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基于多源遥感信息的作物病虫害生境评价研究进展

沈艳艳 张竞成 沈栋 田洋洋 黄文江 杨小冬

沈艳艳, 张竞成, 沈栋, 田洋洋, 黄文江, 杨小冬. 基于多源遥感信息的作物病虫害生境评价研究进展[J]. 中国生态农业学报 (中英文), 2023, 31(7): 1012−1025 doi: 10.12357/cjea.20220930
引用本文: 沈艳艳, 张竞成, 沈栋, 田洋洋, 黄文江, 杨小冬. 基于多源遥感信息的作物病虫害生境评价研究进展[J]. 中国生态农业学报 (中英文), 2023, 31(7): 1012−1025 doi: 10.12357/cjea.20220930
SHEN Y Y, ZHANG J C, SHEN D, TIAN Y Y, HUANG W J, YANG X D. Research progress on habitat suitability assessment of crop diseases and pests by multi-source remote sensing information[J]. Chinese Journal of Eco-Agriculture, 2023, 31(7): 1012−1025 doi: 10.12357/cjea.20220930
Citation: SHEN Y Y, ZHANG J C, SHEN D, TIAN Y Y, HUANG W J, YANG X D. Research progress on habitat suitability assessment of crop diseases and pests by multi-source remote sensing information[J]. Chinese Journal of Eco-Agriculture, 2023, 31(7): 1012−1025 doi: 10.12357/cjea.20220930

基于多源遥感信息的作物病虫害生境评价研究进展

doi: 10.12357/cjea.20220930
基金项目: 国家自然科学基金项目(42071420)、宁波市重大科技任务攻关项目(科技创新2025重大专项, 2021Z048)和国家重点研发计划项目(2022YFD2000100)资助
详细信息
    作者简介:

    沈艳艳, 主要研究方向为农业遥感及病虫害监测方法。E-mail: shenyanyan@hdu.edu.cn

    通讯作者:

    张竞成, 主要研究方向为农业遥感、病虫害监测方法及病虫害预警模型。E-mail: zhangjcrs@hdu.edu.cn

  • 中图分类号: S435

Research progress on habitat suitability assessment of crop diseases and pests by multi-source remote sensing information

Funds: This study was supported by the National Natural Science Foundation of China (42071420), the Major Scientific and Technological Task Project in Ningbo City (Major Special Project for 2025 Scientific and Technological Innovation, 2021Z048), and the National Key R&D Program of China (2022YFD2000100).
More Information
  • 摘要: 作物病虫害严重影响粮食产量和质量, 对农业生产造成巨大损失。开展作物病虫害生境适宜性评价能够对适合病虫害繁殖和流行的环境区域进行有效表征, 为病虫害预测提供重要信息。由于作物病虫害发生和流行受多种生境因素影响, 同时这些生境因素时空异质性高, 难以通过传统气象站点数据、人为调查等方式进行有效表征, 为病虫害生境评价带来较大的挑战。遥感技术的发展和成熟为病虫害生境信息表征带来重要机遇。多源遥感信息在时空异质信息表征方面具有天然优势, 同时能与传统气象站点数据形成信息互补, 为病虫害生境适宜性评价提供全面、丰富的信息, 支持生境适宜性评价模型的构建。本文对多源遥感信息在作物病虫害生境适宜性评价方面的研究进展进行综述, 重点分析多源遥感数据在寄主作物分布及生长状态、环境气象条件和景观等病虫害生境因子表征方面的潜力, 以及大范围生境适宜性评价涉及的统计模型、机器学习模型和生态位模型等建模方法。在此基础上, 提出基于多源遥感信息的作物病虫害生境评价模型构建的框架, 并对技术的发展趋势进行探讨, 为更加精准、科学的区域尺度病虫害防控管理提供技术支撑, 为病虫害统防统治和绿色防控提供科学指导。
  • 图  1  基于多源遥感信息的作物病虫害生境因素表征

    Figure  1.  Habitat factors characterization of crop diseases and pests based on multi-source remote sensing information

    图  2  综合多源遥感信息的作物病虫害生境评价方法框架

    Figure  2.  Methodological framework for habitat assessment of crop diseases and pests based on multi-source remote sensing information

    表  1  多源遥感数据在生境因素表征中的应用示例

    Table  1.   Examples of application of multi-source remote sensing data in habitat factors characterization

