ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
137
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
༂Нႁ ȃ೩݃ਦ ȃ྇‫ע‬ᆱ ȃച࡛ይ ȃጿᎵҔ ȃ஼ུଗ ȃ྇ෙ݃
1
2
2
2
1
୾ҴᇄᢋτᏱၼ᛻Ᏹ‫ق‬
ϜѶः‫ف‬ଲҢ‫ޑ‬Ӽኻ‫ܓ‬ः‫ف‬ϜЗ
1
1,2*
1
2
ᄣ्
Ґः‫ف‬пϛӤҢ‫෉ي‬ಒ 60ȃ90ȃ360 І 540
Љϟҡጶ (Saccharum officinarum L.) ဩб࣐؆
ਠȂໍ՘ဩбЇৣӏᜋขۢȂ‫ٯ‬пЇৣ౦ዂᔤॏ
ᆘፐ࢒ሎขෛҢࡿ኶(vegetation index, VI)Ȃզ
ᆘ‫ڐ‬ဩбϟဩᆧષ(chlorophyll, Chl)֥໕Ȅᓎ຀
Ң‫ي‬С኶‫ޠ‬ቩђȂҡጶဩбဩᆧષІ᜹ऩᡏፀષ
֥໕ഃᅛ෶ЎȄҦЇৣӏᜋϸ‫ݚ‬ᡘұȂҡጶဩб
Ӷ 550 nm ᇅ 710 nm ϟЇৣ౦ᄈՔષ֥໕ᡑϾ
‫ڏ‬ԥഷτఄད࡚Ȅпߗकѵӏ‫ ࢳݱ‬750 nm Їৣ
౦ϸրᇅ 710 nm І 550 nm Їৣ౦ॏᆘෛҢࡿ
኶ SRVI (simple ratio vegetation index)ȃNDVI
(normalized difference vegetation index)ᇅ GI
(greenness index)ȃNDVI ȂпІዂᔤ SPOT
ፐ࢒ቷ‫ࢳݱ‬҂ְЇৣ౦ॏᆘෛҢࡿ኶ SRVI
І NDVI ࡤȂᇅဩбՔષ֥໕ໍ՘ଠᘫϸ
‫ݚ‬ȂѠூ‫ژ‬ဩᆧષ֥໕զᆘРโԓȄпԫϳಣР
โԓզᆘҔளҢߞϟဩбဩᆧષ֥໕Ȃ‫ྦڐ‬ጃ࡚
ࣻ࿌ାȇկզᆘ‫ڨ‬ଜძ֥߇ߨષϟဩбဩᆧષ֥
໕ਣȂࠍԥ݃ᡘϟᇳৰȄҐः‫ف‬๗‫ݏ‬ᡘұւңЇ
ৣӏᜋॏᆘෛҢࡿ኶Ȃߩખᚾ‫ܓ‬ӵզᆘҔளҢߞ
‫ݸޒ‬ίϟဩбဩᆧષ֥໕ϟᡑϾȂໍՅᆀขձ‫ޑ‬
Ңߞ‫ݸޒ‬Ȃ࣐ԥਞѠ՘‫ޠ‬Р‫ݳ‬Ȅ
ᜱᗥມȈҡጶȃЇৣ౦ȃፐ࢒ሎขȃෛҢࡿ኶ȃ
ဩᆧષȃ߇ߨષȄ
Green
broad
broad
೾߭ձ޲, [email protected]
‫׺‬ጉС෉Ȉ2004 Ԓ 10 У 1 С
௦‫ڨ‬С෉Ȉ2005 Ԓ 1 У 10 С
ձ‫ޑ‬ȃᕘძᇅҢ‫ޑ‬ၦଊ 2:137-147 (2005)
*
Crop, Environment & Bioinformatics 2: 137-147 (2005)
189 Chung-Cheng Rd., Wufeng, Taichung Hsien 41301, Taiwan
(ROC)
Mimicking Satellite Remote Sensing
of Chlorophyll Content in Sugarcane
(Saccharum officinarum) Leaves
Wen-Dar Huang1, Ming-Huang Hsu2, ZhiWei Yang2, Jane-Chang Chen2, Yang-Zenq
Tsai1, Shin-Shinge Chang1, and Chi-Ming
Yang1,2*
1
2
Deparment of Agronomy, National Taiwan
University, Taipei 106, Taiwan (ROC)
Research Center for Biodiversity, Academia Sinica,
Nankang, Taipei 115, Taiwan (ROC)
ABSTRACT
The reflectance spectra of sugarcane (Saccharum
officinarum) leaves at 60, 90, 360 and 540 days after
planting were measured to calculate vegetation indices
to mimic the satellite remotely estimate of chlorophyll
content. The contents of pigments declined following
the increasing of days after planting. The signature
analysis of reflectance spectra indicated that the
maximum sensitivity to chlorophyll content in the
leaves of sugarcane was at 550 nm and 710 nm. SRVI
(simple ratio vegetation index) and NDVI (normalized
difference vegetation index) calculated using
reflectance at 750 nm and 710 nm and GI (greenness
index) and NDVIGreen calculated using reflectance at
750 nm and 550 nm correlated very well with
chlorophyll content. SRVIbroad and NDVIbroad calculated
using simulated broad-band reflectance of SPOT
satellite also correlated well with chlorophyll content.
