综合多源遥感信息的水稻纹枯病区域生境适宜性评价方法

田洋洋; 吴开华; 李惠紫; 沈艳艳; 邱晗潇; 翟婧; 张竞成

doi:10.11834/jrs.20233219

浏览量 : 0 下载量: 105 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

综合多源遥感信息的水稻纹枯病区域生境适宜性评价方法
Method for evaluating habitat suitability of rice sheath blight at a regional scale based on multi-source remote sensing information
2023年页码：1-20
网络出版日期： 2023-11-03 ，
DOI： 10.11834/jrs.20233219

扫描看全文

田洋洋，吴开华，李惠紫，沈艳艳，邱晗潇，翟婧，张竞成.XXXX.综合多源遥感信息的水稻纹枯病区域生境适宜性评价方法.遥感学报，XX（XX）： 1-20

Tian Yangyang，Wu Kaihua，Li Huizi，Shen Yanyan，Qiu Hanxiao，Zhai Jing，Zhang Jingcheng. XXXX. Method for evaluating habitat suitability of rice sheath blight at a regional scale based on multi-source remote sensing information. National Remote Sensing Bulletin， XX（XX）：1-20
田洋洋，吴开华，李惠紫，沈艳艳，邱晗潇，翟婧，张竞成.XXXX.综合多源遥感信息的水稻纹枯病区域生境适宜性评价方法.遥感学报，XX（XX）： 1-20 DOI： 10.11834/jrs.20233219.

Tian Yangyang，Wu Kaihua，Li Huizi，Shen Yanyan，Qiu Hanxiao，Zhai Jing，Zhang Jingcheng. XXXX. Method for evaluating habitat suitability of rice sheath blight at a regional scale based on multi-source remote sensing information. National Remote Sensing Bulletin， XX（XX）：1-20 DOI： 10.11834/jrs.20233219.

摘要

作物病害的发生与其生境的适宜性关系密切，目前病害生境监测和评价通常较为粗放，主要依靠气象信息，对田块间作物生长状态和环境条件等空间异质性因素缺乏精细的描述，难以为病害的精准预测提供有效信息。本研究以水稻中发生面积较大的主要病害水稻纹枯病为研究对象，通过在县域尺度开展多年份病害等级调查获得建模及验证样本，综合运用光学、微波及热红外等多源遥感数据对病害关键生境因素进行监测，关键生境因素主要包括寄主生长状态、稻田水层状态及稻田地表温度生境因素，并在此基础上，结合空间网格化分析和偏最小二乘（PLS）回归方法建立了病害生境适宜性评价模型。结果表明，利用遥感信息能够有效表征病害相关生境因素，同时，基于多源遥感表征的生境因素建立的水稻纹枯病区域生境适宜性评价模型能够得到与实际病害调查空间分布趋势基本一致的结果，模型对病情等级的拟合优度R

为0.60-0.65，RMSE分别为0.72、0.56。基于多源遥感信息能够以空间连续的方式对寄主状态、环境条件等重要病害生境因素进行表征，及实现病害生境适宜性的有效评价，为病害预测、防控提供重要的参考和指导信息。

Abstract

Crop diseases have a severe impact on food security

and the excessive use of pesticides in crop disease prevention and control is a common issue. The evaluation of disease habitat suitability is able to provide important information for disease forecasting and control. The occurrence of crop diseases is closely associated with factors such as the growth status of host and environmental conditions. While the disease habitat conditions vary significantly due to cultivation practices and microclimate variations in the field. At present

disease habitat monitoring and evaluation are generally coarse

mainly relying on meteorological information and lacking detailed descriptions of spatially heterogeneous factors such as crop growth status and environmental conditions among fields. In this study

rice sheath blight (RSB)

a major disease widespread in rice cultivation

was selected as the research object

the disease surveys were conducted at a county level in 2018 and 2019. Multi-source remote sensing data including optical

