PROSPECT模型的特征波长优化与作物叶绿素含量检测

doi:10.3964/j.issn.1000-0593(2022)05-1514-08

摘要
参考文献
相关文章 (15)

全文: PDF (4749 KB)
输出: BibTeX | EndNote (RIS)

摘要：叶绿素是作物生长诊断的重要参数，对其进行高效检测是农田精细化管理的基础。PROSPECT模型是作物光谱学检测研究的重要工具，可为建立高精度叶绿素诊断模型提供数据集基础。为了建立具有普适性的田间玉米作物叶绿素含量检测模型，使用PROSPECT模型输入叶片结构参数和生化参数模拟叶片400～2 500 nm波段反射率曲线10 650条。在其他参数设置保持不变的情况下，分析光谱反射率曲线对叶绿素含量参数的敏感性，结果显示叶绿素含量仅在400～780 nm区间对光谱反射率曲线产生影响。讨论了3种叶绿素检测特征波长筛选策略，分别为：根据敏感性分析结果，选出548～610和694～706 nm区域共计76个波长，记为SEN-BAND；基于反向区间偏最小二乘法（Bi-PLS）筛选5个区间共计91个波长，记为BP-BAND；基于连续投影算法（SPA），在叶绿素影响区域400～780 nm筛选10个特征波长，记为SPA-BAND。进而使用2019年、2020年两年期田间实测玉米叶片光谱反射率曲线和叶绿素含量数据，分别应用上述3种方法选取的特征波长构建玉米叶片叶绿素含量检测模型。结果显示，使用SPA-BAND特征波长构建的模型，在两年期数据中均得到最佳结果。2019年数据模型建模集决定系数（R²_c）为0.815 6，建模集均方根误差RMSEC为2.908 6，验证集决定系数（R²_v）为0.799 5，验证集均方根误差RMSEV为2.997 7。2020年数据模型建模集决定系数（R²_c）为0.949 2，建模集均方根误差RMSEC为0.976 8，验证集决定系数（R²_v）为0.910 2，验证集均方根误差RMSEV为1.562 9。表明，基于PROSPECT模型筛选叶绿素含量特征波长建立的叶绿素诊断模型具有普适性。

关键词：PROSPECT模型；叶绿素；波长筛选；SPA；Bi-PLS；PLSR

Abstract：Chlorophyll is an important biochemical parameter involved in crop growth. Accurate detection of chlorophyll in real-time has great significance for the precision management of farmland. The PROSPECT model can simulate the reflectivity and transmissibility of leaf at 400~2 500 nm based on leaf’s input structural and biochemical parameters. This study used the PROSPECT model to generate 10 650 reflectivity curves of maize leaf under different input parameters. The sensitivity of the spectral reflectance curve to the chlorophyll content parameter was analyzed when other parameters remained unchanged. The result shows that the chlorophyll content only affects the spectral reflectance curve in the range of 400~780 nm. According to the sensitivity analysis result, 76 wavelengths in 548~610 and 694~706 nm were selected as the characteristic wavelengths of chlorophyll content, which were recorded as SEN-BAND. Based on Backward Interval PLS (Bi-PLS), 5 intervals of 91 characteristic wavelengths were selected, recorded as BP-BAND. Based on the Successive Projections Algorithm (SPA), 10 characteristic wavelengths were selected in chlorophyll-influenced area in 400~780 nm, recorded as SPA-BAND. The PLS detection model of chlorophyll content based on the three characteristic wavelengths was constructed with measured field data in 2019 and 2020. The results show that the -SPA-BAND model has the best results in both 2019 and 2020 datasets. In the 2019 dataset, the coefficient of determination (R²_c) of the modeling set is 0.815 6, the root mean square error (RMSEC) of the modeling set is 2.908 6, the coefficient of determination (R²_v) of the validation set is 0.799 5, and the root means square error (RMSEV) of the validation set is 2.997 7. In the 2020 database, the coefficient of determination (R²_c) of the modeling set is 0.949 2, the root mean square error (RMSEC) of the modeling set is 0.976 8, the coefficient of determination (R²_v) of the validation set was 0.910 2, and the root means square error (RMSEV) of the validation set was 1.562 9. Therefore, the characteristic wavelength of chlorophyll content can be selected under the influence of multiple factors by constructing spectral reflectance curves with multi-parameter input based on the PROSPECT model and the characteristic wavelengths of chlorophyll content can be verified in multi-year data.

