臺灣博碩士論文加值系統

本研究利用高光譜訊號技術分析樟樹葉片於不同含水率程度下，葉綠素濃度與光譜的相關性。利用ASD光譜儀測量於不同含水率程度下樟樹葉片之光譜反射資料後，分別測定葉片含水率及葉綠素濃度，並進行連續統去除法光譜轉換、光譜吸收特徵波段面積參數、光譜微分轉換及9種植生指標(NDVI、RDVI、SR、mSR700、mND700、MCARI、TCARI、Green NDVI及R750/R550)以利迴歸分析。並由中篩選出與植生葉綠素有關的光譜特徵，進而完成高光譜與葉綠素濃度模式之建立，並進行高光譜與葉綠素濃度模式之驗證。
研究結果顯示，以綠光區波段特徵(Ref 520 nm、Ref 550 nm、Ref 557 nm、Ref 570 nm、CR 520 nm、CR 550 nm 、CR 550 nm 、CR 557 nm、CR 570 nm、SDb、SDr/SDb、(SDr - SDb)/( SDr + SDb)、MCARI、TCARI、Green NDVI、750/R550、FDS 500 nm及FDS 520 nm)所建構之模式不會受到含水率之影響，其預測葉綠素濃度的準確度R2可達0.67~0.90以上。而藍光、紅光區波段特徵及紅邊參數所建構之模式，其對葉綠素濃度之推估能力明顯受到含水率影響，使其與葉綠素濃度相關性低，但當這些波段特徵與含水率相關波段(Ref 1450 nm)結合建構模式，其與葉綠素濃度相關性提高，R2由0.01~0.71提高至0.28~0.89。顯示再利用紅光區波段特徵應加入含水率因素，才能正確有效地來推估測量葉片葉綠素濃度。另外，在一階微分光譜FDS 645 nm及二階微分紅邊峰點位置為紅光區中的例外，不會受到含水率影響，能建構的良好的推估模式。

This study used an ASD spectroradiometer to measure the spectral reflectance of Cinnamomum camphora leaves of varying leaf water content (LWC). Measurements were taken of chlorophyll concentration and leaf water content. A regression analysis was performed using spectral absorption band parameters following continuum removal, first and second derivative reflectance, and nine vegetation indexes (NDVI, RDVI, SR, mSR700, mND700, MCARI, TCARI, Green NDVI, and R750/R550). Spectral behavior models were developed and used to predict chlorophyll and leaf water content in the experimental data.
The performance of the regression models was first demonstrated using spectral features in the green wavelength region to estimate chlorophyll concentrations in leaves with wide range of water content (these included Ref 520 nm, Ref 550 nm, Ref 557 nm, Ref 570 nm, CR 520 nm, CR 550 nm, CR 550 nm, CR 557 nm, CR 570 nm, SDb, SDr/SDb, (SDr - SDb)/( SDr + SDb), MCARI, TCARI, Green NDVI, 750/R550, FDS 500 nm and FDS 520 nm). The R2 value of the green-based regression models ranged between 0.67 and 0.90.
Using blue- and red-based spectral features, and the red edge parameters, chlorophyll concentration estimation models whose prediction efficiency might affected by the water content of leaves. As expected, correlation between the spectral features in the red wavelength region and chlorophyll concentrations was found to be low, however, when the spectral features were related to leaf water content (Ref 1450 nm), the level of correlation increased. The R2 value increased from 0.01~0.71 to 0.28~0.89. The results showed that spectral features of the red wavelength region ought to be associated with leaf water content in order to estimate chlorophyll concentrations effectively. A notable exception occurred in the red wavelength region at the positions of 645 nm and red edge. Models with the first derivative of 645 nm or second derivative of red edge could achieve a relatively stable results of chlorophyll concentration.