microwave and thermal infrared images were used for monitoring the key disease habitat factors. Multi-temporal Sentinel-2 optical images were utilized to extract the planting area of the host crop

which solved the problem of confusing the host with other vegetation in single phase images; the growth status of host was indicated by the tasseled cap products of Sentinel-2 optical images; the status of water layer in rice field was extracted by combining Sentinel-1 microwave images and Sentinel-2 optical images

the optical image of rice region was segmented by object-oriented analysis method to obtain the rice plot boundary in order to eliminate the noise of microwave image; and the MODIS land surface temperature (LST) products were utilized to reflect evapotranspiration and respiration status of rice plants. Based on these remote sensing habitat features of the RSB and a spatial gridding analysis

the habitat suitability evaluation model was established using the partial least squares (PLS) regression method.By validating against the disease survey data

the results showed that the remote sensing information can effectively characterize the disease habitat features. The R

of the habitat suitability evaluation model was 0.60-0.65

and the RMSE was 0.72 and 0.56

respectively

and the output of the model was in good agreement with the actual spatial pattern of the disease. Besides

the hot and cold spots of the disease habitat suitability map were highly consistent with the actual pattern of disease occurrence in the region. Moreover

the rate of habitat suitability under each disease grade was also analyzed

and the results further confirmed the rationality of the evaluation. Therefore

this study demonstrates the feasibility of utilizing multi-source remote sensing data in evaluating the disease habitat suitability. The disease habitat evaluation map can be integrated into some disease epidemic models to develop spatio-temporal dynamic disease forecasting models at regional scale

and multi-source data

such as meteorological data

remote sensing data

and ground sensor networks

can be incorporated to establish a more comprehensive habitat suitability evaluation model

which is expected to be beneficial for large-scale disease control.

关键词

水稻纹枯病生境遥感信息空间网格化评价模型

Keywords

rice sheath blighthabitatremote sensing informationspatial griddingevaluation model

references

Bennett F G A. 1984. Resistance to powdery mildew in wheat: a review of its use in agriculture and breeding programmes. Plant pathology, 33(3): 279-300 [DOI: 10.1111/j.1365-3059.1984.tb01324.xhttp://dx.doi.org/10.1111/j.1365-3059.1984.tb01324.x]

Cao Y. 2021. Spatial-temporal characteristics of surface deformation and soil water in coal mining area of Changhe River basin in the Loess Plateau based on microwave remote sensing. Shanxi agricultural university (曹毅. 2021. 基于微波遥感的黄土高原长河流域采煤区地表形变与土壤水时空特征.山西农业大学) [DOI:10.27285/d.cnki.gsxnu. 2021.000815]

Castilla N P, Leaño R M, Elazhour F A and Teng Paul P S. 1996. Effects of Plant Contact, Inoculation Pattern, Leaf Wetness Regime, and Nitrogen Supply on Inoculum Efficiency in Rice Sheath Blight. Journal of Phytopathology, 144(4):187-192 [DOI: 10.1111/j.1439-0434.1996.tb01512.xhttp://dx.doi.org/10.1111/j.1439-0434.1996.tb01512.x]

Chen Zhongxin, Ren Jianqiang, Tang Huajun, Shi Yun, Leng Pei, Liu Jia, Wang Limin, Wu Wenbin, Yao Yanmin, Hastuya. 2016. Progress and prospect of agricultural remote sensing research and application. Journal of Remote Sensing, 20(05):748-767

陈仲新, 任建强, 唐华俊, 史云, 冷佩, 刘佳, 王利民, 吴文斌, 姚艳敏, 哈斯图亚. 2016. 农业遥感研究应用进展与展望. 遥感学报, 20(05):748-767 [DOI： 10.11834/jrs.20166214http://dx.doi.org/10.11834/jrs.20166214]

Clevers J G P W and Gitelson A A. 2013. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. International Journal of Applied Earth Observation and Geoinformation, 23:344-351 [DOI: 10.1016/j.jag.2012.10.008http://dx.doi.org/10.1016/j.jag.2012.10.008]

