植被叶绿素含量的高光谱遥感反演方法研究进展
收稿日期: 2024-09-02
修回日期: 2024-10-10
网络出版日期: 2025-04-18
Progress on Hyperspectral Remote Sensing Inversion Method for Vegetation Chlorophyll Content
Received date: 2024-09-02
Revised date: 2024-10-10
Online published: 2025-04-18
叶绿素含量对于植物的光合作用能力至关重要,是评估植被生长状况的重要指标。叶绿素含量的测定对植物的健康状况、施肥管理以及产量评估具有显著意义。然而,传统的测量方法费时费力。近年来,高光谱遥感技术作为一种前沿技术,得到了迅速发展,利用高光谱数据进行叶绿素含量估算已成为一种重要手段。全面回顾国内外典型机载星载高光谱成像仪发展历程及其数据应用,通过查阅国内外相关文献,分析光谱植被指数构建、高光谱红边位置参数和机器学习算法3种方法在高光谱数据反演叶绿素中的优势与局限性,并指出当前高光谱遥感发展及植被叶绿素定量反演研究中存在的不足,提出未来的研究方向。
马荣菲 , 陈妍 , 侯鹏 , 任晓琦 . 植被叶绿素含量的高光谱遥感反演方法研究进展[J]. 林草资源研究, 2024 , 0(5) : 166 -178 . DOI: 10.13466/j.cnki.lczyyj.2024.05.018
Chlorophyll content is crucial for the photosynthetic capacity of plants and serves as an important indicator of vegetation growth status.Accurate measurement of chlorophyll content is essential for assessing plant health,optimizing fertilizer management,and evaluating crop yields.However,traditional measurement methods are time-consuming and labor-intensive.In recent years,hyperspectral remote sensing technology has been rapidly developing as a cutting-edge technology,and using hyperspectral data for estimating chlorophyll content has become an important approach.This paper provides a comprehensively review of the development of typical airborne star-borne hyperspectral imagers both domestically and internationally.By analyzing relevant literature,the paper analyzes the advantages and limitations of three methods,namely,spectral vegetation index construction,hyperspectral red-edge positional parameters and machine learning algorithms,in inverting chlorophyll from hyperspectral data,and points out the shortcomings of the current development of hyperspectral remote sensing and the research on quantitative inversion of vegetation chlorophyll,and proposes the future research direction.
| [1] | 冯伟, 朱艳, 田永超, 等. 基于高光谱遥感的小麦冠层叶片色素密度监测[J]. 生态学报, 2008(10):4902-4911. |
| [2] | 章曼, 常庆瑞, 张晓华, 等. 不同施肥条件下水稻冠层光谱特征与叶绿素含量的相关性[J]. 西北农业学报, 2015, 24(11):49-56. |
| [3] | 李云梅, 倪绍祥, 王秀珍. 线性回归模型估算水稻叶片叶绿素含量的适宜性分析[J]. 遥感学报, 2003(5):364-371. |
| [4] | OSCO L P, RAMOS A P M, MORIYAé A S, et al. Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks[J]. Remote Sensing, 2019, 11(23):2797. |
| [5] | 刘银年. 高光谱成像遥感载荷技术的现状与发展[J]. 遥感学报, 2021, 25(1):439-459. |
| [6] | 赵恒谦, 李美钰, 吴艳花, 等. 基于PROSAIL模型和Sentinel-2数据的陕北煤炭矿区植被叶绿素含量监测[J]. 地理与地理信息科学, 2024, 40(2):29-36. |
| [7] | LU Bing, HE Yuhong, DAO Phuong D. Comparing the performance of multispectral and hyperspectral images for estimating vegetation properties[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(6):1784-1797. |
| [8] | BAATH G S, FLYNN K C, GOWDA P H, et al. Detecting biophysical characteristics and nitrogen status of finger millet at hyperspectral and multispectral resolutions[J]. Frontiers in Agronomy, 2021,2:604598. |
| [9] | VANE G, GOETZ A F H, WELLMAN J B. Airborne imaging spectrometer:A new tool for remote sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 1984,(6):546-549. |
| [10] | VANE G. First results from the airborne visible/infrared imaging spectrometer(AVIRIS)[C]// Imaging spectroscopy II.SPIE, 1987,834:166-175. |