目次
目次 I
圖目次 III
表目次 VII
中文摘要 VIII
英文摘要 IX
第一章 前言 1
第二章 前人研究 3
一、太陽光譜 3
二、高光譜遙測 5
三、植物反射光譜特徵 8
四、植生含水率與光譜遙測 10
(一) 水對植物的生理生態作用 10
(二) 相關研究 11
五、葉綠素與光譜遙測 13
(一) 葉綠素 13
(二) 相關研究 15
六、光譜資料處理方法 17
(一) 植生指標 17
(二) 連續統去除法 25
(三) 光譜微分技術 27
(四) 紅邊光譜參數分析 28
第三章 材料與方法 30
一、試驗材料與儀器 30
(一) 試驗材料 30
(二) 高光譜儀器 31
二、研究方法 32
(一) 光譜試驗 34
(二) 葉片含水率測定 35
(三) 葉綠素濃度測定 35
(四) 光譜資料分析 36
(五) 統計分析 43
(六) 模式驗證 45
第四章 結果與討論 46
一、樟樹植生反射曲線 46
二、葉片含水率及葉綠素濃度與植生光譜之相關性分析 50
三、葉綠素濃度與高光譜反射特性分析 52
(一) 原始植生光譜模式建立 52
(二) 連續統去除法調整光譜模式建立 56
(三) 光譜吸收特徵面積參數光譜模式建立 60
(四) 微分光譜參數模式建立 65
(五) 植生指標分析與模式建立 74
四、不同含水率程度與葉綠素濃度模式關係之分析 79
(一) 葉片含水率與光譜特徵之模式建立 79
(二) 結合含水率與葉綠素濃度模式關係之迴歸分析 90
五、模式驗證 95
(一) 不受含水率影響之特徵波段參數模式之驗證 95
(二) 結合含水率之特徵波段參數模式之驗證 103
第五章、結論 112
參考文獻 116
圖目次
圖 1、大氣中氣體、氣溶膠和水分子對電磁波在大氣中傳輸之影
響 4
圖 2、太陽光譜及其與葉綠素吸收光譜之關聯 4
圖 3、電磁波譜 4
圖 4、多光譜與高光譜示意圖 7
圖 5、高光譜影像原理示意圖 7
圖 6、綠色植物的典型光譜反射曲線及影響因素 9
圖 7、倒高斯反射模型和相關紅邊參數 12
圖 8、葉綠素之化學結構式 14
圖 9、葉綠素於乙醚溶劑中的吸收光譜 14
圖10、經連續統去除法調整的植物光譜 26
圖11、光譜吸收特徵圖及其連續統去除法光譜吸收特徵圖 27
圖12、試驗植生材料-樟樹 30
圖13、ASD攜帶式野外光譜輻射儀 31
圖14、不同斷水時間處理之試驗樣本 32
圖15、研究流程圖 33
圖16、光譜試驗方法 34
圖17、葉綠素濃度、葉片含水率及原始光譜值轉換後之影像格式 37
圖18、光譜吸收特徵波段面積參數示意圖 39
圖19、樟樹植生光譜反射特性曲線 47
圖20、連續統去除法轉換之樟樹植生光譜特性曲線 48
圖21、植生光譜一階微分轉換特性曲線 48
圖22、樟樹植生葉綠素濃度之400~520 nm吸收特徵 48
圖23、樟樹植生葉綠素濃度之550~750 nm吸收特徵 49
圖24、樟樹植生含水率之1300~1650 nm吸收特徵 49
圖25、樟樹植生含水率之1840~2240 nm吸收特徵 49
圖26、樟樹葉片全波段光譜反射率及其與葉綠素濃度的相關係 數 51
圖27、樟樹葉片全波段光譜反射率及其與葉片含水率(LWC%)的
相關係數 51
圖28、原始植生光譜可見光特性曲線 53
圖29、總葉綠素濃度與Ref 500之相關性 53
圖30、總葉綠素濃度與Ref 520迴歸式 54
圖31、總葉綠素濃度與Ref 550迴歸式 54
圖32、總葉綠素濃度與Ref 557迴歸式 54
圖33、總葉綠素濃度與Ref 645之相關性 54
圖34、總葉綠素濃度與Ref 690之相關性 55
圖35、總葉綠素濃度與Ref 700之相關性 55
圖36、植生光譜可見光連續统去除法轉換特性曲線 57
圖37、總葉綠素濃度與CR 500之相關性 57
圖38、總葉綠素濃度與CR520迴歸式 58
圖39、總葉綠素濃度與CR550迴歸 58
圖40、總葉綠素濃度與CR557迴歸式 58
圖41、總葉綠素濃度與CR 645之相關性 58
圖42、總葉綠素濃度與CR 690之相關性 59
