Establishment of Estimation Model for Different Varieties Based on Red Edge Parameters

doi:10.6048/j.issn.1001-4330.2019.06.006

Abstract

Abstract: 【Objective】 To establish the leaf area index estimation model of different varieties of drip irrigation by using the dynamic variation rule of red-edge parameters. 【Method】Taking Xinluzao No.50, Xinluzao No.58 and Lumianyan No. 24 (hybrid cotton) as experimental materials, the dynamic changes of LAI and red edge position were analyzed, and the estimation model of leaf red edge parameter -LAI of drip irrigation cotton was constructed.【Result】The fastest growing period of LAI appeared between 40 and 70 days, but the LAI growth rates of different varieties of drip-irrigation cotton showed obvious differences, which were as follows: Lumianyan No.24 > Xinluzao No.50 > Xinluzao No.58. In this experiment, different varieties of cotton showed blue shift during the occurrence of pests and diseases, and red shift under normal growth of cotton. In this experiment, the red edge parameters were significantly correlated with LAI. Among the three estimation models, Lumianyan No.24 had the highest accuracy (R² = 0.816,8, RMSE = 0.77).【Conclusion】The LAI can be effectively estimated by the established estimation models. The experimental results can provide theoretical basis for promoting the development of precision agriculture in Xinjiang.

Key words: cotton; hyperspectral; leaf area index; red edge parameter; remote sensing

摘要： 【目的】利用红边参数的动态变化规律建立不同品种滴灌棉花叶面积指数估测模型。【方法】以棉花品种新陆早50号、新陆早58号和鲁棉研24号(杂交棉)为材料,分析LAI和红边位置的动态变化,构建滴灌棉花叶片红边参数-LAI估算模型。【结果】LAI增长最快时期均出现在40~70 d,不同品种滴灌棉花的LAI增长速率有明显的差异,表现为:鲁棉研24号>新陆早50号>新陆早58号;不同品种棉花均在病虫害发生期出现蓝移现象,在棉花正常生长下,出现红移现象。红边参数与LAI均达到极显著相关,构建3个估算模型中,鲁棉研24号精度最高(R²=0.816 8 ,RMSE=0.77)。【结论】建立的估算模型均可对LAI进行有效估测。

关键词: 棉花, 高光谱, 叶面积指数, 红边参数, 遥感

CLC Number:

S562

GE Yuan-mei, CHEN Xiang-yu, HONG Shuai, MA Lu-lu, L Xin , ZHANG Ze. Establishment of Estimation Model for Different Varieties Based on Red Edge Parameters[J]. Xinjiang Agricultural Sciences, 2019, 56(6): 1032-1040.

葛元梅, 陈翔宇, 洪帅, 马露露, 吕新, 张泽. 基于红边参数不同品种的估算模型[J]. 新疆农业科学, 2019, 56(6): 1032-1040.