| [11] | VANE G, GREEN R O, CHRIEN T G, et al. The airborne visible/infrared imaging spectrometer(AVIRIS)[J]. Remote Sensing of Environment, 1993, 44(2):127-143. |
| [12] | 束炯, 王强, 孙娟. 高光谱遥感的应用研究[J]. 华东师范大学学报(自然科学版), 2006(4):1-10. |
| [13] | BABEY S K, SOFFER R J. Radiometric calibration of the compact airborne spectrographic imager(CASI)[J]. Canadian Journal of Remote Sensing, 2014, 18(4):233-242. |
| [14] | KRUSE F A, BOARDMAN J W, LEFKOFF A B, et al. 1999 AIG/HyVista HyMap group shoot:Commercial hyperspectral sensing is here[C]// Algorithms for Multispectral,Hyperspectral,and Ultraspectral Imagery VI.SPIE, 2000,4049:210-217. |
| [15] | TONG Qingxi, XUE Yongqi, ZHANG Lifu. Progress in hyperspectral remote sensing science and technology in China over the past three decades[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 7(1):70-90. |
| [16] | YARBROUGH S, CAUDILL T R, KOUBA E T, et al. MightySat II.hyperspectral imager:Summary of on-orbit performance[C]// Imaging Spectrometry VII.SPIE, 2002,4480:186-197. |
| [17] | PEARLMAN J S, BARRY P S, SEGAL C C, et al. Hyperion,a space-based imaging spectrometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6):1160-1173. |
| [18] | FLETCHER P F. Image acquisition planning for the CHRIS sensor onboard PROBA[C]// Imaging spectrometry X.SPIE, 2004,5546:141-148. |
| [19] | RAST M, BEZY J L, BRUZZI S. The ESA medium resolution imaging spectrometer MERIS a review of the instrument and its mission[J]. International Journal of Remote Sensing, 1999, 20(9):1681-1702. |
| [20] | MURCHIE S, ARVIDSON R, BEDINI P, et al. Compact reconnaissance imaging spectrometer for Mars(CRISM)on Mars reconnaissance orbiter(MRO)[J]. Journal of Geophysical Research:Planets, 2007, 112(E5):1-57. |
| [21] | LUCKE R L, CORSON M, MCGLOTHLIN N R, et al. Hyperspectral Imager for the Coastal Ocean:Instrument description and first images[J]. Applied Optics, 2011, 50(11):1501-1516. |
| [22] | CHO H J, OGASHAWARA I, MISHRA D, et al. Evaluating Hyperspectral Imager for the Coastal Ocean(HICO)data for seagrass mapping in Indian River Lagoon,FL[J]. GIScience& Remote Sensing, 2014, 51(2):120-138. |
| [23] | KREBS, GUNTER D. Resurs-P 1,2,3(47KS)[EB/OL].(2024-04-10)[2024-08-22]. https://space.skyrocket.de/doc_sdat/resurs-p.htm |
| [24] | THE CEOS DATABASE. CEOS EO:Handbook-Instrument Index[EB/OL].(2023-10-01)[2024-06-07]. http://database.eohandbook.com/database/instrumentindex.aspx#H |
| [25] | STORCH T, HONOLD H P, CHABRILLAT S, et al. The EnMAP imaging spectroscopy mission towards operations[J]. Remote Sensing of Environment, 2023,294:113632. |
| [26] | CHIEN S, SILVERMAN D, DAVIES A G, et al. Onboard science processing concepts for the HyspIRI mission[J]. IEEE Intelligent Systems, 2009, 24(6):12-19. |
| [27] | NASA Airborne Science Program. Hyperspectral infrared imager(HyspIRI) airborne campaign[EB/OL].(2020-01-21)[2024-06-07]. https://airbornescience.nasa.gov/content/Hyperspectral_Infrared_Imager_HyspIRI_Airborne_Campaign |