圖43、總葉綠素濃度與CR 700之相關性 59
圖44、藍、紅光吸收特徵面積示意圖 60
圖45、藍、紅光吸收特徵波段面積之深度及寬度 61
圖46、總葉綠素濃度與Blue AFA之相關性 62
圖47、總葉綠素濃度與Red AFA之相關性 63
圖48、總葉綠素濃度與DEPblue之相關性 63
圖49、總葉綠素濃度與DEPred之相關性 63
圖50、總葉綠素濃度與WIDblue之相關性 64
圖51、總葉綠素濃度與WIDred之相關性 64
圖52、植生光譜反射率可見光一階微分特性曲線圖 66
圖53、總葉綠素濃度與FDS 500迴歸式 67
圖54、總葉綠素濃度與FDS 520迴歸式 67
圖55、總葉綠素濃度與FDS 645迴歸式 67
圖56、總葉綠素濃度與FDS 690之相關性 67
圖57、總葉綠素濃度與FDS 700之相關性 68
圖58、總葉綠素濃度與紅邊位置之相關性 69
圖59、總葉綠素濃度與SDb迴歸式 70
圖60、總葉綠素濃度與SDr迴歸式之相關性 70
圖61、總葉綠素濃度與SDr/SDb迴歸式 70
圖62、總葉綠素濃度與(SDr-SDb)/(SDr+SDb)迴歸式 70
圖63、植生光譜反射率可見光二階微分特性曲線圖 71
圖64、總葉綠素濃度與二階微分光譜紅邊峰點位移變化之迴歸 式 72
圖65、總葉綠素濃度與二階微分光譜紅邊谷點位移變化之相關 性 72
圖66、總葉綠素濃度與NDVI之相關性 75
圖67、總葉綠素濃度與RDVI之相關性 75
圖68、總葉綠素濃度與SR之相關性 76
圖69、總葉綠素濃度與mSR700之相關性 76
圖70、總葉綠素濃度與mND700之相關性 76
圖71、總葉綠素濃度與MCARI迴歸式 77
圖72、總葉綠素濃度與TCARI迴歸式 77
圖73、總葉綠素濃度與Green NDVI迴歸式 77
圖74、總葉綠素濃度與R750/R550迴歸式 77
圖75、葉片含水率與原始光譜特徵波段迴歸式 80
圖76、葉片含水率與連續統去除法光譜特徵波段迴歸式 81
圖77、葉片含水率與吸收特徵波段面積迴歸式 82
圖78、葉片含水率與水分指標迴歸式 83
圖79、原始光譜的葉片含水率模式驗證 86
圖80、連續統去除法光譜的葉片含水率模式驗證 87
圖81、吸收特徵波段面積的葉片含水率模式驗證 87
圖82、水分指標的葉片含水率模式驗證 88
圖83、不受含水率影響原始光譜的葉綠素模式驗證 97
圖84、不受含水率影響連續統去除法光譜的葉綠素模式驗證 98
圖85、藍邊及紅邊一階微分總和參數的葉綠素模式驗證 99
圖86、不受含水率影響植生指標的葉綠素模式驗證 100
圖87、不受含水率影響微分光譜參數的葉綠素模式驗證 101
圖88、受含水率影響原始光譜的葉綠素模式驗證 105
圖89、受含水率影響連續統去除法光譜的葉綠素模式驗證 106
圖90、吸收特徵波段面積參數的葉綠素模式驗證 107
圖91、受含水率影響微分光譜參數的葉綠素模式驗證 108
圖92、受含水率影響植生指標的葉綠素模式驗證 109


表目次
表 1、1973~2002年葉綠素相關植生指數及研究文獻 21
表 2、相關係數的強度大小與意義 37
表 3、本研究選用之植生指數及水分指數 41
表 4、樟樹葉片試驗樣本葉綠素含量及含水率 46
表 5、葉綠素濃度與原始光譜模式迴歸分析之比較 56
表 6、葉綠素濃度與連續統去除法調整光譜模式迴歸分析之比 較 60
表 7、葉綠素濃度與光譜吸收特徵波段參數迴歸分析之比較 65
表 8、葉綠素濃度與微分光譜參數迴歸分析之比較 73
表 9、葉綠素濃度與植生指標反射特性迴歸分析之比較 78
表10、特徵波段參數及指標與葉片含水率LWC(%)迴歸分析之
比較 84
表11、特徵波段參數及指標與葉片含水率LWC(%)模式之驗證
89
表12、特徵波段相關參數結合Ref 1450 nm與葉綠素濃度迴歸
分析之比較 92
表13、不受含水率影響之特徵波段參數模式與驗證模式R2 102
表14、結合含水率之特徵波段參數模式與驗證模式R2 110

王月雲、陳是瑩、童武夫 (2003) 植物生理學實驗增訂本。國立台灣師範大學出版組。台北市。286頁。