Gbogbo A Y, Kouakou B K, Dabo-Niang S and Zoueu J T. 2022. Predictive model for airborne insect abundance intercepted by a continuous wave Scheimpflug lidar in relation to meteorological parameters. Ecological Informatics, 68, 101528 [DOI: 10.1016/j.ecoinf.2021.101528http://dx.doi.org/10.1016/j.ecoinf.2021.101528]

Gilligan C A. 2008. Sustainable agriculture and plant diseases: an epidemiological perspective. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 363(1492): 741–759 [DOI:10.1098/rstb.2007.2181http://dx.doi.org/10.1098/rstb.2007.2181]

Hu Gensheng, WU Qantian, Luo Juhua, Huang Wenjiang, Liang Dong and Huang Linsheng. 2017. Remote sensing monitoring of wheat aphids by combining HJ satellite image and least square twin support vector machine. Journal of Zhejiang University (Agricultural and Life Sciences Edition), 43(2):211-219.

胡根生, 吴问天, 罗菊花, 黄文江, 梁栋和黄林生. 2017. 结合HJ卫星影像和最小二乘孪生支持向量机的小麦蚜虫遥感监测. 浙江大学学报（农业与生命科学版）, 43(2):211-219 [DOI: 10.3785/j.issn.1008-9209.2016.08.021http://dx.doi.org/10.3785/j.issn.1008-9209.2016.08.021]

Huang W J, Zhang J C, Luo J H, Zhao J L, Huang L S and Zhou X F. 2015. Remote sensing monitoring and prediction of crop diseases and pests. Beijing: Science Press

黄文江,张竞成,罗菊花, 赵晋陵，黄林生，周贤锋. 2015. 作物病虫害遥感监测与预测. 北京: 科学出版社

Isabelle Merle, Philippe Tixier, Elías de Melo Virginio Filho, Christian Cilas and Jacques Avelino. 2020. Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica. Crop Protection 130:105046 [DOI: 10.1016/j.cropro.2019.105046http://dx.doi.org/10.1016/j.cropro.2019.105046]

Lai C Y and Yuan G Q. 2008. Agricultural plant pathology. Beijing: Science Press

赖传雅, 袁高庆. 2008. 农业植物病理学. 北京：科学出版社

Li H, Song X P, Hansen M C, Becker-Reshef I, Adusei B, Pickering J, Wang L, Wang L, Lin Z, Zalles V, Potapov P, Stehman S V and Justice C. 2023. Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation. Remote Sensing of Environment, 294, 113623 [DOI: 10.1016/j.rse.2023.113623http://dx.doi.org/10.1016/j.rse.2023.113623]

Li P, Ding L, Zhang J C and Mu J W. 2016. Effects of different cultivation and management measures on rice sheath blight. Modernizing Agriculture, 11:1-2

李鹏, 丁亮, 张金成, 穆娟微. 2016. 不同栽培管理措施对水稻纹枯病的影响. 现代化农业, 11:1-2 [DOI: 10.3969/j.issn.1001-0254.2016.11.001http://dx.doi.org/10.3969/j.issn.1001-0254.2016.11.001]

Li Peilin. 2017. Symptoms of transmission and prevention measures of rice sheath blight. Journal of agriculture and technology, 37(1):25-26

李沛霖. 2017. 水稻纹枯病的症状传播途径及防治措施. 农业与技术, 37(1):25-26 [DOI: 10.11974 / nyyjs. 20170132010http://dx.doi.org/10.11974/nyyjs.20170132010]

Liu W C, Liu Z D, Huang C, Lu M H, Liu J and Yang Q P. 2016. Statistics and analysis of crop yield losses caused by main diseases and insect pests in recent 10 years. Plant Protection, 42(005):1-9

