HYPERSPECTRAL REMOTE SENSING FOR ESTIMATING BIOCHEMICAL VARIABLES OF CANOPY
Received date: 2002-05-21
Revised date: 2002-08-30
Online published: 2003-02-01
Hyperspectral remote sensing (narrow bands) provides the possible estimates of biochemical characteristics of plant such as moisture, leaf pigment (for example, chlorophyll, carotenoids and anthocyanins), lignin, cellulose, protein, amino acids, sugar, starch, macro-element (nitrogen, phosphorus and potassium as the example), micro-element (such as Fe, Mn, Co, Zn, Cu). It is special significant to obtain biochemical information of canopy at territorial or even at global levels which could be widely used to study the ecosystem, accelerate the application of precision agriculture technology, and so on. At leaf or small area levels, not only it is significant for the researching on the forming of plant reflectance spectrum for remote sensing, but also, it may provide a rapid, nondestructive method for the detection of these characteristics in lab or in field. But from ground leaf or dry leaf level to fresh leaf level and to canopy level, more and more factors influence the spectrum. There are interferences among the factors, which may add the difficulty to estimate a certain biochemical variable or influence the accuracy. Some new progress in obtaining information from ground-level and airborne or satellitic level hyperspectral reflectance data were summarized and emphasis was especially focused on introducing the methods for improving accuracy of estimating biochemical variables and the mechanism of hyperspectral remote. The problems might be occurred because of the methods self that were being widely used to estimate biochemical variables of canopy also were pointed out. Future work should be focused both on improving the ratio of signal to noise, reforming the method of spectrum analysis and accelerating application of the existing research findings, but also on itemizing the biophysical variables and making clear their spectral characteristics (contribution to the reflectance spectrum). On the other hand, it is necessary to do more research on establishing new canopy spectrum reflectance models or improving existing models by mechanism in the future. The paper may supply the convenience, general picture and discuss to understand the new progress in the technologies and theories of hyperspectral remote sensing for estimating biochemical variables of plant.
Zhao Dehua, Li Jianlong, Song Zijian . HYPERSPECTRAL REMOTE SENSING FOR ESTIMATING BIOCHEMICAL VARIABLES OF CANOPY[J]. Advances in Earth Science, 2003 , 18(1) : 94 -099 . DOI: 10.11867/j.issn.1001-8166.2003.01.0094
[1] Pu Ruiliang, Gong Peng. Hyperspectral Remote Sensing and its Applications [M]. Beijing: Higher Education Press, 2000.185-228. [浦瑞良, 宫鹏. 高光谱遥感及其应用[M]. 北京: 高等教育出版社, 2000.185-228.]
[2] Curran P J. Remote sensing of foliar chemistry [J]. Remote Sensing of Environment, 1989, 30: 271-278.
[3] Fouryt T, Baret F. On spectral estimates of fresh leaf biochemistry [J]. International Journal of Remote Sensing, 1998, 19: 1 283-1 297.
[4] Jackson R D. Spectral response of cotton to suddenly induced water stress [J]. International Journal of Remote Sensing, 1985, 6: 177-185.
[5] Michio S, Tsuyoshi A. Seasonal visible, near-infrared and mid-infrared spectral of rice canopies in relation to LAI and above-ground dry phytomass [J]. Remote Sensing of Environment, 1989, 27: 119-127.
[6] Penuelas J, Filella I, Sweeano L. Cell wall elastivity and water index (R970 nm / R900 nm) in wheat under different nitrogen availabilities [J]. International Journal of Remote Sensing, 1996, 17: 373-382.
[7] Tian QingJiu, Gong Peng, Zhao ChunJiang, et al. A feasibility study on diagnosing wheat water status using spectral reflectance [J]. Chinese Science Bulletin, 2001, 46 (8): 666-669. [田庆久, 宫鹏, 赵春江, 等. 用光谱反射率诊断小麦水分状况的可行性分析[J].科学通报,2000,45（24）：2 645-2 650.]
[8] Wang Jihua, Zhao ChunJiang, Guo Xiaowei, et al. Study on the water content of wheat leaves by the remote sensing [J]. Acta Agriculturae Boreali-Sinica, 2000, 15(4): 68-72. [王纪华, 赵春江, 郭晓维, 等. 利用遥感方法诊断小麦叶片含水量的研究[J]. 华北农学报, 2000, 15(4): 68-72.]
[9] Burgan R E. Use of remotely sensed data for fire danger estimation [J]. EARSel Advances in Remote Sensing, 1996, 4(4): 1-8.
[10] Gond V, De Pury D G G, Veroustracte F, et al. Seasonal varictions in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multiplayer, multispecies temperate forest [J]. Tree Physiology, 1999, 19: 673-679.
[11] Goetz S J, Prince S D, Goward S N, et al. Satellite remote sensing of primary production: An improved production efficiency modeling approach [J]. Ecological Modelling, 1999, 122: 239-255.
[12] Ceccato P, Flasse S, Tarantola, et al. Detecting vegetation leaf water content using reflectance in the optical domain [J]. Remote Sensing of Environment, 2001, 77: 22-33.
[13] Gamon J A, Field C B, Golden M L, et al. Relationship between NDVI, canopy structure and photosynthesis in three Californian vegetation typers [J]. Ecological Applications, 1995, 5: 28-41.