王秀珍、黃敬峰、李雲梅 (2003) 水稻生物化學參數與高光譜遙感特徵參數的相關分析。農業工程學報 19(2)：144-148。
李匡邦、許東明、何東英 (1997) 光譜化學分析。揚智文化。台北市。345頁。
吳志鴻、張上鎮 (1998) 葉綠素的結構與類別。台大實驗林研究報告12：59-68。
易希道 (1999) 植物生理學。正中書局。台北市。643頁。
林金樹 (2002) 應用主軸轉換法辨認空載高光譜影像土地利用型特性之研究。台灣林業科學 17(3)：347-359。
林金樹、溫慧霖、黃淑清 (2006) 植物葉綠素濃度及含水率高光譜的估測與行為模式之建立。彰雲嘉大學校院聯盟2006年學術研討會論文集 737-751頁。
邱皓政 (2000) 量化研究與統計分析。五南圖書出版有限公司。台北市。604頁。
徐百輝 (2007) 大地的辨識密碼—高光譜影像。科學發展416: 13-19。
浦瑞良、宮鵬 (2002) 高光譜遙測及其應用。五南圖書出版股份有限公司，台北市。332頁。
許明晃、楊志雄、張新軒、楊棋明、黃文達 (2006) 空氣污染物對甘蔗葉片色素與反射光譜特徵之影響。作物、環境與生物資訊 3：345-354。
張致盛、張林仁 (1998) 兩種速測法在果樹葉片葉綠素含量測定之應用。台中區農業改良場研究彙報 59:37-45。
陳宏銘、許明晃、楊志維、黃文達、楊棋明、張新軒 (2007) 氮肥等級與刈割頻度度對Tifway 419百慕達草反射光譜之影響。中華民國雜草學會會刊 28(1): 71-97。
陳榮坤、楊純明 (2002) 以近地面高解析植被光譜及模擬SPOT衛星寬頻光譜估測水稻生長性狀的變化。中華農業研究 51(4): 1-18。
黃文達、許明晃、楊志雄、陳建璋、蔡養正、張新軒、楊棋明 (2005) 模擬衛星遙測估算甘蔗葉片葉綠素含量。作物、環境與生物資訊 2：137-147。
馮偉、朱豔、田永超、馬吉峰、莊森、曹衛星 (2008) 基於高光譜遙感的小麥冠層葉片色素密度監測。生態學報 28 (10)：4902-4911。
黃淑清 (2008) 樟樹葉綠素含量與光譜訊號之研究。國立嘉義大學農學院林業暨自然資源研究所碩士論文。77頁
曾世昌、郭幸榮、李遠欽 (1991) 鹽沫對木麻黃之若干生理為害。中華林學季刊 24(3):27-34。
楊純明、吳正宗、沈百奎、余志儒、羅朝村、申雍 (2003) 利用植被光譜特徵估測莧菜植株生長及氮素狀態。中華農業研究 52: 268-290。
劉業經、呂福原、歐辰雄 (1994) 台灣樹木誌。國立中興大學農學院出版委員會。925頁。
劉懿聰、林金樹 (2003) 高光譜特徵與冠層密度關係之研究。中華林學會 92年度學術論文發表會論文集，463-472頁。
鄭美淑、王慶裕、朱德民 (1990) 植物生理。復文書局，台南市，123頁。
潘國樑 (2006) 遙測學大綱。科技圖書股份有限公司。台北市。292頁。
Baldini, E., O. Facini, F. Nerozzi, F. Rossi, and A. Rotondi (1997) Leaf characteristics and optical properties of different woody species. Trees 12: 73-81.
Birth, G. S. and G. McVey (1968) Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60: 640-643.
Blackburn, G. A. (1998) Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. International Journal of Remote Sensing 19: 657-675.
Blackburn, G. A. (2007) Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany 58(4): 855-867.