| [28] | KREBS, GUNTER D. Resurs-P 4,5(47KS)[EB/OL].(2024-04-10)[2024-08-22]. https://space.skyrocket.de/doc_sdat/resurs-p4.htm |
| [29] | CHEN Jinsong, SHAO Yun, GUO Hundong, et al. Destriping CMODIS data by power filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9):2119-2124. |
| [30] | 赵葆常, 杨建峰, 常凌颖, 等. “嫦娥一号”卫星成像光谱仪光学系统设计与在轨评估[J]. 光子学报, 2009, 38(3):479-483. |
| [31] | National Satellite Meteorological Center. Feng Yun series satellites[EB/OL].(2013-01-18)[2024-06-20]. https://www.nsmc.org.cn/nsmc/en/satelli-te/index.html |
| [32] | WANG Qiao, WU Chuanqing, LI Qing, et al. Chinese HJ-1A/B satellites and data characteristics[J]. Science China Earth Sciences, 2010,53:51-57. |
| [33] | LIAO Chunhua, ZHANG Xianfeng, BAO Huiyi. Estimation of fractional vegetation cover by unmixing HJ-1 satellite hyperspectral data[C]// 2012 Second International Workshop on Earth Observation and Remote Sensing Applications.IEEE, 2012,278-281. |
| [34] | 覃帮勇, 刘志文, 李盛阳. 天宫一号高光谱数据的陆地应用[J]. 载人航天, 2018, 24(4):546-552. |
| [35] | ZHONG Yanfei, WANG Xinyu, WANG Shaoyue, et al. Advances in spaceborne hyperspectral remote sensing in China[J]. Geo-spatial Information Science, 2021, 24(1):95-120. |
| [36] | 杜润凤, 王霄鹏, 张佳华, 等. 高分五号卫星高光谱遥感分类方法研究综述[J]. 青岛大学学报(工程技术版), 2023, 38(4):83-93. |
| [37] | 张立福, 王飒, 颜军, 等. 珠海一号高光谱数据辐射质量评价[J]. 遥感学报, 2023, 27(8):1925-1935. |
| [38] | 李先怡, 颜军, 颜志宇, 等. 珠海一号卫星及其综合应用[J]. 卫星应用, 2022(7):25-30. |
| [39] | 生态环境部卫星环境应用中心. 高光谱综合观测卫星(GF5-01A)在轨投入使用[EB/OL].(2024-01-25)[2024-08-23]. http://www.se-cmep.cn/gzdt/kjgz/202401/t20240125_1064661.shtml |
| [40] | 阿热孜古力·肉孜, 买买提·沙吾提, 何旭刚, 等. 基于多植被指数组合的棉花叶片叶绿素含量估算[J]. 干旱区研究, 2023, 40(11):1865-1874. |
| [41] | 唐彧哲, 晋永芬, 吴凌波, 等. 红边位置估测玉米叶片叶绿素含量算法的比较[J]. 玉米科学, 2024, 32(4):46-54. |
| [42] | 依尔夏提·阿不来提, 买买提·沙吾提, 白灯莎·买买提艾力, 等. 基于随机森林法的棉花叶片叶绿素含量估算[J]. 作物学报, 2019, 45(1):81-90. |
| [43] | REN Kehui, DONG Yingying, HUANG Wenjing, et al. Monitoring of winter wheat stripe rust by collaborating canopy SIF with wavelet energy coefficients[J]. Computers and electronics in agriculture, 2023,215:108366. |
| [44] | ZHANG Teng, HUANG Mutao, WANG Zhongjing. Estimation of chlorophyll-a concentration of lakes based on SVM algorithm and Landsat 8 OLI images[J]. Environmental Science and Pollution Research, 2020,27:14977-14990. |
| [45] | 王震, 李映雪, 吴芳, 等. 冠层光谱红边参数结合随机森林机器学习估算冬小麦叶绿素含量[J]. 农业工程学报, 2024, 40(4):166-176. |
| [46] | 郭云开, 许敏, 张晓炯, 等. 结合PRO-4SAIL和BP神经网络的叶绿素含量高光谱反演[J]. 测绘通报, 2020(3):21-24. |
| [47] | 王天丽. 基于高光谱和机器学习的作物叶片氮素与叶绿素含量反演研究[D]. 长春: 吉林大学, 2023. |
| [48] | JORDAN C F. Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4):663-666. |
| [49] | YU Kang, LENZ-WIEDEMANN V, CHEN Xinping, et al. Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,97:58-77. |
| [50] | PERSON R L, MILLER L D. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie[J]. Remote Sensing of Environment, 1972,1357-1381. |
| [51] | BLACKBURN G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales:An evaluation of some hyperspectral approaches[J]. Remote Sensing of Environment, 1998, 66(3):273-285. |