刘万才, 刘振东, 黄冲, 陆明红, 刘杰, 杨清坡. 2016. 近10年农作物主要病虫害发生危害情况的统计和分析. 植物保护, 42(005):1-9 [DOI:10.3969/j.issn.0529-1542.2016.05.001http://dx.doi.org/10.3969/j.issn.0529-1542.2016.05.001]

Liu Y Y, Liu X Y, Zhang B and Li M Y. 2020. Spatial characteristics analysis of water conservation function in hilly region of Loess Plateau based on InVEST model. Acta Ecologica Sinica, 40(17):6161-6170

刘宥延，刘兴元，张博，李妙莹. 2020. 基于InVEST模型的黄土高原丘陵区水源涵养功能空间特征分析. 生态学报, 40(17):6161-6170 [DOI: 10.5846/stxb201910102108http://dx.doi.org/10.5846/stxb201910102108]

Marques d S J R, Damásio C V, Sousa A M O, Bugalho L, Pessanha L and Quaresma P. 2015. Agriculture pest and disease risk maps considering msg satellite data and land surface temperature. International Journal of Applied Earth Observation and Geoinformation, 38, 40-50 [DOI: 10.1016/j.jag.2014.12.016http://dx.doi.org/10.1016/j.jag.2014.12.016]

Nedkov R. 2017. Orthogonal transformation of segmented images from the satellite sentinel-2. Comptes rendus de l'Académie bulgare des sciences: sciences mathématiques et naturelles, 70(5):687-691

Niu Z, Li J H, Gao Z H, Gong E D, Zhang S M, Zhang J, Liu S, Ou Y X Y and Zhang R. 2018. Ecological environment monitoring for sustainable development goals in the Belt and Road region. Journal of Remote Sensing, 22(4): 680–685

牛铮, 李加洪, 高志海, 宫阿都, 张松梅, 张景, 刘爽, 欧阳晓莹, 张瑞. 2018. 《全球生态环境遥感监测年度报告》进展与展望. 遥感学报, 22(4): 680–685 [DOI： 10.11834/jrs.20188060http://dx.doi.org/10.11834/jrs.20188060]

Peng S Q, Zeng Z R and Zhang Z G. 1986. Rice sheath blit and its control. Shanghai: Shanghai Science and Technology Press

彭绍裘, 曾昭瑞, 张志光. 1986. 水稻纹枯病及其防治. 上海: 上海科学技术出版社

Remigio A Guzman-Plazola, R Michael Davis and James J Marois. 2003. Effects of relative humidity and high temperature on spore germination and development of tomato powdery mildew (Leveillula taurica). Crop Protection 22(10). 1157-1168 [DOI: 10.1016/S0261-2194(03)00157-1http://dx.doi.org/10.1016/S0261-2194(03)00157-1]

Shen C Y. 2009. Plant Pathology (5th Ed.). Beijing: China Agricultural University Press

沈崇尧. 2009. 植物病理学（第五版）. 北京: 中国农业大学出版社

Strand J F. 2000. Some agrometeorological aspects of pest and disease management for the 21st century. Agricultural and Forest Meteorology, 103(1/2): 73–82 [DOI:10.1016/S0168-1923(00)00119-2http://dx.doi.org/10.1016/S0168-1923(00)00119-2]

Sun Y Y. 2018. Soil water collaborative inversion based on active and passive microwave remote sensing in C and L bands. China Research Institute of Water Resources and Hydropower

孙亚勇. 2018 基于C和L波段主被动微波遥感的土壤水分协同反演研究. 中国水利水电科学研究院

Tang Q L.2023. Optical/microwave collaborative remote sensing retrieval of vegetation and soil ecological water in Hongyuan County, West Sichuan Plateau. Chengdu University of Technology(唐巧林. 2023. 川西高原红原县光学/微波协同植被土壤生态水遥感反演. 成都理工大学) [DOI:10.26986/d.cnki.gcdlc.2021.000727http://dx.doi.org/10.26986/d.cnki.gcdlc.2021.000727]