Broge, N. H. and E. Leblanc (2001) Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76(2): 156– 172.
Buschmann, C. and E. Nagel (1993) In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International journal of remote sensing 14(4): 711-722.
Carter, G. A. (1993) Responses of leaf spectral reflectance to plant stress. American Journal of Botany 80: 231-243.
Carter, G. A. (1994) Ratios of leaf reflectances in narrow wavebands as indicator of plant stress. International Journal of Remote Sensing 15: 697-703.
Chappelle, E. W., Kim, M. S. and McMurtrey III, J. E. (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. emote Sensing of Environment 39: 239-247.
Chen, J. C. C., M. Yang, S. T. Wu, Y. L. Chung, A. L. Charles and C. T. Chen (2007) Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan’s Kenting National Park. Botanical Studies 48: 71-77.
Choudhury, B. J. (1994) Synergism of multispectral satellite observations forestimating regional land surface evaporation. Remote Sensing of Environment 49:264-274.
Cihlar, J., St. Laurent, L. and Dyer, J. A. (1991). Relation between the normalized difference vegetation index and ecological variables. Remote Sensing of Environment 35:279-298.
Clark, R. N. and T. L. Roush (1984) Reflectance spectroscopy: quantitative analysis techniques for remote sensing application Journal of Geophysical Research 89: 6329-6340.
Cloutis E. A. (1996) Hyperspectral geological remote sensing: evaluation of analytical techniques. International Journal of Remote Sensing 17: 2215-2242.
Curran, P. J., and J. A. Kupiec (1995) Imaging spectrometry: a new tool for ecology. In: Advances in Environmental Remote Sensing edited by F. M. Danson and S. E. Plummer. Wiley. Chichester. pp. 71-88.
Curran, P. J., J. L. Dungan and D. L. Peterson (2001) Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sensing of Environment 76: 349-359.
Daughtry, C. S. T., C. L. Walthall, M. S. Kim, E. B. de Colstoun and J. E. McMurtrey III (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74: 229-239.
Davies, K. M. (2004) Plant pigments and their manipulation. Oxford. BlackwellBoca Raton. 351 pp.
Devlin, R. M. and A. V. Barker (1971) Photosynthesis. Van Nostrand Reinhold. New York. 304 pp.
Fang, Z., J. Bouwkamp, T. Solomos (1998) Chlorophyllase activities and chlorophyll degradation during leaf senescence in nonyellowing mutant and wild type of Phaseolus vulgaris L. Journal of Experimental Botany 49: 503-510.
Filella, I., L. Serrano, J. Serra, and J. Pen˜uelas (1995) Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science 35: 1400-1405.
Fourty, T. and F. Baret (1998) On spectral estimates of fresh leaf biochemistry. International Journal of Remote Sensing 19: 1283-1297.
Gilbert, M. A., Seggara, D. and Melia, J. (1990) A simplified algorithm for the evaluation of frost-affected citrus, application of chlorophyll fluorescence. Lichtenthaler, H. K. ed. pp.273-284.
Gitelson, A. A. and M. N. Merzlyak (1994 b) Quantitative estimation of chlorophyll-a using reflectance spectrum: Experiments with autumn chestnut and maple leaves. Journal of photochemistry and photobiology. B: Biology 22: 247-252.
Gitelson, A. A. and M. N. 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. Journal of Plant Physiology 143: 286-292.
Gitelson, A. A. and M. N. Merzlyak (1996) Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148: 494-500.
Gitelson, A. A. and M. N. Merzlyak (1997) Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18: 2691-2697.
Gitelson, A. A., M. N. Merzlyak and H. K. Lichtenthaler (1996) Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology 148: 501-508.
Gitelson, A. A., Y. Gritz and M. N. Merzlyak (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160: 271-282.
Gitelson, A. A., Y. J. Kaufman and M. N. Merzlyak (1996) Use of a green channel in remote sensing. global vegetation. EOS-MODIS. Remote Sensing of Environment 58: 289-298.
Gupta, R. K., D. Vijayan and T. S. Prasad (2001) New hyperspectral vegetation characterization parameters. Advances in Space Research 28: 201-206 .
Haboudane, D., J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, L. Dextraze (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81: 416-426.
Hoffer, R. M. (1978) Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data. In: Remote Sensing: The Quantitative Approach edited by P. H. Swam and S. M. Davis. McGraw-Hill. New York . pp.289.