    生境监测因素
    Habitat monitoring factor
    卫星遥感数据
    Satellite remote sensing data
    监测对象
    Monitoring object
    参考文献
    References
    寄主因素
    Host factor
    寄主分布
    Host distribution
    Landsat 8水稻、玉米
    Oryza sativa, Zea mays
    [29-30]
    MODIS, Sentinel-1/2水稻 Oryza sativa[31-32]
    物候期
    Phenological phase
    HJ-1A/B冬小麦 Triticum aestivum[10]
    Sentinel-1/2玉米 Zea mays[33]
    MODIS小麦、水稻、玉米
    Triticum aestivum,
    Oryza sativa,
    Zea mays
    [34]
    叶面积指数反演
    Leaf area index inversion
    Landsat 5/7玉米 Zea mays[35]
    Sentinel-2小麦 Triticum aestivum[36]
    氮素、水分监测
    Nitrogen and water monitoring
    Sentinel-2玉米 Zea mays[37]
    MODIS, Landsat 8玉米 Zea mays[38-39]
    气象因素
    Meteorological factor
    气温 TemperatureMODIS[40-41]
    降水 PrecipitationTRMM[42-43]
    景观因素
    Landscape factor
    农田聚集度等景观指标
    Farmland aggregation and other landscape indicator
    HJ-CCD小麦 Triticum aestivum[44]
    MODIS水稻 Oryza sativa[45]
    Landsat-TM/ETM小麦 Triticum aestivum[46]
    土壤因素
    Edaphic factor
    土壤温度
    Soil temperature
    ASTER小麦、玉米
    Triticum aestivum, Zea mays
    [47]
    土壤湿度
    Soil humidity
    RadarSat, ERS-2褐飞蝗 Locustana pardalina[48-49]
    土壤盐度
    Soil salinity
    ASTER东亚飞蝗
    Locusta migratoria manilensis
    [50]
    下载: 导出CSV

    表  2  作物病虫害生境适宜性评价方法

    Table  2.   Methods for habitat suitability assessment of crop diseases and pests

    建模方法
    Modeling method
    作用对象
    Action object
    精度
    Accuracy
    参考文献
    References
    统计类模型
    Statistical model
    Logistic冬小麦白粉病 Blumeria graminisOA=78%[92]
    GLM小麦蚜虫 Macrosiphum avenaeOA=52%[46]
    FLDA春小麦白粉病 Blumeria graminisOA=82%[93]
    春小麦蚜虫 Macrosiphum avenaeOA=82%[93]
    PLSR水稻纹枯病 Rhizoctonia solaniR2=0.68[98]
    机器学习模型
    Machine learning model
    LMRF东亚飞蝗 Locusta migratoria manilensisU>0.76[99]
    CART小麦白粉病 Blumeria graminisOA=73.1%[18]
    SVM沙漠蝗虫 Schistocerca gregariaOA=77.46%[100]
    SMOTE-BPNN小麦条锈病 Puccinia striiformisOA=86.7%[18]
    KNN小麦白粉病 Blumeria graminisOA=84.6%[18]
    生态位模型
    Ecological niche modeling
    GARP松材线虫 Bursaphelenchus xylophilusAUC =0.89[101]
    ENFA白背飞虱 Sogatella furciferaAUC=0.922[102]
    MaxEnt草地贪夜蛾 Spodoptera frugiperdaAUC=0.912[103]
    小麦麦瘟病 Magnaporthe oryzaeAUC>0.99[104]
      OA: 总精度; R2: 相关系数; U: 分类精度; AUC: 运行曲线下面积。OA: overall accuracy; R2: correlation coefficient; U: classification accuracy; AUC: area under receiver operation curve.
    下载: 导出CSV
  • [1] STRANGE R N, SCOTT P R. Plant disease: a threat to global food security[J]. Annual Review of Phytopathology, 2005, 43: 83−116 doi: 10.1146/annurev.phyto.43.113004.133839
    [2] SINGH V K, SINGH R, KUMAR A, et al. Current status of plant diseases and food security[M]//DROBY S, KUMAR A. Food Security and Plant Disease Management. Woodhead: Elsevier, 2020
    [3] DEUTSCH C A, TEWKSBURY J J, TIGCHELAAR M, et al. Increase in crop losses to insect pests in a warming climate[J]. Science, 2018, 361(6405): 916−919 doi: 10.1126/science.aat3466
    [4] 黄文江, 张竞成, 罗菊花. 作物病虫害遥感监测与预测[M]. 北京: 科学出版社, 2015

    HUANG W J, ZHANG J C, LUO J H. Remote Sensing Monitoring and Prediction of Crop Diseases and Insect Pests[M]. Beijing: Science Press, 2015
    [5] MIZUBUTI E S, AYLOR D E, FRY W E. Survival of Phytophthora infestans sporangia exposed to solar radiation[J]. Phytopathology, 2000, 90(1): 78−84 doi: 10.1094/PHYTO.2000.90.1.78
    [6] GUZMAN-PLAZOLA R A, DAVIS R M, MAROIS J J. Effects of relative humidity and high temperature on spore germination and development of tomato powdery mildew (Leveillula taurica)[J]. Crop Protection, 2003, 22(10): 1157−1168 doi: 10.1016/S0261-2194(03)00157-1
    [7] XU X M, RIDOUT M S. Effects of prevailing wind direction on spatial statistics of plant disease epidemics[J]. Journal of Phytopathology, 2001, 149(3/4): 155−166
    [8] MERLE I, TIXIER P, FILHO E M V, et al. Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica[J]. Crop Protection, 2020, 130: 105046 doi: 10.1016/j.cropro.2019.105046
    [9] 黄青, 王利民, 滕飞. 利用MODIS-NDVI数据提取新疆棉花播种面积信息及长势监测方法研究[J]. 干旱地区农业研究, 2011, 29(2): 213−217

