|
|
|
|
|
|
Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3 |
1. College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,China
2. Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
3. College of Arts and Sciences,Shanghai Maritime University,Shanghai 201306,China
|
|
|
Abstract Solar-induced chlorophyll fluorescence (SIF) can sensitively reflect crop disease stress information, but the geometric structure of canopy and other factors seriously affected the ability of SIF to capture changes in photosynthetic function and stress status of vegetation. Therefore, in this paper, the normalized difference vegetation index (NDVI) and MERIS terrestrial chlorophyll index (MTCI), which can sensitively reflect crop biomass, were integrated with SIFP (SIFP-NDVI,SIFP-MTCI,SIFP-NDVI*MTCI), and the remote sensing monitoring accuracy of SIF on wheat stripe rust before and after the integration was compared and analyzed. The results show that: (1) at the O2-B, O2-A and H2O absorption at 719 nm bands, integrated reflectance spectral indices of SIFP-NDVI, SIFP-MTCI and SIFP-NDVI*MTCI showed different improvements in correlation with disease index (DI) than SIFP. The O2-B band increased the most significantly, by 23.48%, 33.61% and 36.49% respectively, while the O2-A band increased the least by 2.39%, 2.14% and 1.51%, respectively. (2) If SIFP-NDVI and SIFP-MTCI were regarded as independent variables respectively, the averaged R2 value of the prediction model based on random forest regression (RFR) algorithm were increased by 1.15% and 4.02%, and the averaged RMSE value were decreased by 2.7% and 14.41%, respectively, compared to those with SIFP as the independent variable. (3) The prediction model based on SIFP-NDVI*MTCI gave the best performance with an R2 value 5.74% higher than that of SIFP, and an RMSE value 22.52% lower than that of SIFP. The results of this paper are of great significance to improve the accuracy of remote sensing monitoring of wheat stripe rust and have a certain reference value for disease monitoring of other crops.
|
Received: 2021-01-31
Accepted: 2021-03-06
|
|
Corresponding Authors:
JING Xia
E-mail: jingxia1001@163.com
|
|
[1] MIN Wen-jiang,LÜ Jun-hai,DONG Zhi-ping,et al(闵文江,吕军海,董志平,等). Modern Rural Science and Technology(现代农村科技),2019,(10):108.
[2] CHEN Si-yuan,JING Xia,DONG Ying-ying,et al(陈思媛,竞 霞,董莹莹,等). Remote Sensing Technology and Application(遥感技术与应用),2019,34(3):511.
[3] Ashourloo D,Mobasheri M R,Huete A. Remote Sensing,2014,6(6):4723.
[4] XU Ling-ling(许凌凌). Journal of Yichun University(宜春学院学报),2019,41(6):76.
[5] ZHANG Zhao-ying,WANG Song-han,QIU Bo,et al(章钊颖,王松寒,邱 博,等). Journal of Remote Sensing(遥感学报),2019,23(1):37.
[6] JING Xia,ZHANG Teng,BAI Zong-fan,et al(竞 霞,张 腾,白宗璠,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2020,51(11):253.
[7] ZHAO Ye,JING Xia,HUANG Wen-jiang,et al(赵 叶,竞 霞,黄文江,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2019,39(9):2739.
[8] JING Xia,LÜ Xiao-yan,ZHANG Chao,et al(竞 霞,吕小艳,张 超,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报),2020,51(6):191.
[9] BAI Zong-fan,JING Xia,ZHANG Teng,et al(白宗璠,竞 霞,张 腾,等). Acta Agronomica Sinica(作物学报),2020,46(8):1248.
[10] Du S S,Liu L Y,Liu X J,et al. Remote Sensing,2017,9(9):911.
[11] Porcar-Castell A,Tyystjärvi E,Atherton J,et al. Journal of Experimental Botany,2014,65(15):4065.