Hopkins, W. G.. and N. P. A. Hüner (2004) Introduction to Plant Physiology, third edition. John Wiley. Hoboken. 560 pp.
Huanga, Zhi, Brian J. Turnera, Stephen J. Durya,1, Ian R. Wallisb and William J. Foleyb (2004) Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment 93: 18-29.
Huete, A. R. and C. Justice (1999) MODIS vegetation index (MOD 13) algorithm theoretical basis document version 3. Algorithm theoretical basis document, greenbelt: NASA goddard space flight center, http://modarch.gsfc.nasa.gov/MODISL/LAND/#vegetation-indices. 129 pp.
Hurcom, S. J. and A. R. Harrison (1998) The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. International Journal of Remote Sensing 19: 3109-3125.
Jago, R. A., M. E. J. Cutler and P. J. Curran (1999) Estimation canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment 68: 217-224.
Jensen, J. R. (2005) Introductory Digital Image Processing : a remote sensing perspective. third edition. Upper Saddle River, NJ. Prentice Hall. 526 pp.
Johnkutty, L., and S. P. Palaniappan (1995) Use of chlorophyll meter for nitrogen management in lowland rice. Nutrient Cycling in Agroecosystems 45: 21-24.
Kokaly, R. F. (2001) Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing of Environment 75: 153-161.
Kokaly, R. F. and R. N. Clark (1999) Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment 67: 267-287.
Li, X. Y., G. S. Liu, Y. F Yang, C. H. Zhao, Q. W. Yu and S. X. Song (2007) Relationship between hyperspectral parameters and physiological and biochemical indexes of flue-cured Tobacco leaves. Agricultural Sciences in China 6(6): 665-672.
Lichtenhaler, H. K. (1987) Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. Methods Enzymo1 148: 350-382.
Lichtenthaler, H. K., A. A. Gitelson, and M. Lang (1996). Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. Journal of Plant Physiology 148: 483-493.
Lillesand, T. M., R. W. Kiefer and J. W. Chipman (2000) Remote sensing and image interpretation, fourth edition. John Wiley. New York. 724 pp.
Liu, L. Y., J. H. Wang, W. J. Huang, C. J. Zhao, B. Zhang, and Q. X. Tong (2004) Estimating winter wheat plant water content using red edge parameters. International Journal of Remote Sensing 25(7): 3331-3342.
Maire, G. L., C. François, E. Dufrȇne (2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89: 1-28.
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., Solovchenko, A. E., and Pogosyan, S. I. (2003) Application of reflectance spectroscopy for analysis of higher plant pigments. Russian Journal of Plant Physiology 50: 785-792.
Miller, J. R., J. Wu, M. G. Boyer, M. Belanger, E. W. Hare (1991) Seasonal patterns in leaf reflectance red-edge characteristics. International Journal of Remote Sensing 12(7): 1509-1523.
Moran, J.A., A.K. Mitchell, G. Goodmanson, and K.A. Stockburger (2000) Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar reflectance: a comparison of methods. Tree Physiology 20: 1113-1120.
Mutanga, O., A. K. Skidmore and H. H. T. Prins (2004) Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89: 393-408.
Mutanga, O., A. K. Skidmore. and S. V. Wieren (2003) Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry & Remote Sensing 57: 263– 272.
Mutanga, O., A. K. Skidmore., L. Kumar. and J. Ferwerda (2005) Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain. International Journal of Remote Sensing 26: 1093-1108.
Peñuelas J. and I. Filella. (1998) Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3(4): 151-156.
Peñuelas J., I. Filella, C. Biel, L. Sweeano and R. Save (1993) The reflectance at the 950-970 nm region as an indicator of plant water status. International Journal of Remote Sensing 14: 1887-1905.
Peñuelas J., I. Filella, L. Sweeano (1996) Cell wall elastivity and water index (R970/R900 nm) in wheat under different nitrogen availabilities. International Journal of Remote Sensing 17: 373-382.
Peñuelas, J., R. Gamon, J. A., Griffin, K. L. and Field, C. B. (1993) Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment 46:110-118.
Peterson, D. L., J. D. Aber, P. A. Matson, D. H. Card, N. Swanberg, C. Wessman and M. Spanner (1988) Remote sensing of forest canopy leaf biochemical contents. Remote Sensing of Environment 24: 85-108.
