained from this study with a linear fit from Asrar et al. (1985b)</note><note type="content">Table 1: Vegetation and Soil Optical Properties Obtained by Inversion of SAIL Model and Subsequently Used in This Study for LAI Mapping</note><relatedItem type="host"><titleInfo><title>Remote Sensing of Environment</title></titleInfo><titleInfo type="abbreviated"><title>RSE</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">200007</dateIssued></originInfo><identifier type="ISSN">0034-4257</identifier><identifier type="PII">S0034-4257(00)X0066-1</identifier><part><date>200007</date><detail type="volume"><number>73</number><caption>vol.</caption></detail><detail type="issue"><number>1</number><caption>no.</caption></detail><extent unit="issue pages"><start>1</start><end>114</end></extent><extent unit="pages"><start>18</start><end>30</end></extent></part></relatedItem><identifier type="istex">E2D3145FEC083B1E63C9615CDC9D10E1AE04C1F0</identifier><identifier type="DOI">10.1016/S0034-4257(99)00113-3</identifier><identifier type="PII">S0034-4257(99)00113-3</identifier><accessCondition type="use and reproduction" contentType="copyright">©2000 Elsevier Science Inc.</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Inc., ©2000</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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