The developed algorithms predicting leaf chlorophyll
from the leaf optics were validated from two
independent data. A significant error in chlorophyll
prediction of the stressed leaves containing
anthocyanin was observed. Results suggest that it is
feasible to create indices using reflectance spectra for
non-destructive estimation of chlorophyll content, by
which to indirectly monitor crop growth under normal
environment.
Crop, Environment & Bioinformatics, Vol. 2, June 2005
138
Key words: Sugarcane, Satellite remote sensing, Vegetation indices, Chlorophyll, Anthocyanin.
ࠊّ
ᆧՔෛ‫ޑ‬ဩб֥ԥτ໕‫ޠ‬ӏӬՔષ
(photosynthetic pigments) Ȃ є ࢃ ဩ ᆧ ષ
(chlorophyll, Chl) ‫ڸ‬᜹ऩᡏፀષ (carotenoid,
Car)Ȃ૗ԥਞӵ֝ԞѠُӏ‫ޠ‬कӏ‫ڸ‬ᙣӏ‫ࢳݱ‬
ໍ՘ӏӬձңȂໍՅ஡ӏ૗ᙾᡑ࣐ϾᏱ૗п‫ٽ‬
ւңȄෛ‫ޑ‬ဩб‫ޠ‬ӏӬՔષ֥໕ϛ༊ӶϛӤ‫ޠ‬
ึ‫ي‬໧ࢳԥ‫ܛ‬ᡑϾȂӶՃϾ‫ޠ‬ႇโϜҼഃᅛӵ
෶ЎӏӬՔષ֥໕Յ‫׾‬ᡑᚠՔȄဤဩෛ‫ޑ‬Ӷऎ
ЉဩбՃϾႇโϜȂ‫ڐ‬ဩбՔષ፵໕౱Ңᡑ
ϾȂဩбᚠՔηӱՅᙾᡑ(Hendry et al. 1987,
Matile et al. 1989, Matile et al. 1992)Ȅෛ‫ޑ‬᎐‫ڨ‬
ଜძਣȂឌԄౄЬȃୂ‫؁‬ȃାմྤȃଷ૪Ꮩ‫ܗ‬
੿ᙬড়ਣȂဩбՔષ֥໕ᇅᚠՔᡑϾᇅՍดՃ
Ͼਣϟ௒‫ࣻם‬ծ(Hendry et al. 1987)ȄӱԫȂ
ဩᆧષ֥໕Ѡձ࣐፞ԄӏӬձң૗ΩȃҢߞึ
‫ي‬໧ࢳȃҢ౱ΩІଜძ།ড়ϟҢ౪ࡿዀ
(Whittaker and Marks 1975, Danks et al.
Ȅ
༉ಜ‫ޠ‬Քષϸ‫ݚ‬Р‫ݳ‬ᙥҦԥᐡྚᏙ‫ޠ‬๶
‫ࡤڦ‬пӏႬЩՔॏ‫ܗ‬ାਞషࣻՔቺϸ‫ݳݚ‬
(High Performance Liquid ChromatographyȂ
HPLC) ๊ขۢϟ (Arnon 1969, Porra et al.
1989, Yang et al. 1998)Ȃԫࣲ࣐ખᚾ‫ܓ‬ϟ‫ڦ‬ኻ
Р‫ݳ‬Ȃณ‫ݳ‬ଭᄈ൑Κ‫׈‬ᐍဩбӶϛӤਣโϜՔ
ષ֥໕ϟᡑϾໍ՘ᆀ௢Ȃйຳਣέ૊ϏȄࣻᄈ
ӵȂпဩб‫ܗ‬ෛࠆЇৣӏᜋขۢဩбՔષ֥
໕Ȃ‫ڏ‬ԥߩખᚾ‫ܓ‬ȃ‫ץ‬ഁέࣹϏϟ੬‫(ܓ‬Baret et
1983)
al. 1987, Baret et al. 1992, Buschmann and
Nagel 1993, Chappelle et al. 1992, Curran et
al. 1991, Gamon and Qiu 1999, Gitelson and
Merzlyak 1994a, b, 1997, Hsu et al. 2003,
Sims
and
Gamon
Gausman 1977)
2002,
Thomas
and
ȄҦܼဩᆧષӶकӏІᙣӏ‫ݱ‬
ࢳІ᜹ऩᡏፀષӶᙣӏ‫ࢳݱ‬എ‫ڏ‬ԥ஽֝Ԟ‫ޠ‬
੬‫ܓ‬Ȃӱԫෛ‫ޑ‬ဩб‫ܗ‬ෛࠆࣲѠᙥԫ੬‫ܓ‬ඣᛳ
