|
|
|
|
|
|
The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3 |
1. Institute of Agricultural Information and Economics, Shandong Academy of Agricultural Sciences, Jinan 250100, China
2. Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3. Eco-redline Supervision Conter, Satellite Environment Center, Ministry of Ecology and Environment, Beijing 100029, China
|
|
|
Abstract Anthocyanin (Anth) is the third major group of leaf pigments. It can provide valuable information about plant physiology, and the information on the dynamics of their concentrations is a key to understand plants' the physiological reaction and resistance to different environmental stress factors brought about by episodic events or seasonal fluctuations. Traditionally, pigments are extracted from vegetation with spectrophotometry or high-pressure liquid chromatography, which are destructive and do not permit repeated measurements on the same samples. Optical methods monitoring plant physiological status through measuring leaf optical properties (absorbance or reflectance) possess several advantages over traditional destructive methods. However, there is a lack of research on the inversion of Anth through optical methods. Overlapping features in the specific absorption coefficient of pigments makes retrieving Anth content through remote sensing challenging. Understanding the relationship between Anth and leaf optical properties is necessary and helpful to estimate Anth content from leaves. This research used PROSPECT-D, a radiative transfer model including Anth parameter, to construct leaf spectral data in different parameter conditions. The global sensitivity analysis was conducted to quantify the influence of Anth on leaf optical properties through the modified Sobol method. we aimed to find sensitive Anth bands and calculate spectral indices relating to Anth content. Furthermore, inversing strategies based on hyperspectral narrow wavebands and spectral indices, including the Anthocyanin Reflectance Index (ARI) and modified Anthocyanin Reflectance Index (mARI), were discussed. The results showed that: (1) Anth can influence leaf optical properties in the 400~689 nm range, and leaf reflectance decreased in the visible band when Anth concentration increased. (2) The leaf reflectance in 467~589 nm was sensitive to the dynamics of Anth concentrations. There was the highest total sensitivity index of Anth at 509 nm. Chlorophyll and carotenoid had an impact on leaf reflectance in 467~589 nm. According to total sensitivity indices of chlorophyll and carotenoid, three spectral regions can be formed: 467~505 nm (influenced by carotenoids, chorophylls and Anth), 506~541 nm (influenced by Anth and carotenoids), 542~589 nm (influenced by chorophylls and Anth). (3) Anth concentration correlated best with leaf reflectance at 560 nm. Because of the pigments' overlapping absorption features, including chorophylls and carotenoids, Anth had better relationship with spectral indices than reflectance in individual narrow wavebands. Spectral indices partly removed other plant pigments' influence on plant leaf reflectance. Consequently, they can describe the dynamics of Anth concentrations accurately. This research will provide a theory method for remote sensing estimation of Anth content at the leaf scale.
|
Received: 2022-07-25
Accepted: 2022-11-04
|
|
|
[1] Jay S, Maupas F, Bendoula R, et al. Field Crops Research, 2017, 210: 33.
[2] GONG Zhao-ning, ZHAO Ya-li, ZHAO Wen-ji, et al(宫兆宁, 赵雅莉, 赵文吉, 等). Acta Ecologica Sinica(生态学报), 2014, 34(20): 5736.
[3] Close D C, Beadle C L. The Botanical Review, 2003, 69(2): 149.
[4] Lev-Yadun S, Gould K S. Springer New York, 2008: 22.
[5] Blackburn G A. Journal of Experimental Botany, 2007, 58(4): 855.
[6] WU Wei-mo, NIU Jian-long, WEN Shan-ju, et al(伍维模, 牛建龙, 温善菊, 等). Journal of Tarim University(塔里木大学学报), 2009, 21(4): 61.
[7] Allen W A, Gausman H W, Richardson A J, et al. Journal of the Optical Society of America, 1969, 59(10): 1376.
[8] Jacquemoud S, Baret F. Remote Sensing of Environment, 1990, 34(2): 75.
[9] Féret J B, Gitelson A A, Noble S D, et al. Remote Sensing of Environment, 2017, 193: 204.
[10] Gitelson A A, Solovchenko A. CRC Press-Taylor and Francis Group, 2018: 135.