    HUANG Q, WANG L M, TENG F. MODIS-NDVI-based monitoring of cotton planting areas and growth condition in Xinjiang[J]. Agricultural Research in the Arid Areas, 2011, 29(2): 213−217
    [10] 蒙继华, 吴炳方, 杜鑫, 等. 基于HJ-1A/1B数据的冬小麦成熟期遥感预测[J]. 农业工程学报, 2011, 27(3): 225−230

    MENG J H, WU B F, DU X, et al. Predicting mature date of winter wheat with HJ-1A/1B data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(3): 225−230
    [11] 陈颖姝, 张晓春, 王修贵, 等. 基于Landsat8 OLI与MODIS数据的洪涝季节作物种植结构提取[J]. 农业工程学报, 2014, 30(21): 165−173

    CHEN Y S, ZHANG X C, WANG X G, et al. Extraction of crop planting structure in seasons prone to waterlogging using Landsat8 OLI and MODIS data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(21): 165−173
    [12] ZHENG Q, HUANG W J, CUI X M, et al. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery[J]. Sensors (Basel, Switzerland), 2018, 18(3): 868 doi: 10.3390/s18030868
    [13] GAO H, WANG C C, WANG G Y, et al. A crop classification method integrating GF-3 PolSAR and sentinel-2A optical data in the Dongting Lake Basin[J]. Sensors (Basel, Switzerland), 2018, 18(9): 3139 doi: 10.3390/s18093139
    [14] MERCIER A, BETBEDER J, DENIZE J. Estimating crop parameters using Sentinel-1 and 2 datasets and geospatial field data[J]. Data in Brief, 2021, 38: 107408 doi: 10.1016/j.dib.2021.107408
    [15] KHUSH G S. Disease and insect resistance in rice[M]//SPARKS D L. Advances in Agronomy. Amsterdam: Elsevier, 1977: 265–341
    [16] BENNETT F G A. Resistance to powdery mildew in wheat: a review of its use in agriculture and breeding programmes[J]. Plant Pathology, 1984, 33(3): 279−300 doi: 10.1111/j.1365-3059.1984.tb01324.x
    [17] 刘占宇. 水稻主要病虫害胁迫遥感监测研究[D]. 杭州: 浙江大学, 2008

    LIU Z Y. Monitoring the rice disease and insect stress with remote sensing[D]. Hangzhou: Zhejiang University, 2008
    [18] 马慧琴. 基于多源多时相遥感分析的小麦主要病害动态监测[D]. 南京: 南京信息工程大学, 2020

    MA H Q. Dynamic monitoring of major wheat diseases based on multi-source and multi-temporal remote sensing analysis[D]. Nanjing: Nanjing University of Information Science & Technology, 2020
    [19] George N. Agrios. 植物病理学[M]. 北京: 中国农业大学出版社, 2009

    AGRIOS G N. Plant Pathology[M]. Beijing: China Agricultural University Press, 2009
    [20] 唐翠翠. 基于多源遥感数据的小麦病虫害大尺度监测预测研究[D]. 合肥: 安徽大学, 2016