я‫ڐ‬Їৣӏᜋ੬ኊԣጤ(Fuchs 1990)ȄҦЇৣ
ӏᜋ‫ޠ‬ϸ‫ݚ‬ᡘұȂϛӤ‫ࢳݱ‬ЇৣЩঅ‫ޠ‬ᡑϾᇅ
ձ‫ޠޑ‬Ң౪ЇᔗІҢߞ‫ݸޒ‬ԥஞϹᜱ߾(Bauer
1975, Ma et al. 1996, Masoni et al. 1997,
ȂՅпϛӤ‫ࢳݱ‬ЇৣЩঅ
‫ܛ‬ॏᆘϟөᆎෛҢࡿ኶(vegetation index)Ȃࠍ
‫ژڨ‬ဩб‫يึޠ‬ІЬϸ֥໕ȃՔષ֥໕ȃಡब
ᏪಣԚȃဩߓ८੬ኊІဩбϲഌ๗ᄻ๊ϛӤӱ
φϟኈ៫(Elvidge and Chen 1995)ȄҔளϟᆧ
Քෛ‫ޑ‬ԥ֝ԞᙣӏȃकӏІ஽੩Їৣकѵӏϟ
੬‫ܓ‬Ȃဩᆧષ‫ڸ‬᜹ऩᡏፀષӶᙣӏϟ஽֝Ԟ‫ݱ‬
ࢳϤԥ२᠓Ȃࢉ೾ளၷϛпᙣӏ‫ࢳݱ‬զᆘဩᆧ
ષ֥໕Ȅளңܼෛ‫ޑ‬ၦྜ௥ข‫ޠ‬ෛҢࡿ኶ȂӼ
‫ٻ‬ңकӏᇅᇅߗकѵӏϟЇৣЩঅ‫ܗ‬ৰঅ
(Elvidge and Chen 1995)Ȅᇑӱෛ‫ޑ‬Ңߞ຺ܽ
౿Ȃ‫֝ڐ‬Ԟϟकӏ຺ӼІЇৣϟकӏ຺ЎȂՅ
Їৣϟߗकѵӏ຺஽ȂकӏᇅߗकѵӏϟЇৣ
ৰ౵(reflectance discrepancy)‫ܗ‬Щঅ(ratio)֊
຺τ (Green et al. 1997, Price and Bausch
1995) Ȅဩбဩᆧષϟकӏ஽֝Ԟ‫ࢳݱ‬ङ՞ܼ
675 nm ѿѢȂկહ‫ ޠ‬Chl a ‫ ܗ‬Chl b Ӷकӏୣ
஀ 645 І 663 nm ԥ஽֝ԞȂй‫ྚٸ‬ᏙՅԥᡑ
ϾȂկৰ౵ϛτȄဩбЇৣӏᜋ੬ኊᡘұ‫ޠ‬ᔗ
ϛ࢑હ‫ޠ‬ဩᆧષȂՅ࢑ဩᆧષ೗ҪፓӬᡞ
(Chl-protein complex)ϟ᜹‫ޠ‬੬ኊȂ675 nm ᔗ
࢑ဩᆧષᇅ࢛‫ٳ‬೗Ҫ፵๗ӬԚ Chl-protein
complex ϟ஽֝ԞȄᗷดဩбဩᆧષӶ 675 nm
ѿѢԥ஽֝ԞȂկԫΚ‫ࢳݱ‬ጓ൝ϟЇৣ౦ϛᎍ
ңܼզᆘဩᆧષ֥໕Ȃӱ࣐ဩᆧષմᐩ࡚ίӶ
675 nm ѿѢ‫ࢳݱ‬ϟ֝Ԟ֊ϑႁႺ‫ڸ‬ȂԫΚ‫ݱ‬
ࢳᄈմᐩ࡚ϟဩᆧષ֥໕ᡑϾ‫ڏ‬ԥၷାϟఄ
ད࡚Ȃկ࢑ᄈܼၷାᐩ࡚ဩᆧષ֥໕ᡑϾϟఄ
ད࡚ࠍၷմ (Gitelson and Merzlyak 1994a,
1996, 1997, Hsu et al. 2003)ȄᄈܼϑႁႺ‫֝ڸ‬
Ԟϟၷାᐩ࡚ဩᆧષ֥໕ϟզᆘȂॏᆘෛҢࡿ
Walburg et al. 1982)
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
»Í$
õö 550 n 700 nm æçe
œ÷(Buschman and Nagel 1993, Datt 1998,
1999, Gitelson and Merzlyak 1994a, b, 1996,
Hsu et al. 2003, Lichtenthaler et al. 1996,
Schepers et al. 1996, Sims and Gamon 2002,
Thomas and Gausman 1977, Yoder and
/
Ґः‫ف‬,03I5෉eҡጶ)؆
ਠ!"œž†‡‫ٯ‬,œ÷ዂᔤ
SPOT ፐ࢒ሎ† ‚¸Iq»Ö¸
࡛Ҵሎ†Ö¸eዂ
ԓ/
Waring 1994)
؆ਠᇅР‫ݳ‬
Κȃၑᡜ؆ਠ
Ґः‫ف‬,პဏᑫഫ\໑7ºeҡጶ
(Saccharum officinarum L.) ROC 10 ဵ)؆
ਠ/c 2002 Ԓ 7 УՎ 2003 Ԓ 10 У෉໣Ӓ
uѳ‫״‬º௵ӓೂ़º޴ਠ$
N-P O -K O = 195-36-72 kg ha /ì03P෉
ºeҥê%௵Œ~ூI5෉ 60U90U360U
540 Eeҡጶੂ~Fಒή’ӓ৥໡e;
Æå!"œžn b
e†‡/
2
5
-1
2
ΡȃЇৣӏᜋขۢ
œž,ପറᑗu౩ (integrating
