Loading…
Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image
Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 5106 |
container_issue | |
container_start_page | 5103 |
container_title | |
container_volume | |
creator | Bouroubi, Yacine Tremblay, Nicolas Vigneault, Philippe Benoit, Mathieu |
description | Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Montérégie region of Quebec, Canada. EO derived soil properties were strongly correlated to EC a . Correlation between dark soil abundance and EC a reached R=0.9 and correlation between bright soil abundance and EC a was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications. |
doi_str_mv | 10.1109/IGARSS.2014.6947645 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_6947645</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6947645</ieee_id><sourcerecordid>6947645</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-f9f9324875549b36bc55221eaea534ad493bb68969188b3ba5556ef8db1be3513</originalsourceid><addsrcrecordid>eNotkNtKw0AURUdRsK1-QV_mBxLndiaZx1K0FgKC1ecyk5wpI7kxibb-vUHztBebfS5sQtacpZwz87jfbd4Oh1QwrlJtVKYVXJElV5kxkGUA12QhOMgkY0zezKyN0XdkOQyfk5kLxhbEFqFFG-nQYzlGW9OvtgmX0J6o7yItY9dT21Z06EJNQzt5jR1D11K8TOnyD33sGmrpMA3VSM9drKvvgOdE0NDYE96TW2_rAR9mXZGP56f37UtSvO72202RBJ7BmHjjjRQqn15XxkntSgAhOFq0IJWtlJHO6dxow_PcSWcBQKPPK8cdSuByRdb_ewMiHvs4HY8_x7ka-QteVFep</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image</title><source>IEEE Xplore All Conference Series</source><creator>Bouroubi, Yacine ; Tremblay, Nicolas ; Vigneault, Philippe ; Benoit, Mathieu</creator><creatorcontrib>Bouroubi, Yacine ; Tremblay, Nicolas ; Vigneault, Philippe ; Benoit, Mathieu</creatorcontrib><description>Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Montérégie region of Quebec, Canada. EO derived soil properties were strongly correlated to EC a . Correlation between dark soil abundance and EC a reached R=0.9 and correlation between bright soil abundance and EC a was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications.</description><identifier>ISSN: 2153-6996</identifier><identifier>EISSN: 2153-7003</identifier><identifier>EISBN: 1479957755</identifier><identifier>EISBN: 9781479957750</identifier><identifier>DOI: 10.1109/IGARSS.2014.6947645</identifier><language>eng</language><publisher>IEEE</publisher><subject>Agriculture ; Correlation ; crop status observation ; Data mining ; Linear spectral unmixing ; precision farming ; Soil measurements ; Soil properties ; Vegetation mapping</subject><ispartof>2014 IEEE Geoscience and Remote Sensing Symposium, 2014, p.5103-5106</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6947645$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6947645$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Bouroubi, Yacine</creatorcontrib><creatorcontrib>Tremblay, Nicolas</creatorcontrib><creatorcontrib>Vigneault, Philippe</creatorcontrib><creatorcontrib>Benoit, Mathieu</creatorcontrib><title>Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image</title><title>2014 IEEE Geoscience and Remote Sensing Symposium</title><addtitle>IGARSS</addtitle><description>Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Montérégie region of Quebec, Canada. EO derived soil properties were strongly correlated to EC a . Correlation between dark soil abundance and EC a reached R=0.9 and correlation between bright soil abundance and EC a was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications.</description><subject>Agriculture</subject><subject>Correlation</subject><subject>crop status observation</subject><subject>Data mining</subject><subject>Linear spectral unmixing</subject><subject>precision farming</subject><subject>Soil measurements</subject><subject>Soil properties</subject><subject>Vegetation