    TANG C C. Large scale monitoring and forecasting of wheat diseases and pests based on multi-source remote sensing data[D]. Hefei: Anhui University, 2016
    [21] STRAND J F. Some agrometeorological aspects of pest and disease management for the 21st century[J]. Agricultural and Forest Meteorology, 2000, 103(1/2): 73−82
    [22] BRENNAN J M, EGAN D, COOKE B M, et al. Effect of temperature on head blight of wheat caused by Fusarium culmorum and F. graminearum[J]. Plant Pathology, 2005, 54(2): 156−160 doi: 10.1111/j.1365-3059.2005.01157.x
    [23] CRUZ D R, LEANDRO L F S, MUNKVOLD G P. Effects of temperature and pH on Fusarium oxysporum and soybean seedling disease[J]. Plant Disease, 2019, 103(12): 3234−3243 doi: 10.1094/PDIS-11-18-1952-RE
    [24] MEENTEMEYER R K, HAAS S E, VÁCLAVÍK T. Landscape epidemiology of emerging infectious diseases in natural and human-altered ecosystems[J]. Annual Review of Phytopathology, 2012, 50: 379−402 doi: 10.1146/annurev-phyto-081211-172938
    [25] GILLIGAN C A. Sustainable agriculture and plant diseases: an epidemiological perspective[J]. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, 2008, 363(1492): 741−759 doi: 10.1098/rstb.2007.2181
    [26] PARNELL S, GOTTWALD T R, GILLIGAN C A, et al. The effect of landscape pattern on the optimal eradication zone of an invading epidemic[J]. Phytopathology, 2010, 100(7): 638−644 doi: 10.1094/PHYTO-100-7-0638
    [27] YUEN J, MILA A. Landscape-scale disease risk quantification and prediction[J]. Annual Review of Phytopathology, 2015, 53: 471−484 doi: 10.1146/annurev-phyto-080614-120406
    [28] QI X L, WANG X H, XU H F, et al. Influence of soil moisture on egg cold hardiness in the migratory locust Locusta migratoria (Orthoptera: Acridiidae)[J]. Physiological Entomology, 2007, 32(3): 219−224 doi: 10.1111/j.1365-3032.2007.00564.x
    [29] XU X J, JI X S, JIANG J L, et al. Evaluation of one-class support vector classification for mapping the paddy rice planting area in Jiangsu Province of China from landsat 8 OLI imagery[J]. Remote Sensing, 2018, 10(4): 546 doi: 10.3390/rs10040546
    [30] YANG L B, WANG L M, HUANG J F, et al. Monitoring policy-driven crop area adjustments in Northeast China using landsat-8 imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 82: 101892 doi: 10.1016/j.jag.2019.06.002
    [31] RAMADHANI F, PULLANAGARI R, KERESZTURI G, et al. Automatic mapping of rice growth stages using the integration of SENTINEL-2, MOD13Q1, and SENTINEL-1[J]. Remote Sensing, 2020, 12(21): 3613 doi: 10.3390/rs12213613
    [32] ZHAN P, ZHU W Q, LI N. An automated rice mapping method based on flooding signals in synthetic aperture radar time series[J]. Remote Sensing of Environment, 2021, 252: 112112 doi: 10.1016/j.rse.2020.112112
    [33] WANG Y Y, FANG S H, ZHAO L L, et al. Parcel-based summer maize mapping and phenology estimation combined using sentinel-2 and time series Sentinel-1 data[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 108: 102720 doi: 10.1016/j.jag.2022.102720
    [34] LUO Y C, ZHANG Z, CHEN Y, et al. ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000−2015 based on leaf area index (LAI) products[J]. Earth System Science Data, 2020, 12(1): 197−214 doi: 10.5194/essd-12-197-2020
    [35] GONZÁLEZ-SANPEDRO M C, LE-TOAN T, MORENO J, et al. Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data[J]. Remote Sensing of Environment, 2008, 112(3): 810−824 doi: 10.1016/j.rse.2007.06.018
    [36] HERRMANN I, PIMSTEIN A, KARNIELI A, et al. LAI assessment of wheat and potato crops by VENμS and sentinel-2 bands[J]. Remote Sensing of Environment, 2011, 115(8): 2141−2151 doi: 10.1016/j.rse.2011.04.018
    [37] CLEVERS J G, GITELSON A A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and -3[J]. International Journal of Applied Earth Observation and Geoinformation, 2013, 23: 344−351 doi: 10.1016/j.jag.2012.10.008
    [38] SAYAGO S, OVANDO G, BOCCO M. Landsat images and crop model for evaluating water stress of rainfed soybean[J]. Remote Sensing of Environment, 2017, 198: 30−39 doi: 10.1016/j.rse.2017.05.008
    [39] XU C Y, QU J J, HAO X J, et al. Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa[J]. Agricultural Water Management, 2020, 227: 105844 doi: 10.1016/j.agwat.2019.105844
    [40] HUANG R, ZHANG C, HUANG J X, et al. Mapping of daily mean air temperature in agricultural regions using daytime and nighttime land surface temperatures derived from TERRA and AQUA MODIS data[J]. Remote Sensing, 2015, 7(7): 8728−8756 doi: 10.3390/rs70708728
    [41] YANG Y, CAI W, YANG J. Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in northeast China[J]. Remote Sensing, 2017, 9(5): 410 doi: 10.3390/rs9050410
    [42] 刘小婵, 赵建军, 张洪岩, 等. TRMM降水数据在东北地区的精度验证与应用[J]. 自然资源学报, 2015, 30(6): 1047−1056

    LIU X C, ZHAO J J, ZHANG H Y, et al. Accuracy validation and application of TRMM precipitation data in northeast China[J]. Journal of Natural Resources, 2015, 30(6): 1047−1056
    [43] CHEN Y Y, HUANG J F, SHENG S X, et al. A new downscaling-integration framework for high-resolution monthly precipitation estimates: combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data[J]. Remote Sensing of Environment, 2018, 214: 154−172 doi: 10.1016/j.rse.2018.05.021
    [44] 张竞成. 多源遥感数据小麦病害信息提取方法研究[D]. 杭州: 浙江大学, 2012

