Application of Spectral Remote Sensing for Agronomic Decisions
Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrie...
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Veröffentlicht in: | Agronomy journal 2008-05, Vol.100 (3), p.S117-S131 |
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description | Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications. |
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The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.</description><identifier>ISSN: 0002-1962</identifier><identifier>EISSN: 1435-0645</identifier><identifier>DOI: 10.2134/agronj2006.0370c</identifier><identifier>CODEN: AGJOAT</identifier><language>eng</language><publisher>Madison: American Society of Agronomy</publisher><subject>agricultural history ; agronomy ; Agronomy. Soil science and plant productions ; Biological and medical sciences ; canopy ; chlorophyll ; crop management ; crop yield ; crops ; decision making ; Fundamental and applied biological sciences. Psychology ; hyperspectral imagery ; image analysis ; leaves ; literature reviews ; nutritional status ; optical properties ; plant nutrition ; plant-water relations ; reflectance ; Remote sensing ; satellites ; shape ; species differences ; spectral analysis ; temperature ; vegetation cover</subject><ispartof>Agronomy journal, 2008-05, Vol.100 (3), p.S117-S131</ispartof><rights>Copyright © 2008 by the American Society of Agronomy</rights><rights>2008 INIST-CNRS</rights><rights>Copyright American Society of Agronomy May/Jun 2008</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c538C-84cbb1ed5082c032b83a8c802719c10bcaedd959381a3c1425266056912097eb3</citedby><cites>FETCH-LOGICAL-c538C-84cbb1ed5082c032b83a8c802719c10bcaedd959381a3c1425266056912097eb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.2134%2Fagronj2006.0370c$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.2134%2Fagronj2006.0370c$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,782,786,1419,27931,27932,45581,45582</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20390724$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Hatfield, J.L</creatorcontrib><creatorcontrib>Gitelson, A.A</creatorcontrib><creatorcontrib>Schepers, J.S</creatorcontrib><creatorcontrib>Walthall, C.L</creatorcontrib><title>Application of Spectral Remote Sensing for Agronomic Decisions</title><title>Agronomy journal</title><description>Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.</description><subject>agricultural history</subject><subject>agronomy</subject><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>canopy</subject><subject>chlorophyll</subject><subject>crop management</subject><subject>crop yield</subject><subject>crops</subject><subject>decision making</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>hyperspectral imagery</subject><subject>image analysis</subject><subject>leaves</subject><subject>literature reviews</subject><subject>nutritional status</subject><subject>optical properties</subject><subject>plant nutrition</subject><subject>plant-water relations</subject><subject>reflectance</subject><subject>Remote sensing</subject><subject>satellites</subject><subject>shape</subject><subject>species differences</subject><subject>spectral analysis</subject><subject>temperature</subject><subject>vegetation cover</subject><issn>0002-1962</issn><issn>1435-0645</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkEFLw0AUhBdRsFbv3gyCx9T3dpNNchFC1GopFlp7DpvtpqSk2bibIv33bozo0dODYeYb3hByjTChyIJ7sTW62VEAPgEWgTwhIwxY6AMPwlMyAgDqY8LpObmwdgeAmAQ4Ig9p29aVFF2lG0-X3qpVsjOi9pZqrzvlrVRjq2brldp4aV-h95X0HpWsrEvYS3JWitqqq587Juvnp_fsxZ8vpq9ZOvdlyOLMjwNZFKg2IcRUAqNFzEQsY6ARJhKhkEJtNkmYsBgFkxjQkHIOIU-QQhKpgo3J7cBtjf44KNvlO30wjavM3R8hcozAmWAwSaOtNarMW1PthTnmCHk_Uv43Uv49kovc_XCFlaIujWjcZ785CiyBiAbOlw6-z6pWx3-5eTqd0XS6XLzNerHXMse4GRil0H3C9axXFJABxgFH5OwLdHiDpQ</recordid><startdate>200805</startdate><enddate>200805</enddate><creator>Hatfield, J.L</creator><creator>Gitelson, A.A</creator><creator>Schepers, J.S</creator><creator>Walthall, C.L</creator><general>American Society of Agronomy</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X2</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M0K</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>200805</creationdate><title>Application of Spectral Remote Sensing for Agronomic Decisions</title><author>Hatfield, J.L ; Gitelson, A.A ; Schepers, J.S ; Walthall, C.L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c538C-84cbb1ed5082c032b83a8c802719c10bcaedd959381a3c1425266056912097eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>agricultural history</topic><topic>agronomy</topic><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>canopy</topic><topic>chlorophyll</topic><topic>crop management</topic><topic>crop yield</topic><topic>crops</topic><topic>decision making</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>hyperspectral imagery</topic><topic>image analysis</topic><topic>leaves</topic><topic>literature reviews</topic><topic>nutritional status</topic><topic>optical properties</topic><topic>plant nutrition</topic><topic>plant-water relations</topic><topic>reflectance</topic><topic>Remote sensing</topic><topic>satellites</topic><topic>shape</topic><topic>species differences</topic><topic>spectral analysis</topic><topic>temperature</topic><topic>vegetation cover</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hatfield, J.L</creatorcontrib><creatorcontrib>Gitelson, A.A</creatorcontrib><creatorcontrib>Schepers, J.S</creatorcontrib><creatorcontrib>Walthall, C.L</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural Science Database</collection><collection>Research Library</collection><collection>Science Database (ProQuest)</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Agronomy journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hatfield, J.L</au><au>Gitelson, A.A</au><au>Schepers, J.S</au><au>Walthall, C.L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application of Spectral Remote Sensing for Agronomic Decisions</atitle><jtitle>Agronomy journal</jtitle><date>2008-05</date><risdate>2008</risdate><volume>100</volume><issue>3</issue><spage>S117</spage><epage>S131</epage><pages>S117-S131</pages><issn>0002-1962</issn><eissn>1435-0645</eissn><coden>AGJOAT</coden><abstract>Remote sensing has provided valuable insights into agronomic management over the past 40 yr. The contributions of individuals to remote sensing methods have lead to understanding of how leaf reflectance and leaf emittance changes in response to leaf thickness, species, canopy shape, leaf age, nutrient status, and water status. Leaf chlorophyll and the preferential absorption at different wavelengths provides the basis for utilizing reflectance with either broad-band radiometers typical of current satellite platforms or hyperspectral sensors that measure reflectance at narrow wavebands. Understanding of leaf reflectance has lead to various vegetative indices for crop canopies to quantify various agronomic parameters, e.g., leaf area, crop cover, biomass, crop type, nutrient status, and yield. Emittance from crop canopies is a measure of leaf temperature and infrared thermometers have fostered crop stress indices currently used to quantify water requirements. These tools are being developed as we learn how to use the information provided in reflectance and emittance measurements with a range of sensors. Remote sensing continues to evolve as a valuable agronomic tool that provides information to scientists, consultants, and producers about the status of their crops. This area is still relatively new compared with other agronomic fields; however, the information content is providing valuable insights into improved management decisions. This article details the current status of our understanding of how reflectance and emittance have been used to quantitatively assess agronomic parameters and some of the challenges facing future generations of scientists seeking to further advance remote sensing for agronomic applications.</abstract><cop>Madison</cop><pub>American Society of Agronomy</pub><doi>10.2134/agronj2006.0370c</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
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subjects | agricultural history agronomy Agronomy. Soil science and plant productions Biological and medical sciences canopy chlorophyll crop management crop yield crops decision making Fundamental and applied biological sciences. Psychology hyperspectral imagery image analysis leaves literature reviews nutritional status optical properties plant nutrition plant-water relations reflectance Remote sensing satellites shape species differences spectral analysis temperature vegetation cover |
title | Application of Spectral Remote Sensing for Agronomic Decisions |
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