Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat
WANG Jing1, 2, JING Yuan-shu1, HUANG Wen-jiang2*, ZHANG Jing-cheng3, ZHAO Juan1, ZHANG Qing2, WANG Li2
1. School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China 2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 3. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
Abstract:In order to improve the accuracy of wheat yellow rust disease severity using remote sensing and to find the optimum inversion model of wheat diseases, the canopy reflectance and disease index (DI) of winter wheat under different severity stripe rust were acquired. The three models of PLS (Partial Least Square), BP neural network using seven hyperspectral vegetation indices which have significant relationship with the occurrence of disease and vegetation index (PRI) were adopted to build a feasible regression model for detecting the disease severity. The results showed that PLS performed much better. The inversion accuracy of PLS method is best than of the VI (PRI, Photochemical Reflectance Index) and BP neural network models. The coefficients of determination (R2) of three methods to estimate disease severity between predicted and measured values are 0.936,0.918 and 0.767 respectively. Evaluation was made between the estimated DI and the measured DI, indicating that the model based on PLS is suitable for monitoring wheat disease. In addition, to explore the different contributions of diverse types of vegetation index to the models, the paper attempts to use NDVI, GNDVI and MSR which on behalf of vegetation greenness and NDWI and MSI that represents the moisture content to be input variables of PLS model. The results showed that, for the wheat yellow rust disease, changes in chlorophyll content is more sensitive to the disease severity than the changes in water content of the canopy . However, the accuracy of the two models are both lower than predicted when participating in all seven vegetation indices, namely using several species of vegetation indices tends to be more accurate than that using single category. It indicated that it has great potential for evaluating wheat disease severity by using hyper-spectral remote sensing.
Key words:Hyper-spectral;Yellow rust;Partial Least Square;BP neural network;Disease index
[1] LI Guang-bo, ZENG Shi-mai, LI Zhen-qi(李广博,曾士迈,李振歧). Integrated Management of Wheat Pests(小麦病虫草鼠害综合治理). Beijing: China Agricultural Science and Technology Press(北京: 中国农业科技出版社), 1989. 185. [2] Huang W J, Huang M Y, Liu L Y, et al. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(4): 97. [3] Devadas R, Lamb D W, Simpfendorfer S, et al. Precision Agriculture, 2009, 10(6): 459. [4] ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, et al(张竞成,袁 琳,王记华,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(20): 1. [5] HUANG Mu-yi, WANG Ji-hua, HUANG Wen-jiang(黄木易,王记华,黄文江). Journal of Anhui Agriculture University(安徽农业大学报), 2004, 31(1): 119. [6] YUAN Lin, ZHANG Jing-cheng, ZHAO Jin-ling, et al(袁 琳,张竞成,赵晋陵,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(6): 1608. [7] Nguyen Hung T, Lee Byun-Woo. European Journal of Agronomy, 2006, (24): 349. [8] Hansen P M, Schjoerring J K. Remote Sensing of Environment, 2003, (86): 542. [9] Clevers J G P W, Heijden G W A M Van der, Verzkov S, et al. The 9th International Symposium on Physical Measurements and Signatures in Remote Sensing. Beijing, 2005. 56. [10] JING Xia, HUANG Wen-jiang, JU Cun-yong, et al(竞 霞,黄文江,琚存勇,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 229. [11] Huang W J, Lamb D W, Niu Z, et al. Precision Agriculture, 2007, 8: 187. [12] LI Min, DENG Ji-zhong, YUAN Zhi-bao, et al(李 敏,邓继忠,袁之报, 等). Electronic Science and Technology(电子科技), 2011, 24(12): 10. [13] HU Xiao-ping, YANG Zhi-wei, LI Zhen-qi, et al(胡小平,杨之为,李振岐,等). Journal of Northwest Agriculture(西北农业学报), 2000, 9(3): 28.