    ZHANG J C. Methods for information extraction of wheat disease based on multi-source remote sensing data[D]. Hangzhou: Zhejiang University, 2012
    [45] 张静文. 基于多源时空信息的水稻病害区域流行预测研究[D]. 杭州: 杭州电子科技大学, 2022

    ZHANG J W. Regional forcasting of rice disease prevalence based on multi-source spatio-temporal information[D]. Hangzhou: Hangzhou Dianzi University, 2022
    [46] 张永生, 欧阳芳, 门兴元, 等. 区域农田景观格局对麦蚜种群数量的影响[J]. 生态学报, 2018, 38(23): 8652−8659

    ZHANG Y S, OUYANG F, MEN X Y, et al. Effects of regional agricultural landscape patterns on populations of wheat aphids[J]. Acta Ecologica Sinica, 2018, 38(23): 8652−8659
    [47] 刘振华, 赵英时. 基于遗传算法的不同光照条件下植被和土壤组分温度反演[J]. 农业工程学报, 2012, 28(1): 161−166, 294

    LIU Z H, ZHAO Y S. Retrieval of plant and soil component temperature under different light conditions based on genetic algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(1): 161−166, 294
    [48] CROOKS W T, ARCHER D J. SAR observations of dryland moisture — towards monitoring outbreak areas of the brown locust in South Africa[C]//IEEE International Geoscience and Remote Sensing Symposium. Toronto, ON, Canada: IEEE, 2002: 1994–1996
    [49] CROOKS W T, CHEKE R A. Soil moisture assessments for brown locust Locustana pardalina breeding potential using synthetic aperture radar[J]. Journal of Applied Remote Sensing, 2014, 8(1): 084898 doi: 10.1117/1.JRS.8.084898
    [50] 扶卿华. 土壤盐分含量的遥感反演及其在东亚飞蝗研究中的应用[D]. 南京: 南京师范大学, 2005

    FU Q H. Soil salt content inversion using remote sensing and its application in study on oriental migratory locust[D]. Nanjing: Nanjing Normal University, 2005
    [51] 吴炳方, 蒙继华, 李强子. 国外农情遥感监测系统现状与启示[J]. 地球科学进展, 2010, 25(10): 1003−1012

    WU B F, MENG J H, LI Q Z. Review of overseas crop monitoring systems with remote sensing[J]. Advances in Earth Science, 2010, 25(10): 1003−1012
    [52] ALI A M, SAVIN I, PODDUBSKIY A, et al. Integrated method for rice cultivation monitoring using Sentinel-2 data and leaf area index[J]. The Egyptian Journal of Remote Sensing and Space Science, 2021, 24(3): 431−441 doi: 10.1016/j.ejrs.2020.06.007
    [53] CHUANG Y C M, SHIU Y S. A comparative analysis of machine learning with WorldView-2 pan-sharpened imagery for tea crop mapping[J]. Sensors (Basel, Switzerland), 2016, 16(5): 594 doi: 10.3390/s16050594
    [54] LIU Q S, HUANG C, LIU G H, et al. Comparison of CBERS-04, GF-1, and GF-2 satellite panchromatic images for mapping quasi-circular vegetation patches in the Yellow River Delta, China[J]. Sensors (Basel, Switzerland), 2018, 18(8): 2733 doi: 10.3390/s18082733
    [55] DONG J W, XIAO X M. Evolution of regional to global paddy rice mapping methods: a review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119: 214−227 doi: 10.1016/j.isprsjprs.2016.05.010
    [56] FAN L L, YANG J, SUN X, et al. The effects of Landsat image acquisition date on winter wheat classification in the North China Plain[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 187: 1−13 doi: 10.1016/j.isprsjprs.2022.02.016
    [57] WHYTE A, FERENTINOS K P, PETROPOULOS G P. A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms[J]. Environmental Modelling & Software, 2018, 104: 40−54
    [58] PHUNG H P, NGUYEN L D, NGUYEN-HUY T, et al. Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data[J]. Journal of Applied Remote Sensing, 2020, 14(1): 014518
    [59] MANSARAY L R, WANG F M, HUANG J F, et al. Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets[J]. Geocarto International, 2020, 35(10): 1088−1108 doi: 10.1080/10106049.2019.1568586
    [60] SHEN Y Y, ZHANG J C, YANG L B, et al. A novel operational rice mapping method based on multi-source satellite images and object-oriented classification[J]. Agronomy, 2022, 12(12): 3010 doi: 10.3390/agronomy12123010
    [61] MANSARAY L R, ZHANG D D, ZHOU Z, et al. Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales[J]. Remote Sensing Letters, 2017, 8(10): 967−976 doi: 10.1080/2150704X.2017.1331472
    [62] YANG C C, PRASHER S O, ENRIGHT P, et al. Application of decision tree technology for image classification using remote sensing data[J]. Agricultural Systems, 2003, 76(3): 1101−1117 doi: 10.1016/S0308-521X(02)00051-3
    [63] WEI P L, CHAI D F, LIN T, et al. Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 174: 198−214 doi: 10.1016/j.isprsjprs.2021.02.011
    [64] CHEN X M. Epidemiology and control of stripe rust (Puccinia striiformis f. sp. tritici) on wheat[J]. Canadian Journal of Plant Pathology, 2005, 27(3): 314−337 doi: 10.1080/07060660509507230
    [65] YANG C M, CHENG C H, CHEN R K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder[J]. Crop Science, 2007, 47(1): 329−335 doi: 10.2135/cropsci2006.05.0335
    [66] SAKAMOTO T, YOKOZAWA M, TORITANI H, et al. A crop phenology detection method using time-series MODIS data[J]. Remote Sensing of Environment, 2005, 96(3/4): 366−374
    [67] ZENG L L, WARDLOW B D, WANG R, et al. A hybrid approach for detecting corn and soybean phenology with time-series MODIS data[J]. Remote Sensing of Environment, 2016, 181: 237−250 doi: 10.1016/j.rse.2016.03.039
    [68] BOSCHETTI M, BUSETTO L, MANFRON G, et al. PhenoRice: a method for automatic extraction of spatio-temporal information on rice crops using satellite data time series[J]. Remote Sensing of Environment, 2017, 194: 347−365 doi: 10.1016/j.rse.2017.03.029
    [69] 蒙继华. 农作物长势遥感监测指标研究[D]. 北京: 中国科学院研究生院(遥感应用研究所), 2006