sphere)৉ӈe Hitachi U-3010 ž቉(spectroradiometer) !"†‡/ž௮¨¢÷) 600
nm min õöc 200 Վ 900 nmž
၍vl) 1 nm/†‡P,ಆሗᎤï‫)ݗ‬୥Մ
/†‡œžP,૖໣eêë)
лœ÷)œᒮ°୥Մï‫œݗ‬
ᒮ°e°/ᙐܿIq»(simple ratio
vegetation index, SRVI) e ‚ ¸ w x )
R /R R )ÜÏ œ÷‘ૢ
-1
NIR
RED
NIR
139
) 750 Վ 900 nm e໣% R ) œ
÷‘ૢ) 660 Վ 720 nm e໣/Íᄙ*Ý
âIq» (normalized difference vegetation index, NDVI) e‚¸wԓ) NDVI =
(R ɯ R )/(R ɮ R ) ȇ GI (greenness
index) e ‚ ¸ ϵ ԓ ) GI = R /R
ȇ
NDVI
e ‚ ¸ ϵ ԓ ) NDVI =
(R -R
)/(R +R
)/ѫҐः‫<ف‬ዂᔤx
୾ SPOT ×Øፐ࢒enÜÏ ‚
¸ ቷ (broad-band) e SRVI n
NDVI
(Hsu et al. 2003)/
RED
NIR
RED
NIR
RED
NIR
Green
Green
NIR
Green
Green
NIR
Green
broad
broad
ήȃဩᆧષ(Chl)І᜹ऩᡏፀ(Car)‫ޠ‬ขۢ
ҡጶ,ƒᄙ෩ࡩ¢տঞ‫ٯ‬,ःನᑒ
¾લ!"տঞVᕎ/dદ~ 0.01 g Œࠣ
¾લ, 80%ж⢅}~2 4,500 rpm ᚕ
З 5 uយ~αఽƒ, Hitachi U-2000 ž
቉†‡ A UA n A °/ή޲
uր) Chl aUChl b n Car §೏/,
Porra et al. (1989)
ϵԓ‚¸ Chl a b Chl b ȇ, Holm (1954)
wx‚¸ Car /
663.6
646.6
440.5
ѳȃ߇ߨષขۢ
દ~ 0.01g Œࠣ¾લ,Å 1 % HCl e
ҧ᎖(methanol)}~߇ߨF}~ƒ¥ఌ'
ె{ഀ៊}~ 2 ϊP¥ 4ʨn 2000 rpm
ûᚕЗ 15 uយ~Fαఽƒ, Hitachi
U-2000 uú‚†‡ A657 n A530 °‫ٯ‬, Mancinelli et al. (1975)
wx‚¸߇
ߨᖃ/
๗‫ݏ‬ᇅଇ፤
ҡጶ Chl aUChl b b Car ᓎ
຀I5෉eቩђ%HIൾú(*(Table 1)
j<œ࢏2œže(*α(Fig. 1)/ᓎ
຀I5С»
ቩђҡጶ Chl n Car ᖃ
=>û७Chl ᖃz 8,721 µg g- ൾ७
1
Crop, Environment & Bioinformatics, Vol. 2, June 2005
140
e Chl bœže(*xyb8*
9:;ehi/
yzuvœž;œ÷
(â
z5ž(*xyn
ú
) 2,905 µg g Chl a/b °¼z 3.84 p
3.58(*mú0j®¯ Chl a ?@
¢÷b Chl b ?@¢÷hÝ0/Car z 3,636 µg g p) 1,449 µg g Car/Chl
°¼2 0.42 0.50 e(*0®¯
Car (*M½ Chl ÊËq[
´µ¶·/%œž2 400 700 nm
êëeœ÷klr, 550 nm æç
œ÷klmús% 700 nm æçe
tuê뜝÷<v®klèkmÕf/2
,DwC)
;®¯2
8*9:;Chl ?@2 510-600 nm n
690 nm ,Y eœ÷v®kl(Gitelson
and Merzlyak 1994a, 1996)/%;
-1
-1
-1
(Gitelson and Merzlyak 1997, Hsu et al.