| [52] | ROUSE Jr J W, HAAS R H, DEERING D W, et al. Monitoring the vernal advancement and retrogradation green wave effect of natural vegetation[R]. Greenbelt: Goddard Space Flight Center,1973:1-69. |
| [53] | 王家强, 伍维模, 李志军, 等. 基于高光谱指数的天然胡杨叶绿素遥感建模研究[J]. 干旱区资源与环境, 2014, 28(10):95-99. |
| [54] | GITELSON A A, KAUFMAN Y J, MERZLVAK M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3):289-298. |
| [55] | RONDEAUX G, STEVEN M, BARET F. Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55(2):95-107. |
| [56] | 肖汉, 陈秀万, 杨振宇, 等. 基于光谱分析的草地叶绿素含量估测植被指数[J]. 光谱学与光谱分析, 2014, 34(11):3075-3078. |
| [57] | DASH J, CURRAN P. Evaluation of the MERIS terrestrial chlorophyll index(MTCI)[J]. Advances in Space Research, 2006, 39(1):100-104. |
| [58] | DAUGHTRY C S, WALTHALL C L, KIM M S, et al. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance[J]. Remote Sensing of Environment, 2000, 74(2):229-239. |
| [59] | 姚付启, 张振华, 杨润亚, 等. 基于主成分分析和BP神经网络的法国梧桐叶绿素含量高光谱反演研究[J]. 测绘科学, 2010, 35(1):109-112. |
| [60] | 张东彦, 刘镕源, 宋晓宇, 等. 应用近地成像高光谱估算玉米叶绿素含量[J]. 光谱学与光谱分析, 2011, 31(3):771-775. |
| [61] | QI Haixia, ZHU Bingyu, KONG Lingxi, et al. Hyperspectral inversion model of chlorophyll content in peanut leaves[J]. Applied sciences, 2020, 10(7):2259. |
| [62] | 张思楠, 王权, 靳佳, 等. 应用光谱指数法估算多枝柽柳同化枝叶绿素含量[J]. 干旱区研究, 2016, 33(5):1088-1097. |
| [63] | 曹晓明, 冯益明, 史建康, 等. 运用实测高光谱数据对梭梭同化枝叶绿素的估算[J]. 东北林业大学学报, 2020, 48(1):56-61. |
| [64] | 王敬湧, 张明珍, 凌华荣, 等. 干旱胁迫下玉米叶片叶绿素含量与含水量高光谱成像反演方法[J]. 智慧农业(中英文), 2023, 5(3):142-153. |
| [65] | 茹克亚·萨吾提, 阿不都艾尼·阿不里, 尼加提·卡斯木, 等. 基于分数阶微分的春小麦叶绿素含量高光谱估算[J]. 麦类作物学报, 2019, 39(6):738-746. |
| [66] | 姚付启, 张振华, 杨润亚, 等. 基于高光谱曲线峰度、偏度的植被叶绿素含量反演模型研究[J]. 测绘科学, 2009, 34(5):32-35. |
| [67] | 高兴, 李斐, 杨海波, 等. 基于红边位置的马铃薯植株氮浓度估测方法研究[J]. 植物营养与肥料学报, 2019, 25(2):296-310. |
| [68] | 曹英丽, 江凯伦, 刘亚帝, 等. 基于高光谱红边位置提取的水稻叶绿素反演研究[J]. 沈阳农业大学学报, 2021, 52(6):718-728. |
| [69] | 张佳伟, 王仲林, 谭先明, 等. 利用不同红边位置算法估测玉米叶绿素含量[J]. 浙江大学学报(农业与生命科学版), 2021, 47(4):464-472. |
| [70] | DAWSON T P, CURRAN P J. Technical note A new technique for interpolating the reflectance red edge position[J]. International Journal of Remote Sensing, 1998, 19(11):2133-2139. |
| [71] | CLEVERS W P G J, JONG D M S, EPEMA F G, et al. Derivation of the red edge index using the MERIS standard band setting[J]. International Journal of Remote Sensing, 2002, 23(16):3169-3184. |
| [72] | HORLER D N H, DOCKRAY M, BARBER J. The red edge of plant leaf reflectance[J]. International Journal of Remote Sensing, 1983, 4(2):273-288. |
| [73] | BONHAM-CARTER G F. Numerical procedures and computer program for fitting an inverted Gaussian model to vegetation reflectance data[J]. Computers and Geosciences, 1988, 14(3):339-356. |
| [74] | CHO A M, SKIDMORE K A. A new technique for extracting the red edge position from hyperspectral data:The linear extrapolation method[J]. Remote Sensing of Environment, 2006, 101(2):181-193. |
| [75] | PU R, GONG P, BIGING S G, et al. Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(4):916-921. |
| [76] | 李向阳, 刘国顺, 史舟, 等. 利用室内光谱红边参数估测烤烟叶片成熟度[J]. 遥感学报, 2007(2):269-275. |
| [77] | CLEVERS J G P W, KOOISTRA L. Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2011, 5(2):574-583. |