[11] Saltelli A, Sobol' I M. Reliability Engineering and System Safety, 1995, 50(3): 225.
[12] MA Han-qing, ZHANG Kun, MA Chun-feng, et al(马瀚青, 张 琨, 马春锋, 等). National Remote Sensing Bulletin(遥感学报), 2022, 26(2): 286.
[13] WANG Li-juan, NIU Zheng(王李娟, 牛 铮). Remote Sensing Technology and Application(遥感技术与应用), 2014, 29(2): 219.
[14] Asner G P. Remote Sensing of Environment, 1998, 64(3): 234.
[15] Saltelli A, Ratto M, Andres T, et al. John Wiley & Sons Ltd., 2008: 304.
[16] MA Jian-wei, HUANG Shi-feng, LI Ji-ren, et al(马建威, 黄诗峰, 李纪人, 等). Bulletin of Surveying and Mapping(测绘通报), 2016, 3: 33.
[17] Cukier R I, Fortuin C M, Shuler K E, et al. The Journal of Chemical Physics, 1973, 59: 3873.
[18] Saltelli A, Tarantola S, Chan K. Technometrics, 1999, 41(1): 39.
[19] LI Yan, HUANG Chun-lin, LU Ling(李 艳, 黄春林, 卢 玲). Remote Sensing Technology and Application(遥感技术与应用), 2014, 29(5): 719.
[20] Sobol' I M. Mathematical Modelling and Computational Experiment, 1993, 1: 407.
[21] Saltelli A, Annoni P, Azzini I, et al. Computer Physics Communications, 2010, 181: 259.
[22] Morcillo-Pallarés P, Rivera-Caicedo J P, Belda S, et al. Remote Sensing, 2019, 11(20): 2418.
[23] Song X, Bryan B A, Paul K I, et al. Ecological Modelling, 2012, 247: 135.
[24] Hosgood B, Jacquemoud S, Andreoli G, et al. European Commission-Joint Research Centre, 1994: 1.
[25] Féret J B, François C, Asner G P, et al. Remote Sensing of Environment, 2008, 112(6): 3030.
[26] Gitelson A A, Chivkunova O B, Merzlyak M N. American Journal of Botany, 2009, 96(10): 1861.
[27] Saltelli A. Computer Physics Communications, 2002, 145(2): 280.
[28] REN Qi-wei, CHEN Yang-bo, ZHOU Hao-lan, et al(任启伟, 陈洋波, 周浩澜, 等). Yangtz River(人民长江), 2010, 41(19): 91.
[29] Gitelson A A. CRC Press-Taylor and Francis Group, 2011: 141.
[30] Gitelson A A. Geophysical Research Letters, 2006, 33: L11402.
[31] Jacquemoud S. Remote Sensing of Environment, 1993, 44(2-3): 281.
|
[1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[2] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[3] |
ZHU Wen-jing1, 2,FENG Zhan-kang1, 2,DAI Shi-yuan1, 2,ZHANG Ping-ping3,JI Wen4,WANG Ai-chen1, 2,WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
[4] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[5] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[6] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[7] |
CUI Zhen-zhen1, 2, MA Chao1, ZHANG Hao2*, ZHANG Hong-wei3, LIANG Hu-jun3, QIU Wen2. Absolute Radiometric Calibration of Aerial Multispectral Camera Based on Multi-Scale Tarps[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3571-3581. |
[8] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
[9] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[10] |
CHEN Hao1, 2, WANG Hao3*, HAN Wei3, GU Song-yan4, ZHANG Peng4, KANG Zhi-ming1. Impact Analysis of Microwave Real Spectral Response on Rapid Radiance Simulation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3260-3265. |
[11] |
WANG Lin, WANG Xiang*, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320. |
[12] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
[13] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
[14] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
[15] |
KONG Bo1, YU Huan2*, SONG Wu-jie2, 3, HOU Yu-ting2, XIANG Qing2. Hyperspectral Characteristics and Quantitative Remote Sensing Inversion of Gravel Grain Size in the North Tibetan Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2381-2390. |
|
|
|
|