/(âß®¯(âeú
ßEœe¼ßf/žœ÷er
{Ý(standard deviation, STD)®¯m 500
nm ,ûnmä 750 nm ,YeÜÏ
(âeúY ä 675 nm |܏(*e
ú<Y/œ÷(âŸX
úe ä 550 nm |Ü nä 710
nm |Üe (Fig. 2)/
2003)
Table 1. The contents of chlorophyll (Chl) and carotenoid (Car) in sugarcane leaves at various developmental stages.
Days after planting
Pigments
60
90
360
540
6918 ± 149
5749 ± 132ʳ
4821 ± 144ʳ
2271 ± 44ʳ
1803 ± 19ʳ
1459 ± 47ʳ
1343 ± 96ʳ
634 ± 22ʳ
Chl a+b (µg g-1 DW)ʳ
8721 ± 155ʳ
7208 ± 176ʳ
6164 ± 230ʳ
2905 ± 64ʳ
Chl a/b ratioʳ
3.84 ± 0.08ʳ
3.94 ± 0.06ʳ
3.60 ± 0.18ʳ
3.58 ± 0.08ʳ
Car (µg g-1 DW)
3636 ± 105ʳ
3149 ± 82ʳ
2816 ± 75ʳ
1449 ± 28ʳ
Car/Chl ratioʳ
0.42 ± 0.01ʳ
0.44 ± 0.00ʳ
0.46 ± 0.01ʳ
0.50 ± 0.00ʳ
Chl a (µg g-1 DW)
Chl b (µg g-1 DW)
†
ʳ
† Data presented as mean ± standard error (SE).
Fig. 1. Reflectance spectra of sugarcane leaves at 60, 90, 360, 540 days after planting.
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
‫ ߞݱ‬550 nm І 710 nm ϟЇৣ౦(R ȃ
R )ᇅҡጶဩбϟ Chl ֥໕֖౫ା࡚ϟԣጤࣻ
ᜱ(Fig. 3)ȄԫҼᡘұȂԫΡ‫ࢳݱ‬Їৣ౦ᄈ Chl
֥໕ᡑϾϟఄད࡚ାȂ‫߾ۢ؛ڐ‬኶ R অϸր࣐
0.92 І 0.91Ȅң‫ٿ‬ॏᆘෛҢࡿ኶пໍ՘Քષ֥
໕զᆘϟ‫ࢳݱ‬Ȃ҇໹ᄈՔષ֥໕‫ڏ‬ԥାఄད
࡚Ȃйϛܿ‫ڐڨ‬уӱφኈ៫ȄࣻᄈՅّȂኈ៫
550
710
2
141
೼‫ࢳݱٳ‬Їৣ౦ϟл्ӱφ֊࣐өᆎՔષȄ
550 nm ‫ࢳݱޠ‬Іङ 710 nm ߤߗϟ‫ࢳݱ‬ᄈՔષ
֥໕‫ڏ‬ԥାఄད࡚Ȃ750 Վ 900 nm ϟߗकѵ
ӏ‫ࢳݱ‬Їৣ౦ (R ) ᄈՔષ֥໕ᡑϾఄད࡚
մȂկѠңܼෛҢࡿ኶ϟॏᆘȄҐः‫ٻܛف‬ң
ϟෛҢࡿ኶֊п R ᇅ R ॏᆘ NDVI ȃ
SRVIȂпІ R ᇅ R ॏᆘ GI ᇅ NDVI
Ȅ
NIR
750
750
710
550
Green
Fig. 2. Standard deviation (STD) of spectral reflectance obtained from sugarcane leaves.
Fig. 3. The reflectance at the wavelengths 710 nm ( ) and 550 nm ( ) versus total Chl content in sugarcane leaves.
Crop, Environment & Bioinformatics, Vol. 2, June 2005
142
4aWbcdefghi jGklmnopWb#qrstu
gvwxyz{I|PWb}~€
‚vwƒ„4hi …,†‡1
ˆWb‰w4uŠ SPOT ‚,‹
$ŒbŽr (multispectral scanner)<=
‘ *+,’W XS1 (500~590
nm)UW XS2 (610~680 nm)SUVW
XS3 (790~890 nm)CG\]^“”
SPOT ‚UWSUVW /0•
(broad-band) SRVI NDVI
(Hsu et al. 2003)
\]^_`-123( Chl –
—˜™š›œ(*+ Table 2 _IJ
broad
broad
œ(ž9078Ÿ ¡¢90
Chl —˜™š›£$—˜%&'(
¤[ 0.86 4a90¥¦(estimation error)
Qg 706 T 879 µg g §$£4 NDVI
™š›,¨©$ R =0.91p < 0.0019
0¥¦ª= 706 µg g ,žw«¡¢—˜™š›9078
Ÿ \]^*+4?¬­8¬­
8(Table 3)®¢¯°Wb78±²56
³&4¡¢—˜™š›90´µ¶78
·w)¸78¹w)56º»
$¼½ Fig. 4 ¾ Fig. 5 _4 NDVI
SRVINDVI
SRVI GI NDVI
™š›90¿ÀÁ¬­8
Chl -1
Green
2
-1
broad
broad
Green
Table 2. The algorithms for Chl estimation in sugarcane leaves.
