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

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

1.
杭州电子科技大学人工智能学院　杭州　310018
2.
中国科学院空天信息创新研究院数字地球重点实验室　北京　100094
3.
农业农村部农业遥感机理与定量遥感重点实验室/北京市农林科学院信息技术研究中心　北京　100097

基金项目: 国家自然科学基金(42071420)、国家重点研发计划青年科学家项目(2022YFD2000100)和宁波市重大科技任务攻关项目(科技创新2025重大专项, 2021Z048)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
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出版历程
- 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

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

1.
College of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3.
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs/Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Funds: This study was supported by the National Natural Science Foundation of China (42071420), the National Key R&D Program of China (2022YFD2000100) and Major Special Project for 2025 Scientific and Technological Innovation (Major Scientific and Technological Task Project in Ningbo City, 2021Z048).

More Information

Corresponding author: E-mail: zhangjcrs@hdu.edu.cn

摘要

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。
- 多源遥感信息 /
- 作物病虫害 /
- 因子表征 /
- 生境适宜性
Abstract: Crop diseases and pests seriously affect the yield and quality of food, which causing great losses to agricultural production. Habitat suitability assessment for crop diseases and pests can effectively characterize environmental areas where are suitable for the breeding and prevalence of pests and diseases. And it can provide crucial information for disease and pest prediction. However, the occurrence and prevalence of crop diseases and pests are affected by a variety of habitat factors. The factors are highly spatially and temporally heterogeneous, which are difficult to characterize effectively through traditional meteorological station data, human surveys, etc. This situation poses a great challenge to the evaluation of pests and diseases habitat. Fortunately, the development and maturity of remote sensing technology present significant opportunities. Multi-source remote sensing information not only has natural advantages in the representation of spatiotemporal heterogeneity, but also can form information complementarities with traditional meteorological station data. Therefore, it can provide comprehensive and abundant information for habitat suitability evaluation of pests and diseases, and support the model construction. This paper reviews the research progress of multi-source remote sensing information in evaluating the habitat suitability of crop pests and diseases, focusing on the potential of multi-source remote sensing data in the characterization of habitat factors such as host crop distribution and growth status, environmental meteorological conditions and landscape, as well as the modelling methods such as statistical models, machine learning models and niche models in the wide-scale habitat suitability assessment. On this basis, the paper proposes a framework of crop diseases and pests habitat evaluation model construction based on multi-source remote sensing information, and discusses the development trend of technology. This research provides technical support for more accurate and scientific regional prevention and management. Besides, it provides scientific guidance for integrated control and green prevention.
- Multi-source remote sensing /
- Crop diseases and pests /
- Factor representation /
- Habitat suitability

HTML全文

图 1 基于多源遥感信息的作物病虫害生境表征

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

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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

下载: 全尺寸图片幻灯片

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 to habitat factor 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]
	寄主分布 Host distribution	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]
	叶面积指数反演 Leaf area index inversion	Sentinel-2	小麦 Triticum aestivum	[36]
	氮素、水分监测 Nitrogen and water monitoring	Sentinel-2	玉米 Zea mays	[37]
	氮素、水分监测 Nitrogen and water monitoring	MODIS、Landsat 8	玉米 Zea mays	[38-39]
气象因素 meteorological factor	气温 Temperature	MODIS	/	[40-41]
气象因素 meteorological factor	降水 Precipitation	TRMM	/	[42-43]
景观因素 Landscape factor	农田聚集度等景观指标 Farmland aggregation and other landscape indicators	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 graminis	OA=78%	[92]
	GLM	小麦蚜虫 Macrosiphum avenae	OA=52%	[46]
	FLDA	春小麦白粉病 Blumeria graminis	OA=82%	[93]
	FLDA	春小麦蚜虫 Macrosiphum avenae	OA=82%	[93]
	PLSR	水稻纹枯病 Rhizoctonia solani	R²=0.68	[98]
机器学习模型 Machine learning model	LMRF	东亚飞蝗 Locusta migratoria manilensis	U>0.76	[99]
	CART	小麦白粉病 Blumeria graminis	OA=73.1%	[18]
	SVM	沙漠蝗虫 Schistocerca gregaria	OA=77.46%	[100]
	SMOTE-BPNN	小麦条锈病 Puccinia striiformis	OA=86.7%	[18]
	kNN	小麦白粉病 Blumeria graminis	OA=84.6%	[18]
生态位模型 Ecological niche modeling	GARP	松材线虫 Bursaphelenchus xylophilus	AUC =0.89	[101]
	ENFA	白背飞虱 Sogatella furcifera	AUC=0.922	[102]
	MaxEnt	草地贪夜蛾 Spodoptera frugiperda	AUC=0.912	[103]
	MaxEnt	小麦麦瘟病 Magnaporthe oryzae	AUC>0.99	[104]

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