| [78] | ZHANG Aiwu, YIN Shengnan, WANG Juan, et al. Grassland chlorophyll content estimation from drone hyperspectral images combined with fractional-order derivative[J]. Remote Sensing, 2023, 15(23):5623. |
| [79] | 杨峰, 张勇, 谌俊旭, 等. 高光谱数据预处理对大豆叶绿素密度反演的作用[J]. 遥感信息, 2017, 32(4):64-69. |
| [80] | SHEN Lanzhi, GAO Maofang, YAN Jingwen, et al. Winter wheat SPAD value inversion based on multiple pretreatment methods[J]. Remote Sensing, 2022, 14(18):4660. |
| [81] | 尼加提·卡斯木, 师庆东, 王敬哲, 等. 基于高光谱特征和偏最小二乘法的春小麦叶绿素含量估算[J]. 农业工程学报, 2017, 33(22):208-216. |
| [82] | 李莉婕, 岳延滨, 王延仓, 等. 高光谱定量反演火龙果茎枝叶绿素含量的研究[J]. 光谱学与光谱分析, 2021, 41(11):3538-3544. |
| [83] | 张卓然, 常庆瑞, 张廷龙, 等. 基于支持向量机的棉花冠层叶片叶绿素含量高光谱遥感估算[J]. 西北农林科技大学学报(自然科学版), 2018, 46(11):39-45. |
| [84] | 谭林, 何秉宇, 刘卫国, 等. 基于优化SVR高光谱指数的独尾草叶绿素含量估算[J]. 生态学杂志, 2017, 36(2):555-562. |
| [85] | BHADRA S, SAGAN V, MAIMAITIJIANG M, et al. Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning[J]. Remote Sensing, 2020, 12(13):2082. |
| [86] | 王丽爱, 马昌, 周旭东, 等. 基于随机森林回归算法的小麦叶片SPAD值遥感估算[J]. 农业机械学报, 2015, 46(1):259-265. |
| [87] | XU Miaomiao, DAI Jianguo, ZHANG Guoshun, et al. Inversion of glycyrrhiza chlorophyll content based on hyperspectral imagery[J]. Agronomy, 2024, 14(6):1163. |
| [88] | LIU Nan, LI Fei, YANG Haibo, et al. Machine learning models fed with optimized spectral indices to improve inversion accuracy of potato chlorophyll content[J]. Journal of Plant Nutrition and Fertilizers, 2023, 29(8):1531-1542. |
| [89] | 余蛟洋, 常庆瑞, 由明明, 等. 基于高光谱和BP神经网络模型苹果叶片SPAD值遥感估算[J]. 西北林学院学报, 2018, 33(2):156-165. |
| [90] | LIU Meiling, LIU Xiangnan, LI Mi, et al. Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices[J]. Biosystems Engineering, 2010, 106(3):223-233. |
| [91] | WANG Tianli, GAO Maofang, CAO Chunling, et al. Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data[J]. Computers and Electronics in Agriculture, 2022,193:106728. |
| [92] | AN Gangqiang, XING Minfeng, HE Binbin, et al. Using machine learning for estimating rice chlorophyll content from in situ hyperspectral data[J]. Remote Sensing, 2020, 12(18):3104. |
| [93] | LIAN Suyun, GUAN Lixin, PENG Zhongzhen, et al. Retrieval of leaf chlorophyll content in Gannan navel orange based on fusing hyperspectral vegetation indices using machine learning algorithms[J]. Ciência Rural, 2022, 53(3):e20210630. |
| [94] | SHI Hongzhao, GUO Jinjin, AN Jiaqi, et al. Estimation of chlorophyll content in soybean crop at different growth stages based on optimal spectral index[J]. Agronomy, 2023, 13(3):663. |
| [95] | 姚阔, 郭旭东, 南颖, 等. 植被生物量高光谱遥感监测研究进展[J]. 测绘科学, 2016, 41(8):48-53. |
| [96] | ARAúJO M C U, SALDANHA T C B, GALVAO R K H, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and intelligent laboratory systems, 2001, 57(2):65-73. |
| [97] | GAO Dehua, LI Minzan, ZHANG Junyi, et al. Improvement of chlorophyll content estimation on maize leaf by vein removal in hyperspectral image[J]. Computers and electronics in agriculture, 2021,184:106077. |
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