Chl = b + a [VI]‡
Vegetation index
(VI)
R2
a
b
p value
Estimation errorˆ
NDVI
0.91
21775
-2176
< 0.001
711
SRVI
0.87
4142
-3424
< 0.001
841
NDVIbroad
0.88
33934
-16270
< 0.001
816
SRVIbroad
0.86
2059
-4278
< 0.001
879
GI
0.88
3282
-3322
< 0.001
823
NDVIgreen
0.91
23188
-4783
< 0.001
706
‡ VI, vegetation indices. NDVI and SRVI were calculated using R710 and R750; NDVIbroad and SRVIbroad were
calculated using broad-band reflectance, Red: 610-680 nm; NIR: 790-890 nm; GI and NDVIgreen were calculated
using R550 and R750.
ˆ Estimation error was an estimation error of Chl, and R2 was the determination coefficient of samples measured. a,
b and estimation error were in µg g-1 dry wt.
Table 3. The contents of chlorophyll (Chl), carotenoid (Car), and anthocyanin in sugarcane leaves of two
independent data sets for validation of vegetation indices.
Validation data sets
Pigments
Chl a (µg
Set 1
g-1
DW)
Chl b (µg g-1 DW)
Chl a+b (µg
g-1
DW)ʳ
Chl a/b ratioʳ
Car (µg g-1 DW)
Car/Chl ratioʳ
Anthocyanin [(A530-0.333A657)/g DW]
† Data presented as range.
3,200 -
7,434†
807 - 2,089
4,007 - 9,523
3.56 - 4.15
1,808 - 3,822
0.40 - 0.48
0
Set 2
235 - 1,041
227 -
432
463 - 1,376
1.04 -
3.30
362 - 1,041
0.40 -
1.01
9.98 - 83.12
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
·w)¹w)%&'( R *+, 0.90
0.920.780.820.930.9190¥¦*+
, 595503816728424470 µg g (Fig.
4)G4¡¢—˜™š›90AÃÄ=
¬­8
Chl ·w)Źw)
=ÆÇP¦ÈÉ SRVI ™š›¥¦»Ê
VNDVINDVI SRVI ™š›90
Chl ·w)Å=ËX9Ì$/
0Í.ÎTÍÏ)ÐG4 GI NDVI
™š›90 Chl ·w)Å=
9Ì(Fig. 5)
Chl gXÑÒ[ 675 nm ÓÔ ÕÖM×ØÙ¾[×ØÙ¾ÕÖ»Ñ
Chl 90/0123(_`-U
W ŒÚÛ 700 nm ÓÔ(Gitelson
2
-1
broad
broad
Green
and Merzlyak 1994a, 1994b, 1996, Hsu et al.
2003, Lichtenthaler et al. 1996 Schepers et al.
1996, Sims and Gamon 2002, Thomas and
\
]^£?Â2ÜÝ
$ Chl j
Þ[Ñ4 R R /0
NDVISRVINDVI
SRVI 90 Chl
™š›4¯°¿À±²56³&
ߟ ÆGAÃÄàá>=¬­
8$ Chl ZâgÙ¾ÕÖÑ
ÒgUWãäZâåæ Chl PçèGé2ÇêëPìíÐîVgìï
ÒUWðñPòópô R Ëêë
õ`4±²/0123()»X-49
0 Chl ´é2·w)X[¹w)Ì
göŒ]^£4’W /01
23(Z…, Chl P3÷ (Buschmann
Gausman 1977, Yoder and Waring 1994)
750
broad
710
broad
710
and Nagel 1993, Carter 1993, 1994, Gitelson
and Merzlyak 1994a, 1996, 1997, Gitelson et
4 log(R /R )/03(Z…,
øùhò‡ú Chl 3÷î
R /R º) Chl a <=û
al. 1996)
800
550
800
550
143
4Œ
Otü1ý, ؆²P]^
R /R Chl <=P–G
NDVI
Å Chl <=P3(
(Gitelson and Merzlyak 1997)\]^
GI NDVI Chl §
4?¬­8¯°±²56³&
®¢123(90Ÿ ûY࢑4=
¬­8¯°±²56³&Å GI NDVI
5690pô·w)Ë[¹w
)PÌpô¼½঩CZâ,¬­8g
’W <=஽ÕÖ(Harborne 1967)õ`
g’W ƒÁåæ Chl Pç
èGêëࢉ/0 GI NDVI ß)ཽ»
Gpô Chl 9ìíCस
=’W =஽ÕÖ78ß4’
W /0123(ձ, Chl 3÷Ÿ ûZâཽ७X(Hsu et al. 2003)
[¿ÀᕘÄÒ2
$
Chl R R • /0
SRVI NDVI <=/0
GI NDVI <=CIJ12
3(Z4-.56¿À Chl vw9
0îVस᎐AÃÄõ` Chl Ç
७Xß4¿ÀᕘÄ2Ò123(9
0 Chl Zâཽ=»ÇP¥¦:;î=ᎍ
Ӭ123(5690=¬­8᜹
༂⢅Œ⍈᜹hᄀ‫’ܗ‬W <=஽Õ
Ö$B78:jEF$g’W Õ
ÖएഇWb੬ኊࢉ4’W /0123(90 Chl ÅሰՄ
ኍ࢑֐=$B78ϔᘚ4 550 700 nm
ÓÔ/0123(ᎍ-[90 Chl
gÙ¾ÕÖÑ4aßY[ Chl gÙ¾ÕÖÑ4Òß90?ÂýO§
Chl Ù¾ÕÖÑ࢑֐iõs;5iؐ†
]^îVg Chl Ò=¬­8
(R2>0.88) (Buschmann and Nagel 1993)
750
550
Green
Green
Green
green
750
710
144
Crop, Environment & Bioinformatics, Vol. 2, June 2005
’W <=ÕÖ$B78
Chl vw90pô;5i
]^4Wb/0123(Z4
56„ û†9078 !!