Key words：PROSPECT model; Chlorophyll; Wavelength selection; SPA; Bi-PLS; PLSR

收稿日期: 2021-03-25 修订日期: 2021-06-02

中图分类号:

S127

基金资助: 国家“十三五”重点研发计划课题（2018YFD0300505-1）, 国家自然科学基金项目(31971785, 31971786) ，中国农业大学研究生教学改革建设项目(JG2019004, YW2020007, JG202026)资助

通讯作者: 李民赞 E-mail: limz@cau.edu.cn

作者简介: 张俊逸，1988年生，中国农业大学信息与电气工程学院博士研究生 e-mail: junyizh@cau.edu.cn

引用本文:

张俊逸，高德华，宋迪，乔浪，孙红，李民赞，李莉. PROSPECT模型的特征波长优化与作物叶绿素含量检测[J]. 光谱学与光谱分析, 2022, 42(05): 1514-1521.
ZHANG Jun-yi, GAO De-hua, SONG Di, QIAO Lang, SUN Hong, LI Min-zan, LI Li. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1514-1521.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2022)05-1514-08 或 https://www.gpxygpfx.com/CN/Y2022/V42/I05/1514

[1] HE Yong，PENG Ji-yu，LIU Fei，et al（何勇，彭继宇, 刘飞, 等）. Transactions of the Chinese Society for Agricultural Engineering（农业工程学报），2015，31(3): 174.
[2] WANG Hao-yun，CAO Xue-lian，SUN Yun-xiao，et al（王浩云，曹雪莲，孙云晓，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报），2021，52(3): 202.
[3] WANG Qi，SONG Xiao-yu，YANG Gui-jun，et al（王琦, 宋晓宇, 杨贵军, 等）. China Agriculture Information（中国农业信息），2018，30(6): 35.
[4] SUN Hong，CHEN Xiang，SUN Zi-chun，et al（孙红，陈香，孙梓淳，等）. Transactions of the Chinese Society of Agricultural Machinery（农业机械学报），2018，49(3): 173.
[5] QIU Nian-wei，WANG Xiu-shun，YANG Fa-bin，et al（邱念伟，王修顺，杨发斌，等）. Chinese Bulletin of Botany（植物学报），2016，51(5): 667.
[6] GU Dong-dong，WANG Wan-zhang，HU Jian-dong，et al（古冬冬，王万章，胡建东，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2015，31(S2): 179.
[7] Qi Haixia，Zhu Bingyu，Kong Lingxi，et al. Applied Science-Basel，2020，10(7): 2259.
[8] DONG Zhe，YANG Wu-de，ZHANG Mei-jun，et al（董哲，杨武德，张美俊，等）. Crops（作物杂志），2019，190(3): 132.
[9] Li Dong，Chen Jingming，Zhang Xiao，et al. Remote Sensing of Environment，2020，248: 111985.
[10] Berger Katja，Atzberger Clement，Danner Martin，et al. Remote Sensing, 2018, 10(1): 85.
[11] LEI Xiang-xiang，ZHAO Jing，LIU Hou-cheng，et al（雷祥祥，赵静, 刘厚诚, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2019，39(10): 3256.
[12] LÜ Jie，WANG Kang-ning，LI Chong-gui，et al（吕杰，汪康宁，李崇贵，等）. Journal of China University of Mining & Technology（中国矿业大学学报），2016，45(2): 405.
[13] Verrelst Jochem，Dethier Sara，Rivera Juan-Pablo, et al. IEEE Geoscience and Remote Sensing Letters, 2016, 13(7): 1012.
[14] Feret J B, Gitelson A A, Noble S D, et al. Remote Sensing of Environment, 2017, 193: 204.
[15] CHENG Jie-hong，CHEN Zheng-guang，ZHANG Qing-hua（程介虹，陈争光，张庆华）. Journal of Agricultural Science and Technology（中国农业科技导报），2020，22(1): 162.