Tian Y Y. 2021. Habitat suitability evaluation of rice sheath blight based on multi-source satellite remote sensing data. Hangzhou Dianzi University

田洋洋. 2021. 基于多源卫星遥感数据的水稻纹枯病生境适宜性评价研究. 杭州电子科技大学 [DOI:10.27075/d.cnki.ghzdc.2021.000747http://dx.doi.org/10.27075/d.cnki.ghzdc.2021.000747]

Topp G C, Davis J L, and Annan A P. 1980. Electromagnetic determination of soil water content: Measurements in coaxial transmission lines. Water Resources Research, 16(3):574-582. [DOI:10.1029/WR016i003p00574http://dx.doi.org/10.1029/WR016i003p00574]

Wang R. 2018. Research on key technologies of remote sensing urban land monitoring at regional scale. China University of Geosciences

王润. 2018. 区域尺度城市土地遥感监测关键技术研究. 中国地质大学

Wang Y, Fang S, Zhao L, Huang X and Jiang X. 2022. Parcel-based summer maize mapping and phenology estimation combined using Sentinel-2 and time series Sentinel-1 data. International Journal of Applied Earth Observation and Geoinformation, 108, 102720 [DOI:/10.1016/j.jag.2022.102720http://dx.doi.org//10.1016/j.jag.2022.102720]

Wu J G. 2007. Landscape Ecology: Pattern, Process, Scale and Hierarchy. Beijing: Higher Education Press

邬建国. 2007. 景观生态学：格局、过程、尺度、与等级. 北京: 高等教育出版社

Xu X, Ji X, Jiang J, Yao X, Tian Y, Zhu Y, Cao W and Cheng T. 2018. Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery. Remote Sensing, 10(4):546 [DOI: 10.3390/rs10040546http://dx.doi.org/10.3390/rs10040546]

Yang Z, Shao Y, Li K, Liu Q, Liu L and Brisco B. 2017. An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data. Remote Sensing of Environment, 195:184-201 [DOI: 10.1016/j.rse.2017.04.016http://dx.doi.org/10.1016/j.rse.2017.04.016]

Yuan L, Bao Z, Zhang H, Zhang Y and Liang X. 2017. Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery. Optik - International Journal for Light and Electron Optics, 145: 66-73 [DOI: 10.1016/j.ijleo.2017.06.071http://dx.doi.org/10.1016/j.ijleo.2017.06.071]

Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K and Huang W. 2019. Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture, 165:104943 [DOI: 10.1016/j.compag.2019.104943http://dx.doi.org/10.1016/j.compag.2019.104943]

Zhang J, Pu R, Yuan L, Wang J, Huang W and Yang G. 2014. Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi- Temporal Satellite Imagery. PloS one, 9(4): e93107 [DOI: 10.1371/journal.pone. 0093107http://dx.doi.org/10.1371/journal.pone.0093107]

Zhang T H. 2019. Epidemic causes and comprehensive prevention and control measures of rice sheath blight. Rural Economics and Technology, 472(20): 27-28

张涛宏. 2019. 水稻纹枯病流行原因及综合防控对策探讨. 农村经济与科技, 472(20): 27-28 [DOI: 10.3969/j.issn.1007-7103.2019.20.013http://dx.doi.org/10.3969/j.issn.1007-7103.2019.20.013]

Zhao J, Du S and Huang L. 2022 Monitoring wheat powdery mildew (Blumeria graminis f. sp. tritici) using multisource and multitemporal satellite images and support vector machine classifier. Smart Agriculture, 4(1): 17-28 [DOI: 10.12133/j.smartag.SA202202009http://dx.doi.org/10.12133/j.smartag.SA202202009]

文章被引用时，请邮件提醒。

提交

浅论中国亚热带遥感现状、任务与创新发展途径

基于分形的海岸线遥感信息空间尺度研究

基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用