    MENG J H. Research to crop growth monitoring indicators with remote sensing[D]. Beijing: Chinese Academy of Sciences, 2006
    [70] WU B F, MENG J H, LI Q Z, et al. Remote sensing-based global crop monitoring: experiences with China’s CropWatch system[J]. International Journal of Digital Earth, 2014, 7(2): 113−137 doi: 10.1080/17538947.2013.821185
    [71] PACHECO A, BANNARI A, DEGUISE J C, et al. Application of hyperspectral remote sensing for LAI estimation in precision farming[C]//23rd Canadian Remote Sensing Symposium. Sainte-Foy, Québec, Canada. IEEE, 2001: 281–287
    [72] PASQUALOTTO N, DELEGIDO J, VAN WITTENBERGHE S, et al. Multi-crop green LAI estimation with a new simple sentinel-2 LAI index (SeLI)[J]. Sensors (Basel, Switzerland), 2019, 19(4): 904 doi: 10.3390/s19040904
    [73] SADEH Y, ZHU X, DUNKERLEY D, et al. Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 96: 102260 doi: 10.1016/j.jag.2020.102260
    [74] JIN X L, YANG G J, XU X G, et al. Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data[J]. Remote Sensing, 2015, 7(10): 13251−13272 doi: 10.3390/rs71013251
    [75] WANG J, XIAO X M, BAJGAIN R, et al. Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 154: 189−201 doi: 10.1016/j.isprsjprs.2019.06.007
    [76] MERCIER A, BETBEDER J, RAPINEL S, et al. Evaluation of Sentinel-1 and -2 time series for estimating LAI and biomass of wheat and rapeseed crop types[J]. Journal of Applied Remote Sensing, 2020, 14(2): 024512
    [77] 姚霞, 刘小军, 田永超, 等. 基于星载通道光谱指数与小麦冠层叶片氮素营养指标的定量关系[J]. 应用生态学报, 2013, 24(2): 431−437 doi: 10.13287/j.1001-9332.2013.0176

    YAO X, LIU X J, TIAN Y C, et al. Quantitative relationships between satellite channels-based spectral parameters and wheat canopy leaf nitrogen status[J]. Chinese Journal of Applied Ecology, 2013, 24(2): 431−437 doi: 10.13287/j.1001-9332.2013.0176
    [78] DELLOYE C, WEISS M, DEFOURNY P. Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems[J]. Remote Sensing of Environment, 2018, 216: 245−261 doi: 10.1016/j.rse.2018.06.037
    [79] SHARIFI A. Using Sentinel-2 data to predict nitrogen uptake in maize crop[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 2656−2662 doi: 10.1109/JSTARS.2020.2998638
    [80] HAN D, LIU S B, DU Y, et al. Crop water content of winter wheat revealed with sentinel-1 and sentinel-2 imagery[J]. Sensors (Basel, Switzerland), 2019, 19(18): 4013 doi: 10.3390/s19184013
    [81] 杨龙. 景观尺度下作物种植结构调整对棉铃虫种群发生的影响[D]. 北京: 中国农业科学院, 2020