T1ˆWbw&‚vwZMß9
078"wý2#ï5G·
9ýé$á%9&='[(Ÿ)*
+,
Fig. 4. The results of validation of vegetation indices by independent data sets in which samples containing
no anthocyanin. R2 was the determination coefficient between the predicted and the measured Chl
contents. The solid line represents the equation Chlorophyllpred = Chlorophyllmeas. The dotted lines
represent error of Chl prediction.
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
145
Fig. 5. The results of validation of vegetation indices by independent data sets in which samples
containing anthocyanin. R2 was the determination coefficient between the predicted and the
measured Chl contents. The solid line represents the equation Chlorophyllpred = Chlorophyllmeas. The
dotted lines represent error of pigment prediction.
146
Crop, Environment & Bioinformatics, Vol. 2, June 2005
ЖңНᝧ
Arnon DI (1969) Copper enzymes in isolated
chloroplasts. Polyphenoloxidase in Beta vulgaris.
Plant Physiol. 24:1-5.
Baret F, I Champion, G Guyot, A Podaire (1987)
Monitoring wheat canopies with high spectral
resolution radiometer. Remote Sens. Environ.
22:367-378.
Baret F, S Jacquemoud, G Guyot (1992) Modeled
analysis of the biophysical nature of spectral shift
and comparison with information content of broad
bands. Remote Sens. Environ. 41:133-142.
Bauer ME (1975) The role of remote sensing in
determining the distribution and yield of crops.
Adv. Agron. 27:271-304.
Buschmann C, E Nagel (1993) In vivo spectroscopy and
internal optics of leaves as basis for remote sensing
of vegetation. Intl. J. Remote Sens. 14:711-722.
p.323-333. In: Instrumentation for studying
vegetation canopies for remote sensing in optical
and thermal infrared regions. NS Goel, JM Norman
(eds.) Harwood Acad. Publ. Gmb H, U.K.
Gamon JF, H Qiu (1999) Ecological applications of
remote sensing at multiple scales. p.805-846. In:
Handbook of functional plant ecology. FI Pugnaire,
F Valladares (eds.) Marcel Dekker, New York.
Gitelson AA, MN Merzlyak (1994a) Spectral
reflectance changes associated with autumn
senescence of Aesculus hippocastanum L. and Acer
platanoides L. leaves. Spectral features and relation to
chlorophyll estimation. J. Plant Physiol.
143:286-292.
Gitelson AA, MN Merzlyak (1994b) Quantitative
estimation of Chlorophyll a using reflectance spectra:
Experiments with autumn chestnut and maple
leaves. J. Photochem. Photobiol. (B) 22:247-252.
Carter GA (1993) Responses of leaf spectral reflectance
to plant stress. Amer. J. Bot. 80:239-243.
Gitelson AA, MN Merzlyak (1996) Signature analysis
of leaf reflectance spectra: algorithm development
for remote sensing of chlorophyll. J. Plant Physiol.
148:494-500.
Carter GA (1994) Ratios of leaf reflectances in narrow
wavebands as indicator of plant stress. Intl. J.
Remote Sens. 15:697-703.
Gitelson AA, MN Merzlyak (1997) Remote estimation
of chlorophyll content in higher plant leaves. Intl. J.
Remote Sens. 18:2691-2697.
Chappelle EW, MS Kim, JE McMurtrey III (1992) Ratio
analysis of reflectance spectra (RARS): An algorithm
for the remote estimation of the concentrations of
chlorophyll a chlorophyll b, and carotenoids in
soybean leaves. Remote Sens. Environ. 39:239-247.
Gitelson AA, YJ Kaufman, MN Merzlyak (1996) Use of
a green channel in remote sensing of global
vegetation from EOS-MODIS. Remote Sen.
Environ. 58:289-298.
