Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields
Identifieur interne : 000F48 ( Istex/Corpus ); précédent : 000F47; suivant : 000F49Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields
Auteurs : Assefa M. Melesse ; Vijay NangiaSource :
- Hydrological Processes [ 0885-6087 ] ; 2005-09.
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- KwdEn :
Abstract
Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m−2 and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R2 = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m3 ha−1). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.
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DOI: 10.1002/hyp.5779
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Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m−2 and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R2 = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m3 ha−1). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Assefa M. 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Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m−2 and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R2 = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m3 ha−1). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. 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Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m−2 and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R2 = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m3 ha−1). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. 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Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m<sup>−2</sup> and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean <i>R</i><sup>2</sup> = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m<sup>3</sup> ha<sup>−1</sup>). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>SURFACE ENERGY FLUXES OF AGRICULTURAL FIELDS</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields</title></titleInfo><name type="personal"><namePart type="given">Assefa M.</namePart><namePart type="family">Melesse</namePart><affiliation>Department of Environmental Studies, Florida University, Miami, FL 33199, USA</affiliation><affiliation>Department of Environmental Studies, Florida International University, Miami, FL 33199, USA.===</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Vijay</namePart><namePart type="family">Nangia</namePart><affiliation>Earth System Science Institute, University of North Dakota, Grand Forks, ND 58202, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>John Wiley & Sons, Ltd.</publisher><place><placeTerm type="text">Chichester, UK</placeTerm></place><dateIssued encoding="w3cdtf">2005-09</dateIssued><dateCaptured encoding="w3cdtf">2003-09-10</dateCaptured><dateValid encoding="w3cdtf">2004-06-23</dateValid><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">9</extent><extent unit="tables">6</extent><extent unit="references">29</extent></physicalDescription><abstract lang="en">Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m−2 and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R2 = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m3 ha−1). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.</abstract><note type="funding">NASA - No. NAG 13‐02047; </note><subject lang="en"><genre>keywords</genre><topic>remote sensing</topic><topic>latent heat</topic><topic>SEBAL</topic><topic>two‐source model</topic><topic>surface energy flux</topic><topic>yield</topic><topic>Landsat</topic></subject><relatedItem type="host"><titleInfo><title>Hydrological Processes</title><subTitle>An International Journal</subTitle></titleInfo><titleInfo type="abbreviated"><title>Hydrol. Process.</title></titleInfo><genre type="journal">journal</genre><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">0885-6087</identifier><identifier type="eISSN">1099-1085</identifier><identifier type="DOI">10.1002/(ISSN)1099-1085</identifier><identifier type="PublisherID">HYP</identifier><part><date>2005</date><detail type="volume"><caption>vol.</caption><number>19</number></detail><detail type="issue"><caption>no.</caption><number>14</number></detail><extent unit="pages"><start>2653</start><end>2670</end><total>18</total></extent></part></relatedItem><identifier type="istex">EF4665BE6B8055EA18A571330F4ADF9885716C06</identifier><identifier type="DOI">10.1002/hyp.5779</identifier><identifier type="ArticleID">HYP5779</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2005 John Wiley & Sons, Ltd.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>John Wiley & Sons, Ltd.</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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