农作物冠层光谱分析及反演技术综述

doi:10.13474/j.cnki.11-2246.2019.0277

测绘通报 ›› 2019, Vol. 0 ›› Issue (9): 13-17.doi: 10.13474/j.cnki.11-2246.2019.0277

农作物冠层光谱分析及反演技术综述

李月¹, 何宏昌¹, 王晓飞¹, 张国民²

1. 黑龙江大学, 黑龙江哈尔滨 150001;
2. 中国科学院北方粳稻分子育种联合研究中心, 黑龙江哈尔滨 150001

收稿日期:2018-12-29 修回日期:2019-03-21 出版日期:2019-09-25 发布日期:2019-09-28
作者简介:李月(1982-),女,博士,讲师,从事定量遥感、精准施肥的理论和应用研究工作。E-mail:yueliang888@163.com
基金资助:
国家重点研发计划（2016YFB0502502）；黑龙江省物联网感知层与传感网络技术创新服务平台以及黑龙江省省属高等学校基本科研业务费（KJCXZD201704）

Review on crop canopy spectral analysis and retrieval

LI Yue¹, HE Hongchang¹, WANG Xiaofei¹, ZHANG Guomin²

1. School of Electrical Engineering, Heilongjiang University, Harbin 150001, China;
2. Joint Research Center of Molecular Breeding for Japonica Rice of Chinese Academy of Sciences, Harbin 150001, China

Received:2018-12-29 Revised:2019-03-21 Online:2019-09-25 Published:2019-09-28

摘要/Abstract

摘要： 农作物的冠层光谱反射率与作物的氮含量、叶绿素含量及叶面积指数等参数之间具有很强的相关性，通过对作物冠层光谱进行分析可反演出作物的生物物理参数，并应用在长势分析、产量预测、病虫害预警等领域。本文首先阐述了作物冠层反射率采集方法，对地面、机载及遥感卫星3个采集层面的优缺点进行了对比；其次给出了植被指数构建原理及常用植被指数，分析了物理模型反演法和统计反演法的复杂度和性能；最后提出了农作物冠层光谱分析及反演技术的下一步发展方向及面临的挑战。

关键词: 冠层光谱分析, 生物物理参数反演, 植被指数, 机器学习, 冠层反射物理模型

Abstract: The crop canopy spectral reflectance has a strong correlation with the biological and physical parameters of crops such as the leaf nitrogen content, the chlorophyll content and the leaf area index. The biological and physical parameters can be retrieved by crop canopy spectral analysis, and applied to the field of growth analysis, yield prediction, pest and disease warning and so on. Firstly, three kinds of canopy reflectance acquisition methods based on ground, airborne and space-borne spectral meters are expounded separately, whose advantages and disadvantages are compared. Secondly, the principle of vegetation index construction is given, and the complexity and performance of physical model based retrieval and statistical retrieval methods are analyzed. Finally, the future and challenges of crop canopy spectral analysis and retrieval are suggested.

Key words: canopy spectral analysis, biological and physical parameters retrieval, vegetation index, machine learning, canopy reflectance physical model

中图分类号:

P237

李月, 何宏昌, 王晓飞, 张国民. 农作物冠层光谱分析及反演技术综述[J]. 测绘通报, 2019, 0(9): 13-17.

LI Yue, HE Hongchang, WANG Xiaofei, ZHANG Guomin. Review on crop canopy spectral analysis and retrieval[J]. 测绘通报, 2019, 0(9): 13-17.

参考文献

[1] DIACONO M, RUBINO P, MONTEMURRO F. Precision nitrogen management of wheat-a review[J]. Agronomy for Sustainable Development, 2013, 33(1):219-241.
[2] XU X G, ZHAO C J, WANG J H, et al. Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley[J]. Precision Agriculture, 2014, 15(2):227-240.
[3] PAVULURI K, CHIM B K, GRIFFEY C A, et al. Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat[J]. Precision Agriculture, 2015, 16(4):405-424.
[4] XUE L H, LI G H, QIN X, et al. Topdressing nitrogen recommendation for early rice with an active sensor in south China[J]. Precision Agriculture, 2014, 15(1):95-110.
[5] QUEMADA M, GABRIEL J L, TEJADA P Z, et al. Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization[J]. Remote Sensing, 2014, 6(4):2940-2962.
[6] ZHOU X F, HUANG W J, KONG W P, et al. Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements[J]. Advances in Space Research, 2016, 58(9):1627-1637.
[7] WANG Z H, SKIDMOREA A K, WANG T J, et al. Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects[J]. International Journal of Applied Earth Observation and Geoinformation, 2017, 54:84-94.
[8] CATUREGLI L, CORNIGLIA M, GAETANI M, et al. Unmanned aerial vehicle to estimate nitrogen status of turfgrasses[J]. PLoS One, 2016, 11(6):97-127.
[9] MARESMA A, ARIZA M, MARTíNEZ E, et al. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize from a standard UAV service[J]. Remote Sensing, 2016, 8(2):973.
[10] BAUSCH W C, KHOSLA R. QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize[J]. Precision Agriculture, 2010, 11(2):274-290.
[11] BASSO B, FIORENTINO C, CAMMARANO D. Variable rate nitrogen fertilizer response in wheat using remote sensing[J]. Precision Agriculture, 2016, 17(2):168-182.
[12] 黄敬峰, 王福民, 王秀珍. 水稻高光谱遥感实验研究[M]. 1版. 杭州:浙江大学出版社, 2010.
[13] CLEVERS J G P W, KOOISTRA L, BRANDE M M M. Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop[J]. Remote Sensing, 2017, 9(1):405.
[14] MORENO F R, CID F L. A decision tree for nitrogen application based on a low cost radiometry[J]. Precision Agriculture, 2012, 13(1):646-660.
[15] LEBOURGEOIS V, BE'GUE'A, LABBE' S, et al. A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring[J]. Precision Agriculture, 2012, 13(5):525-541.
[16] NILSON T, KUUSK A. A reflectance model for the homogeneous plant canopy and its inversion[J]. Remote Sensing of Environment, 1989, 27:157-167.
[17] MRIDHA N, SAHOO R N, SEHGAL V K, et al. Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters[J]. International Agrophysics, 2015, 29:201-212.
[18] DONG T F, MENG J H, SHANG J L, et al. Evaluation of chlorophyll-related vegetation indices using simulated Sentinel-2 data for estimation of crop fraction of absorbed photosynthetically active radiation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8):4049-4059.
[19] LI G X, WANG C, FENG M C, et al. Hyperspectral prediction of leaf area index of winter wheat in irrigated and rainfed fields[J]. PLoS One, 2017, 12(8):98-110.
[20] 雷宇斌, 朱善宽, 郭云开,等. 极限学习机辅助下路域植被叶面积指数的反演[J]. 测绘通报, 2018(9):82-86.
[21] 尤号田, 邢艳秋, 彭涛, 等. LiDAR不同强度校正法对樟子松叶面积指数估测的影响[J]. 测绘学报, 2018, 47(2):170-179.
[22] JOCHEM V, JORDI M, LUIS A, et al. Machine learning regression algorithms for biophysical parameter retrieval:opportunities for sentinel-2 and -3[J]. Remote Sensing of Environment, 2012, 118:127-139.

农作物冠层光谱分析及反演技术综述

Review on crop canopy spectral analysis and retrieval

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