Green EP, PJ Mymby, AJ Edwards, CD Clark, AC Ellis
(1997) Estimating leaf area index of mangroves from
satellite data. Aquat. Bot. 58:11-19.
Curran PJ, JL Dungan, BA Macler, SE Plummer (1991)
The Effect of a red leaf pigment on the relationship
between red edge and chlorophyll concentration.
Remote Sens. Environ. 35:69-76.
Harborne JB (1967) Comparative biochemistry of the
flavonoids. Academic Press, London. 383pp.
Danks SM, EH Evans, PA Wittaker (1983)
Photosynthetic Systems. Structure Function and
Assembly. Wiley, New York.
Hendry GAF, JD Houghton, SB Brown (1987) The
degradation of chlorophyll – a biological enigma.
New Phytol. 107:255-302.
Datt B (1998) Remote sensing of chlorophyll a,
Chlorophyll b, chlorophyll a+b, and total carotenoid
content in Eucalyptus leaves. Remote Sens.
Environ. 66:111-121.
Holm G (1954) Chlorophyll mutations in barley. Acta
Agric. Scand. 4:457-461.
Elvidge CD, Z Chen (1995) Comparison of broad-band
and narrow-band red and near-infrared vegetation
indices. Remote Sens. Environ. 54:38-48.
Fuchs M (1990) Canopy thermal infrared observations.
Hsu MH, WD Huang, ZW Yang, YZ Tsai, SS Chang,
CM Yang (2003) Remote estimation of pigment
content in sweet potato leaves by reflectance spectra.
(in Chinese) Chinese Agron. J. 13:1-12.
Lichtenthaler HK, AA Gitelson, M Lang (1996)
Non-destructive determination of chlorophyll
ዂᔤፐ࢒ሎขզᆘҡጶဩбဩᆧષ֥໕
147
content of leaves of a green and aurea mutant
tobacco by reflectance measurements. J. Plant
Physiol. 148: 483-493.
Rudorff BFT, GT Batista (1990) Yield estimation of
sugarcane based on agrometeorological-spectral
models. Remote Sens. Environ. 33:183-192.
Ma BL, MJ Morrison, LM Dwyer (1996) Canopy light
reflectance and field greenness to assess nitrogen
fertilization and yield of maize. Agron. J. 88:915-920.
Schepers JS, TM Blackmer, WW Wilhelm, M Resende
(1996) Transmittance and reflectance measurements
of corn leaves from plants with different nitrogen
and water supply. J. Plant Physiol. 148:523-529.
Mancinelli AL, CP Huang-Yang, P Lindquist, O
Anderson, I Rabino (1975) Photocontrol of
anthocyaninsynthesis,
III.
The
action
of
streptomycin on the synthesis of chlorophyll and
anthocyanin. Plant Physiol. 55:251-257.
Masoni A, L Ercoli, M Mariotti (1997) Spectral
properties of leaves deficient in iron, sulfur,
magnesium, and manganese. Agron. J. 88:937-943.
Matile P, M Schellenberg, C Peisker (1992) Production
and release of a chlorophyll catabolite in isolated
senescent chloroplasts. Planta 187:230-235.
Matile P, T Duggelin, M Schellenberg, D Rentsch, K
Bortlik, C Peisker, H Thomas (1989) How and why
is chlorophyll broken down in senescent leaves?
Plant Physiol. Biochem. 27:595-604.
Porra RJ, WA Thompson, PE Kriedelman (1989)
Determination of accurate extraction and
simultaneously equation for assaying chlorophyll a
and b extracted with different solvents: verification
of the concentration of chlorophyll standards by
atomic absorption spectroscopy. Biochem. Biophys.
Acta 975:384-394.
Price JC, WC Bausch (1995) Leaf area index estimation
from visible and near-infrared reflectance data.
Remote Sens. Environ. 52:55-65.
ɯጢᒯȈᗃӑ኿
Sims DA, JA Gamon (2002) Relationships between leaf
pigment content and spectral reflectance across a
wide range of species, leaf structures and
developmental stages. Remote Sens. Environ.
81:337-354.
Thomas JR, HW Gausman (1977) Leaf reflectance vs.
leaf chlorophyll and carotenoid concentrations for
eight crops. Agron. J. 69:799-802.
Walburg G, ME Bauer, CS Daughtry, TL Housley
(1982) Effects of nitrogen on the growth, yield, and
reflectance characteristics of corn. Agron. J.
74:677-683.
Whittaker RH, PL Marks (1975) Methods of assessing
terrestrial productivity. p.55-118 In: Primary
Productivity of the Biosphere. H Lieth, RH
Whittaker (eds.) Springer-Verlag, New York.
Yang CM, KW Chang, MH Yin, HM Huang (1998)
Methods for the determination of the chlorophylls
and their derivatives. Taiwania 43:116-122.
Yoder BJ, RH Waring (1994) The normalized difference
vegetation index of small Douglas-fir canopies with
varying chlorophyll concentrations. Remote Sens.
Environ. 49: 81-91.