mapping</subject><issn>2153-6996</issn><issn>2153-7003</issn><isbn>1479957755</isbn><isbn>9781479957750</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkNtKw0AURUdRsK1-QV_mBxLndiaZx1K0FgKC1ecyk5wpI7kxibb-vUHztBebfS5sQtacpZwz87jfbd4Oh1QwrlJtVKYVXJElV5kxkGUA12QhOMgkY0zezKyN0XdkOQyfk5kLxhbEFqFFG-nQYzlGW9OvtgmX0J6o7yItY9dT21Z06EJNQzt5jR1D11K8TOnyD33sGmrpMA3VSM9drKvvgOdE0NDYE96TW2_rAR9mXZGP56f37UtSvO72202RBJ7BmHjjjRQqn15XxkntSgAhOFq0IJWtlJHO6dxow_PcSWcBQKPPK8cdSuByRdb_ewMiHvs4HY8_x7ka-QteVFep</recordid><startdate>201407</startdate><enddate>201407</enddate><creator>Bouroubi, Yacine</creator><creator>Tremblay, Nicolas</creator><creator>Vigneault, Philippe</creator><creator>Benoit, Mathieu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201407</creationdate><title>Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image</title><author>Bouroubi, Yacine ; Tremblay, Nicolas ; Vigneault, Philippe ; Benoit, Mathieu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f9f9324875549b36bc55221eaea534ad493bb68969188b3ba5556ef8db1be3513</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agriculture</topic><topic>Correlation</topic><topic>crop status observation</topic><topic>Data mining</topic><topic>Linear spectral unmixing</topic><topic>precision farming</topic><topic>Soil measurements</topic><topic>Soil properties</topic><topic>Vegetation mapping</topic><toplevel>online_resources</toplevel><creatorcontrib>Bouroubi, Yacine</creatorcontrib><creatorcontrib>Tremblay, Nicolas</creatorcontrib><creatorcontrib>Vigneault, Philippe</creatorcontrib><creatorcontrib>Benoit, Mathieu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bouroubi, Yacine</au><au>Tremblay, Nicolas</au><au>Vigneault, Philippe</au><au>Benoit, Mathieu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image</atitle><btitle>2014 IEEE Geoscience and Remote Sensing Symposium</btitle><stitle>IGARSS</stitle><date>2014-07</date><risdate>2014</risdate><spage>5103</spage><epage>5106</epage><pages>5103-5106</pages><issn>2153-6996</issn><eissn>2153-7003</eissn><eisbn>1479957755</eisbn><eisbn>9781479957750</eisbn><abstract>Spatial information related to vegetation status and soil properties is needed in precision farming, especially early in the growing season. At these stages, vegetation has already emerged while soil is also visible in multispectral EO images. In this paper, linear spectral unmixing is applied to an 8 bands WorldView-2 image to extract information on both vegetation and soil acquired at in-season nitrogen sidedress stage in 2010 and 2011 for four corn fields located in the Montérégie region of Quebec, Canada. EO derived soil properties were strongly correlated to EC a . Correlation between dark soil abundance and EC a reached R=0.9 and correlation between bright soil abundance and EC a was about R=-0.7. Vegetation abundance for combined data of several fields was better correlated to measured biomass than NDVI and SAVI. The possibility to get valuable soil and plant information from a single multispectral image offers an interesting cost reduction opportunity for precision farming applications.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS.2014.6947645</doi><tpages>4</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2153-6996 |
ispartof | 2014 IEEE Geoscience and Remote Sensing Symposium, 2014, p.5103-5106 |
issn | 2153-6996 2153-7003 |
language | eng |
recordid | cdi_ieee_primary_6947645 |
source | IEEE Xplore All Conference Series |
subjects | Agriculture Correlation crop status observation Data mining Linear spectral unmixing precision farming Soil measurements Soil properties Vegetation mapping |
title | Linear spectral unmixing for crop and soil information extraction from a single worldview-2 image |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T06%3A54%3A34IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Linear%20spectral%20unmixing%20for%20crop%20and%20soil%20information%20extraction%20from%20a%20single%20worldview-2%20image&rft.btitle=2014%20IEEE%20Geoscience%20and%20Remote%20Sensing%20Symposium&rft.au=Bouroubi,%20Yacine&rft.date=2014-07&rft.spage=5103&rft.epage=5106&rft.pages=5103-5106&rft.issn=2153-6996&rft.eissn=2153-7003&rft_id=info:doi/10.1109/IGARSS.2014.6947645&rft.eisbn=1479957755&rft.eisbn_list=9781479957750&rft_dat=%3Cieee_CHZPO%3E6947645%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-f9f9324875549b36bc55221eaea534ad493bb68969188b3ba5556ef8db1be3513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6947645&rfr_iscdi=true |