[14] Jago R A, Mark E J C, Curran P J. Estimation canopy chlorophyll concentration from field and airborne spectra [J]. Remote Sensing of Environment, 1999, 68: 217-224.
[15] Jacquemoud C, Bacour C, Poilve H, et al. Comparison of four radiative transfer models to simulate plant canopied reflectance: Direct and inverse mode [J]. Remote Sensing of Environment, 2000, 74:471-481.
[16] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages [J]. Remote Sensing of Environment, 2002, 81: 331-354.
[17] Penuelas J, Filella I. Visible and near-infrared reflectance techniques for diagnosing plant physiological status [J]. Trends in Plant Science, 1998, 3(4): 151-156.
[18] Merzlyak M N, Gitelson A A, Chivkunova O B, et al. Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening [J]. Physiologia Plantarum, 1999, 106:135-141.
[19] Blackburn G A. Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves [J]. International Journal of Remote Sensing, 1998, 19:657-675.
[20] Peñuelas J, Baret F, Filella I. Semi-empirical indices to assess carotenoid / chlorophyll a ratio from leaf spectral [J]. Photosynthetica, 1995, 31: 221-230.
[21] Gamon J A, Peñuelas J, Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency [J]. Remote Sensing of Environment, 1992, 41:35-41.
[22] Carter G A, Knapp A K. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration [J]. American Journal of Botany, 2001, 88:677-684.
[23] Strachan I B, Pattey E, Boisvert J B. Impact of nitrogen and environment conditions on corn as detected by hyperspectral reflectance [J]. Remote Sensing of Environment, 2002, 80:213-224.
[24] Gamon J A, Surfus J S. Assessing leaf pigment content and activity with a reflectometer [J]. New Phytologist, 1999, 143:105-117.
[25] Wu Changshan, Xiang Yueqin, Zheng Lanfen, et al. Estimating chlorophyll density of crop canopies by using hyperspectral data [J]. Journal of Remote Sensing, 2000, 4(3): 228-232. [吴长山, 项月琴, 郑兰芬, 等. 利用高光谱数据对作物群体叶绿素密度估算的研究 [J].遥感学报,2000, 4(3): 228-232.]
[26] Liu Weidong, Xiang Yueqing, Zheng Lanfen, et al. Relationships between rice LAI, CH.D and hyperspectral data [J]. Journal of Remote Sensing, 2000, 4 (4): 279-283. [刘伟东, 项月琴, 郑兰芬, 等. 高光谱数据与水稻叶面积指数及叶绿素密度的相关分析[J]. 遥感学报, 2000, 4 (4): 279-283.]
[27] Lydia Serrano, Lolanda Filella, Josep Penuelas. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies [J]. Crop Science, 2000, 40:723-731.
[28] Tang Wanglong, Yang Xiangheng, Lei Huanqun, et al. The establishment of models to forecast nitrogen, phosphorusand yield winter wheat by means of spectral characteristics [J]. Remote Sensing Technology and Application, 1993, 8 (3): 8-14. [唐万龙, 杨相桓, 雷浣群, 等. 应用光谱特性建立冬小麦氮磷元素丰缺和产量预报模型的研究[J]. 遥感技术与应用, 1993, 8 (3): 8-14.]
[29] Niu Zheng, Chen Yonghua, Sui Hongzhi, et al. Mechanism analysis of leaf biochemical concentration by high spectral remote sensing [J]. Journal of Remote Sensing, 2000,4（2）：125-130. [牛铮, 陈永华, 隋洪智, 等. 叶片化学组分成像光谱遥感探测机理分析 [J]. 遥感学报, 2000,4（2）：125-130.]
[30] Zheng Lanfen, Wang Jinnian. A study on imaging spectrometry and its extraction of image spectral information[J]. Remote Sensing of Environment China, 1992, 7 (1):49-58. [郑兰芬, 王晋年. 成像光谱遥感技术及其图像光谱信息提取的分析研究[J]. 环境遥感, 1992, 7 (1):49-58.]
[31] Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression [J]. Remote Sensing of Environment, 1999, 67 (3): 267-287.
[32] Kokaly R F. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration [J]. Remote Sensing of Environment, 2001, 75: 153-161.
[33] Curran P J, Dungan J L, Peterson D L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry [J]. Remote Sensing of Environment, 2001, 76: 349-359.
[34] Ma Chaofei, Ma Jianwen, Han Xiuzhen. Mechanism analysis of leaf spectrum response resulted from trace elements [J]. Journal of Remote Sensing, 2001,5(5): 334-339. [马超飞, 马建文, 韩秀珍. 微量元素在植物光谱中的响应机理研究[J]. 遥感学报, 2001,5(5): 334-339.]
[35] Adams M L, Norvell W A, Philpot W D, et al. Spectral detection of micronutrient deficiency in ‘Bragg’ soybean [J]. Agronomy Journal, 2000a, 92(2): 261-268.
[36] Adams M L, Norvell W A, Philpot W D, et al. Toward the discrimination of manganese, zinc, copper, and iron deficiency in ‘Bragg’ soybean using spectral detection methods [J]. Agronomy Journal, 2000b, 92 (2): 268-274.
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