References 21

[1]	贺佳, 刘冰锋, 李军. 不同生育时期冬小麦叶面积指数高光谱遥感监测模型[J].农业工程学报, 2014, 30(24):141-150.HE Jia, LIU Bing-feng, LI Jun. (2014).Monitoring model of leaf area index of winter wheat based on hyperspectral reflectance at different growth stages [J]. Transactions of the Chinese Society of Agricultural Engineering, 30 (24): 141-150. (in Chinese)
[2]	刘洋, 刘荣高, 陈镜明, 等. 叶面积指数遥感反演研究进展与展望[J].地球信息科学学报, 2013, 15(5):734-743.LIU Yang, LIU Rong-gao, CHEN Jing-ming, et al. (2013). Current Status and Perspectives of Leaf Area Index Retrieval from Optical Remote Sensing Data [J]. Journal of Geo-Information Science, 15 (5): 734-743. (in Chinese)
[3]	黄敬峰, 王渊, 王福民, 等. 油菜红边特征及其叶面积指数的高光谱估算模型[J].农业工程学报, 2006, 22(8):22-26.HUANG Jing-feng, WANG Yuan, WANG Fu-min, et al. (2006). Red edge characteristics and leaf area index estimation model using hyperspectral data for rape [J]. Transactions of the Chinese Society of Agricultural Engineering, 22 (8): 22-26. (in Chinese)
[4]	Filella, I., & Penuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15(7):1,459-1,470.
[5]	李园园, 宋于洋, 姜小露. 利用植被指数(Ⅵ)提取遥感影像荒漠林覆盖度信息的差异性研究[J].石河子大学学报(自然科学版), 2015, 33(1):72-77.LI Yuan-yuan, SONG Yu-yang, JANG Xiao-lu. (2015).Study on the differences among 3 kinds of vegetables indexes (Ⅵ) in extracting coverage of desert forest with remote sensing images [J]. Journal of Shihezi University(Natural Science), 33 (1): 72-77. (in Chinese)
[6]	Horler, D. N. H. , Dockray, M. , & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2):273-288.
[7]	Mutanga, O. , & Skidmore, A. K. (2007). Red edge shift and biochemical content in grass canopies. Isprs Journal of Photogrammetry & Remote Sensing, 62(1):34-42.
[8]	张向娟, 张亚黎, 张天宇, 等. 棉花长期干旱后复水叶片光合特性及碳氮含量的变化[J].石河子大学学报(自然科学版), 2014, 32(2):138-142.ZHANG Xiang-juan, ZHANG Ya-li, ZHANG Tian-yu, et al. (2014). Change of rewatering after long-term drought stress on photosynthesis and content of carbon and nitrogen cotton [J]. Journal of Shihezi University (Natural Science), 32 (2): 138-142. (in Chinese)
[9]	Gazala, I. F. S. , Sahoo, R. N. , Pandey, R. , Mandal, B. , Gupta, V. K. , & Singh, R. , et al. (2013). Spectral reflectance pattern in soybean for assessing yellow mosaic disease. Indian Journal of Virology, 24(2):242-249.
[10]	Niemann, K. O. (1995). Remote sensing of forest stand age using airborne spectrometer data. Photogrammetric Engineering & Remote Sensing, 61(9):1,119-1,127.
[11]	Chen, B., Li, S., Wang, K., Zhou, G., & Bai, J. (2012). Evaluating the severity level of cotton＼r, verticillium＼r, using spectral signature analysis. International Journal of Remote Sensing, 33(9):2,706-2,724.
[12]	Smith, K. L., M.D. Steven, & Colls, J. J. (2004). Use of hyperspectral derivative ratios in the red-edge region to identify plant stress responses to gas leaks. Remote Sensing of Environment, 92(2):207-217.
[13]	陈兵, 李少昆, 王克如, 等. 基于TM影像光谱指数的棉花病害严重度估测[J].红外与毫米波学报, 2011, 30(5):451-456.CHEN Bing, LI Shao-kun, WANG Ke- ru, et al. (2011). Estimation severity level of the cotton disease based on spectral indices of TM image [J]. Journal of Infrared and Millimeter Waves, 30(5): 451-456.(in Chinese)
[14]	谢巧云.考虑红边特性的多平台遥感数据叶面积指数反演方法研究[D].中国科学院大学(中国科学院遥感与数字地球研究所), 2017.XIE Qiao-yun. (2017).Research on leaf area index retrieves methods based on the red edge bands from multi-platform remote sensing data [D]. PhD Dissertation. University of the Chinese Academy of Sciences (Institute of Remote Sensing and digital Earth, Chinese Academy of Sciences). (in Chinese)
[15]	黄春燕, 王登伟, 张煜星. 基于棉花红边参数的叶绿素密度及叶面积指数的估算[J].农业工程学报, 2009, 25(S2):137-141.HUANG Chun-yan, WANG Deng-wei, ZHANG Yu-xing. (2009). Estimation of cotton canopy chlorophyll density and leaf area index based on red-edge parameters [J]. Transactions of the Chinese Society of Agricultural Engineering, 25 (S2): 137-141.(in Chinese)
[16]	王秀珍, 黄敬峰, 李云梅, 等. 水稻叶面积指数的高光谱遥感估算模型[J].遥感学报, 2004, 8(1):81-88.WANG Xiu-zhen, HUANG Jing-feng, LI Yun- mei, et al. (2004). The study of hyperspectral remote sensing estimation models about LAI of rice [J]. Journal of Remote Sensing, 8(1): 81-88. (in Chinese)
[17]	马文君.棉花生理生化参数高光谱反演模型研究[D]. 陕西杨堎:西北农林科技大学, 2016.MA Wen-jun. (2016). Study on hyperspectral inversion model of cotton physiological and biochemical parameters [D]. Master Dissertation. Northwest A & F University, Yangling. (in Chinese)
[18]	Lee, K. S. , Cohen, W. B. , Kennedy, R. E. , Maiersperger, T. K. , & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment, 91(3-4):508-520.
[19]	田明璐.西北地区冬小麦生长状况高光谱遥感监测研究[D].陕西杨凌:西北农林科技大学, 2017.TIAN Ming-lu. (2017). Monitoring winter wheat growth conditions in the northeast region of China by using hyperspectral remote sensing [D]. Master Dissertation. Northwest A & F University, Yangling. (in Chinese)
[20]	秦占飞, 常庆瑞, 申健, 等. 引黄灌区水稻红边特征及SPAD高光谱预测模型. [J].武汉大学学报(信息科学版), 2016, 41(9):1-8.QING Zhan-fei, CHANG Qing-Rui, SHEN Jian, et al. (2016). Red edge characteristics and SPAD estimation model using hyperspectral data for rice in Ningxia irrigation zone [J] Geomatics and Information Science of Wuhan University, 41(9):1-8. (in Chinese)
[21]	唐延林, 王秀珍, 李建龙, 等. 棉花高光谱及其红边特征(Ⅱ) [J].棉花学报, 2003, 15(1):215-220.TANG Yan-lin, WANG Xiu - zhen, LI Jian - long, et al. (2003). The hyperspectral and their re edge characteristics of cotton-Ⅱ[J]. Cotton Science, 15(1):215-220. (in Chinese)