Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data
Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine w...
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| creator | Huang, Jingfeng Wei, Chen Zhang, Yao Blackburn, George Alan Wang, Xiuzhen Wei, Chuanwen Wang, Jing |
| description | Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a. |
| doi_str_mv | 10.1371/journal.pone.0137029 |
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Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0137029</identifier><identifier>PMID: 26356842</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Anthocyanins ; Anthocyanins - metabolism ; Canopies ; Carotenoids ; Carotenoids - metabolism ; Chlorophyll ; Chlorophyll - analogs & derivatives ; Chlorophyll - metabolism ; Chlorophyll A ; I.R. radiation ; Leaves ; Light ; Measurement ; Meta-analysis ; Nitrogen ; Physiological aspects ; Physiology ; Pigments ; Pigments, Biological - metabolism ; Plant Leaves - metabolism ; Plant pigments ; Plants - metabolism ; Reflectance ; Remote sensing ; Remote sensing techniques ; Remote Sensing Technology - methods ; Sensing techniques ; Spectrum Analysis - methods ; Wavelengths</subject><ispartof>PloS one, 2015-09, Vol.10 (9), p.e0137029-e0137029</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Huang et al 2015 Huang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-11bca9e4fd9c4b21feb1ec9136c9898850e88140bc762705acedb8940214759a3</citedby><cites>FETCH-LOGICAL-c692t-11bca9e4fd9c4b21feb1ec9136c9898850e88140bc762705acedb8940214759a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565675/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4565675/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23868,27926,27927,53793,53795</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26356842$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Albrectsen, Benedicte Riber</contributor><creatorcontrib>Huang, Jingfeng</creatorcontrib><creatorcontrib>Wei, Chen</creatorcontrib><creatorcontrib>Zhang, Yao</creatorcontrib><creatorcontrib>Blackburn, George Alan</creatorcontrib><creatorcontrib>Wang, Xiuzhen</creatorcontrib><creatorcontrib>Wei, Chuanwen</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><title>Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Huang, Jingfeng</au><au>Wei, Chen</au><au>Zhang, Yao</au><au>Blackburn, George Alan</au><au>Wang, Xiuzhen</au><au>Wei, Chuanwen</au><au>Wang, Jing</au><au>Albrectsen, Benedicte Riber</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-09-10</date><risdate>2015</risdate><volume>10</volume><issue>9</issue><spage>e0137029</spage><epage>e0137029</epage><pages>e0137029-e0137029</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Passive optical hyperspectral remote sensing of plant pigments offers potential for understanding plant ecophysiological processes across a range of spatial scales. Following a number of decades of research in this field, this paper undertakes a systematic meta-analysis of 85 articles to determine whether passive optical hyperspectral remote sensing techniques are sufficiently well developed to quantify individual plant pigments, which operational solutions are available for wider plant science and the areas which now require greater focus. The findings indicate that predictive relationships are strong for all pigments at the leaf scale but these decrease and become more variable across pigment types at the canopy and landscape scales. At leaf scale it is clear that specific sets of optimal wavelengths can be recommended for operational methodologies: total chlorophyll and chlorophyll a quantification is based on reflectance in the green (550-560nm) and red edge (680-750nm) regions; chlorophyll b on the red, (630-660nm), red edge (670-710nm) and the near-infrared (800-810nm); carotenoids on the 500-580nm region; and anthocyanins on the green (550-560nm), red edge (700-710nm) and near-infrared (780-790nm). For total chlorophyll the optimal wavelengths are valid across canopy and landscape scales and there is some evidence that the same applies for chlorophyll a.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26356842</pmid><doi>10.1371/journal.pone.0137029</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Anthocyanins Anthocyanins - metabolism Canopies Carotenoids Carotenoids - metabolism Chlorophyll Chlorophyll - analogs & derivatives Chlorophyll - metabolism Chlorophyll A I.R. radiation Leaves Light Measurement Meta-analysis Nitrogen Physiological aspects Physiology Pigments Pigments, Biological - metabolism Plant Leaves - metabolism Plant pigments Plants - metabolism Reflectance Remote sensing Remote sensing techniques Remote Sensing Technology - methods Sensing techniques Spectrum Analysis - methods Wavelengths |
| title | Meta-Analysis of the Detection of Plant Pigment Concentrations Using Hyperspectral Remotely Sensed Data |
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