[12] General Administration of Quality Supervision Inspection and Quarantine of the People’s Republic of China(中华人民共和国国家质量监督检验检疫总局). GB/T 15795—2011 Rules for Monitoring and Forecast of the Wheat Stripe Rust (Puccinia Striiformis West) (GB/T 15795—2011 小麦条锈病测报技术规范). Beijing:Standards Press of China(北京:中国标准出版社),2011.
[13] Liu L Y,Liu X J,Hu J C. European Journal of Remote Sensing,2015,48:743.
[14] Goulas Y,Fournier A,Daumard F,et al. Remote Sensing,2017,9(1): 97.
[15] Liu X J,Guanter L,Liu L Y,et al. Remote Sensing of Environment,2019,231.
[16] GUO Yun-kai,LIU Yun-ling,ZHANG Xiao-jiong,et al(郭云开,刘雨玲,张晓炯,等). Engineering of Surveying and Mapping(测绘工程),2019,28(6):17.
|
[1] |
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. |
[2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[3] |
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. |
[4] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[5] |
YANG Sen1, ZHANG Xin-ao1, XING Jian1, DAI Jing-min2. Study on Multi-Feature Model Fusion Variety Classification and Multi-Parameter Appearance Inspection for Milled Rice[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2837-2842. |
[6] |
LUAN Xin-xin1, ZHAI Chen2, AN Huan-jiong3, QIAN Cheng-jing2, SHI Xiao-mei2, WANG Wen-xiu3, HU Li-ming1*. Applications of Molecular Spectral Information Fusion to Distinguish the Rice From Different Growing Regions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2818-2824. |
[7] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
[8] |
ZHANG Zhi-fen1, LIU Zi-min1, QIN Rui1, LI Geng1, WEN Guang-rui1, HE Wei-feng2. Real-Time Detection of Protective Coating Damage During Laser Shock Peening Based on ReliefF Feature Weight Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2437-2445. |
[9] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
[10] |
JING Yi-xuan1, WU Di2, LIU Gui-shan2*, HE Jian-guo2*, YANG Shi-hu2, MA Ping2, SUN Yuan-yuan2. Fusion of Near-Infrared Hyperspectral Imaging (NIR-HSI) and Texture Feature for Discrimination of Lingwu Long Jujube With Different Bruise Grades[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2644-2648. |
[11] |
WU Chao1, QIU Bo1*, PAN Zhi-ren1, LI Xiao-tong1, WANG Lin-qian1, CAO Guan-long1, KONG Xiao2. Application of Spectral and Metering Data Fusion Algorithm in Variable Star Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1869-1874. |
[12] |
DENG Xiao-jun1, 2, MA Jin-ge1, YANG Qiao-ling3, SHI Yi-yin1, HUO Yi-hui1, GU Shu-qing1, GUO De-hua1, DING Tao4, YU Yong-ai5, ZHANG Feng6. Visualized Fast Identification Method of Imported Olive Oil Quality Grade Based on Raman-UV-Visible Fusion Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1117-1125. |
[13] |
ZHONG Jing-jing1, 2, LIU Xiao1, 3, WANG Xue-ji1, 3, LIU Jia-cheng1, 3, LIU Hong1, 3, QI Chen1, 3, LIU Yu-yang1, 2, 3, YU Tao1, 3*. A Multidimensional Information Fusion Algorithm for Polarization
Spectrum Reconstruction Based on Nonsubsampled Contourlet
Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1254-1261. |
[14] |
LIU Yong1, ZHANG Jiang2, XIONG Cen-bo3*, DONG Yi3*, JI Wen-long4, XU Feng4, SHEN Jian2. Residual Life Prediction of Wet Clutch Based on Oil Spectrum Data
Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1314-1319. |
[15] |
FENG Xin1, 2, FANG Chao1*, GONG Hai-feng2, LOU Xi-cheng1, PENG Ye1. Infrared and Visible Image Fusion Based on Two-Scale Decomposition and
Saliency Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 590-596. |
|
|
|
|