    YANG L. Effects of cropland use changes on population abundance of Helicoverpa armigera (hübner): a landscape perspective[D]. Beijing: Chinese Academy of Agricultural Sciences, 2020
    [82] 吴涛, 赵冬至, 张丰收, 等. 基于高分辨率遥感影像的大洋河河口湿地景观格局变化[J]. 应用生态学报, 2011, 22(7): 1833−1840

    WU T, ZHAO D Z, ZHANG F S, et al. Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image[J]. Journal of Applied Ecology, 2011, 22(7): 1833−1840
    [83] RASHID I, ANEAUS S. Landscape transformation of an urban wetland in Kashmir Himalaya, India using high-resolution remote sensing data, geospatial modeling, and ground observations over the last 5 decades (1965−2018)[J]. Environmental Monitoring and Assessment, 2020, 192(10): 635 doi: 10.1007/s10661-020-08597-4
    [84] PARAZOO N C, COLEMAN R W, YADAV V, et al. Diverse biosphere influence on carbon and heat in mixed urban Mediterranean landscape revealed by high resolution thermal and optical remote sensing[J]. The Science of the Total Environment, 2022, 806 (3): 151335
    [85] XU J, GUGA S R, RONG G Z, et al. Estimation of frost hazard for tea tree in Zhejiang Province based on machine learning[J]. Agriculture, 2021, 11(7): 607 doi: 10.3390/agriculture11070607
    [86] GENG Y, ZHAO L L, HUANG W J, et al. A landscape-based habitat suitability model (LHS model) for oriental migratory locust area extraction at large scales: a case study along the middle and lower reaches of the Yellow River[J]. Remote Sensing, 2022, 14(5): 1058 doi: 10.3390/rs14051058
    [87] COOPS N C, WULDER M A, WHITE J C. Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation[J]. Remote Sensing of Environment, 2006, 105(2): 83−97 doi: 10.1016/j.rse.2006.06.007
    [88] NUTTER JR F W, RUBSAM R R, TAYLOR S E, et al. Use of geospatially-referenced disease and weather data to improve site-specific forecasts for Stewart’s disease of corn in the US corn belt[J]. Computers and Electronics in Agriculture, 2002, 37(1/2/3): 7−14
    [89] DUTTA S, SINGH S K, KHULLAR M. A case study on forewarning of yellow rust affected areas on wheat crop using satellite data[J]. Journal of the Indian Society of Remote Sensing, 2014, 42(2): 335−342 doi: 10.1007/s12524-013-0329-5
    [90] BHATTACHARYA B K, CHATTOPADHYAY C. A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing[J]. Computers and Electronics in Agriculture, 2013, 90: 35−44 doi: 10.1016/j.compag.2012.10.001
    [91] MARQUES DA SILVA J R, DAMASIO C V, SOUSA A M O, et al. Agriculture pest and disease risk maps considering MSG satellite data and land surface temperature[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 38: 40−50 doi: 10.1016/j.jag.2014.12.016
    [92] ZHANG J C, PU R L, YUAN L, et al. Integrating remotely sensed and meteorological observations to forecast wheat powdery mildew at a regional scale[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11): 4328−4339 doi: 10.1109/JSTARS.2014.2315875
    [93] YUAN L, BAO Z Y, ZHANG H B, et al. Habitat monitoring to evaluate crop disease and pest distributions based on multi-source satellite remote sensing imagery[J]. Optik, 2017, 145: 66−73 doi: 10.1016/j.ijleo.2017.06.071
    [94] SELVA M, AIAZZI B, BUTERA F, et al. Hyper-sharpening: a first approach on SIM-GA data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 3008−3024 doi: 10.1109/JSTARS.2015.2440092
    [95] ZHANG K, WANG M, YANG S Y. Multispectral and hyperspectral image fusion based on group spectral embedding and low-rank factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1363−1371 doi: 10.1109/TGRS.2016.2623626
    [96] GHADJATI M, MOUSSAOUI A, BOUKHAROUBA A. A novel iterative PCA-based pansharpening method[J]. Remote Sensing Letters, 2019, 10(3): 264−273 doi: 10.1080/2150704X.2018.1547443
    [97] 董文全, 蒙继华. 遥感数据时空融合研究进展及展望[J]. 国土资源遥感, 2018, 30(2): 1−11

    DONG W Q, MENG J H. Review of spatiotemporal fusion model of remote sensing data[J]. Remote Sensing for Land & Resources, 2018, 30(2): 1−11
    [98] 田洋洋. 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[D]. 杭州: 杭州电子科技大学, 2021