Richardson, A. D., and G. P. Berlyn.（2002）Spectral reflectance and photosynthetic properties of betula papyrifera (Betulaceae) leaves along an elevational gradient on Mt. Mansfield, Vermont, USA. American Journal of Botany 89: 88-94.
Richardson, A.D., S.P. Duigan, G.P. Berlyn (2002) An evaluation of non-invasive methods to estimate foliar chlorophyll content. New Phytologist 153: 185-194.
Rinehart, G. L., J.H. Cathoun and O. Schabbenberger (2002) Remote sensing of stripe patch and dollar spot on creeping bentgrass and annual bluegrass turf using visible and near-infrared spectroscopy. Australian Turfgrass Management 4(2): 2-80.
Roujean, J. -L. and F. -M. Breon (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment 51(3): 375-384.
Rouse, J. W., R. H. Haad, J. A. Schell and D. W. Deering (1974) Monitoring Vegetation Systems in the Great Plants with ERTS. Proceedings, 3ed Earth Resource Technology Satellite(ERTS) Symposium 1: 48-62.
Running, S. W., C. O. Justice, V. Solomonson, D. Hall, J. Barker, Y. J. Kaufman, A. H. Strahler, A. R. Huete, J. P. Muller, V. Vanderbilt, Z. M. Wan, P. Teillet and D. Carneggie (1994) Terrestrial Remote Sensing Science and Algorithms Planned for EOS/MODIS. International Journal of Remote Sensing 15: 3587-3620.
Schmidt, K. S. and A. K. Skidmore (2003) Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment 85(1): 92-108.
Serrano, L., S. L. Ustin, D. A. Roberts, J. A. Gamon and J. Peñuelas (2000) Deriving water content of chaparral vegetation from AVIRIS data. Remote Sensing of Environment 74: 570-581.
Shaw, G. A. and H.-H. K. Burke (2003) Spectral Imaging for Remote Sensing. Lincoln Laboratory Journal 14(1): 3-28.
Sims D. A. and J. A. Gamon (2002) Relationship between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 337-354.
Sims D. A., and J. A. Gamon (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sensing of Environment 84: 526-537.
Slaton, M. R., E. R. Hunt, JR., and W. K. Smith (2001) Estimating near-infrared leaf reflectance from leaf structural characteristics. American Journal of Botany 88(2): 278-284.
Smith, K. L., M. D. Steven and J. J. Colls (2004) Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Enviroment 92: 207-217.
Taiz, L. and E. Zeiger (2006) Plant physiology, fourth edition. Sinauer Associates. Sunderland. 764 pp.
Takahiro E., Y. Yoshifumi, M. Tamura (2001) Spatial estimation of biochemical parameters of leaves with hyperspectral imager. 22nd Asian Conference on Remote Sensing, 5-9 November, Singapore.
Tian Q., Q. Tong, R. Pu, X. Guo, C. Zhao (2001) Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features. International Journal of Remote Sensing 22: 2329-2338.
Vane, G. and A. F. H. Goetz (1993) Terrestrial imaging spectrometry: current status, future trends. Remote Sensing of Environment 44(2-3): 117-126.
Vane, G. and A. Goetz (1988) Terrestrial imaging spectroscopy. Remote Sensing of Environment 24: 1-29.
Wessman, C. A., J. D. Aber, D. L. Peterson and J. M. Melillo (1988) Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystem. Nature 335: 154-156.
Whittaker, R. H. and P. L. Marks (1975) Methods of assessing terrestrial productivity.In: Primary Productivity of the Biosphere (ed. by Lieth, H. and Whittaker, R. H.). Springer-Verlag, New York. pp.55-118.
Yang, C. M., K. W. Chang, M. H. Yin and H. M. Huang (1998) Methods for the determination of the chlorophylls and their derivatives. Taiwania 43: 116-122.
Yoder, B. J. and R. E. Pettigrew-Crosby (1995) Predicting nitrogen and chlorophyll content and concentration from reflectance spectra (400-2500 nm) at leaf and canopy scales. Remote Sensing of Environment 53: 199-211.
Zarco-Tejada, P. J., J. R. Miller, J. Harron, B. Hu, T. L. Noland, N. Goel, G. H. Mohammed, and P. Sampsond (2004) Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sensing of Environment 89: 189-199.