    TIAN Y Y. Evaluation of habitat suitability for rice sheath blight based on multi-source satellite remote sensing data[D]. Hangzhou: Hangzhou Dianzi University, 2021
    [99] SHI Y, HUANG W J, DONG Y Y, et al. The influence of landscape’s dynamics on the oriental migratory locust habitat change based on the time-series satellite data[J]. Journal of Environmental Management, 2018, 218: 280−290
    [100] SUN R Q, HUANG W J, DONG Y Y, et al. Dynamic forecast of desert locust presence using machine learning with a multivariate time lag sliding window technique[J]. Remote Sensing, 2022, 14(3): 747 doi: 10.3390/rs14030747
    [101] 吴文浩. 外来森林病虫害潜在生境预测方法研究[D]. 南京: 南京林业大学, 2010

    WU W H. Research on potential habitat of alien species based on WEB database and GIS[D]. Nanjing: Nanjing Forestry University, 2010
    [102] HU S J, LIU X F, FU D Y, et al. Projecting distribution of the overwintering population of Sogatella furcifera (Hemiptera: Delphacidae), in Yunnan, China with analysis on key influencing climatic factors[J]. Journal of Insect Science, 2015, 15(1): 148 doi: 10.1093/jisesa/iev131
    [103] 林伟, 徐淼锋, 权永兵, 等. 基于MaxEnt模型的草地贪夜蛾适生性分析[J]. 植物检疫, 2019, 33(4): 69−73

    LIN W, XU M F, QUAN Y B, et al. Potential geographic distribution of Spodoptera frugiperda in China based on MaxEnt model[J]. Plant Quarantine, 2019, 33(4): 69−73
    [104] 曹学仁, 陈林, 周益林, 等. 基于MaxEnt的麦瘟病在全球及中国的潜在分布区预测[J]. 植物保护, 2011, 37(3): 80−83

    CAO X R, CHEN L, ZHOU Y L, et al. Potential distribution of Magnaporthe grisea in China and the world, predicted by MaxEnt[J]. Plant Protection, 2011, 37(3): 80−83
    [105] RAHIMIAN BOOGAR A, SALEHI H, POURGHASEMI H R, et al. Predicting habitat suitability and conserving Juniperus spp. habitat using SVM and maximum entropy machine learning techniques[J]. Water, 2019, 11(10): 2049 doi: 10.3390/w11102049
    [106] 黄文江, 董莹莹, 赵龙龙, 等. 蝗虫遥感监测预警研究现状与展望[J]. 遥感学报, 2020, 24(10): 1270−1279

    HUANG W J, DONG Y Y, ZHAO L L, et al. Review of locust remote sensing monitoring and early warning[J]. Journal of Remote Sensing, 2020, 24(10): 1270−1279
    [107] AZRAG A G, MOHAMED S A, NDLELA S, et al. Predicting the habitat suitability of the invasive white mango scale, Aulacaspis tubercularis; Newstead, 1906 (Hemiptera: Diaspididae) using bioclimatic variables[J]. Pest Management Science, 2022, 78(10): 4114−4126 doi: 10.1002/ps.7030
    [108] 朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用[J]. 生物多样性, 2013, 21(1): 90−98 doi: 10.3724/SP.J.1003.2013.09106

    ZHU G P, LIU G Q, BU W J, et al. Ecological niche modeling and its applications in biodiversity conservation[J]. Biodiversity Science, 2013, 21(1): 90−98 doi: 10.3724/SP.J.1003.2013.09106
    [109] MEYNARD C N, GAY P E, LECOQ M, et al. Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change[J]. Global Change Biology, 2017, 23(11): 4739−4749 doi: 10.1111/gcb.13739
    [110] 沈鹏, 李功权. 基于生态位因子模型的湖北省松材线虫病风险评估[J]. 浙江农林大学学报, 2021, 38(3): 560−566

    SHEN P, LI G Q. Risk assessment of Bursaphelenchus xylophilus in Hubei Province based on ecological niche factor analysis model[J]. Journal of Zhejiang A&F University, 2021, 38(3): 560−566
    [111] 许晴, 张锦水, 张凤, 等. 深度学习农作物分类的弱样本适用性[J]. 遥感学报, 2022, 26(7): 1395−1409

    XU Q, ZHANG J S, ZHANG F, et al. Applicability of weak samples to deep learning crop classification[J]. National Remote Sensing Bulletin, 2022, 26(7): 1395−1409
    [112] 苏扬, 吴鹏海, 程洁, 等. AMSR-E地表温度数据重建深度学习方法[J]. 遥感学报, 2022, 26(4): 739−751

    SU Y, WU P H, CHENG J, et al. Research on deep learning methods for AMSR-E land surface temperature data reconstruction[J]. National Remote Sensing Bulletin, 2022, 26(4): 739−751
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  • 收稿日期:  2022-11-29
  • 录用日期:  2023-03-08
  • 修回日期:  2023-03-08
  • 网络出版日期:  2023-03-14
  • 刊出日期:  2023-07-10

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