基于温湿度与遥感植被指数的冬小麦赤霉病估测
为明晰江淮区域大田冬小麦赤霉病的发生特征，建立冬小麦赤霉病遥感估测模型，该文分析了冬小麦赤霉病病情指数与气候因素（不同时间尺度日均气温和日均空气相对湿度）、生长参数（生物量、叶面积指数和叶片叶绿素含量）和光谱信息（NDVI、RVI和DVI）之间的互作关系。结果表明：1）不同时间尺度日均气温之间存在较好相关性，5日均气温与冬小麦赤霉病病情指数间的相关系数最大为0.77。与日均气温相类似，不同时间尺度日均空气相对湿度之间也存在不同程度的相关性，5日均空气相对湿度与赤霉病病情指数间的相关性最大，其相关性高于5日均气温。2）冬小麦生物量、叶面积指数和叶片叶绿素含量与赤霉病病情指数之间均呈线性正相关关系...
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| Published in | 农业工程学报 Vol. 33; no. 23; pp. 203 - 210 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
江苏省农业科学院农业信息研究所,南京,210014%中国科学院遥感与数字地球研究所,北京,100094
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2017.23.026 |
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| Abstract | 为明晰江淮区域大田冬小麦赤霉病的发生特征，建立冬小麦赤霉病遥感估测模型，该文分析了冬小麦赤霉病病情指数与气候因素（不同时间尺度日均气温和日均空气相对湿度）、生长参数（生物量、叶面积指数和叶片叶绿素含量）和光谱信息（NDVI、RVI和DVI）之间的互作关系。结果表明：1）不同时间尺度日均气温之间存在较好相关性，5日均气温与冬小麦赤霉病病情指数间的相关系数最大为0.77。与日均气温相类似，不同时间尺度日均空气相对湿度之间也存在不同程度的相关性，5日均空气相对湿度与赤霉病病情指数间的相关性最大，其相关性高于5日均气温。2）冬小麦生物量、叶面积指数和叶片叶绿素含量与赤霉病病情指数之间均呈线性正相关关系，且均达到显著水平，说明冬小麦群体密度大、郁闭程度高以及长势过旺是赤霉病易发的主要农学诱因。3）遥感植被指数NDVI（normalized difference vegetation index）、RVI（ratio vegetation index）和DVI（difference vegetation index）分别与冬小麦叶面积指数、生物量和叶片叶绿素含量之间有较好相关性，可以利用NDVI、RVI和DVI分别替换叶面积指数、生物量和叶片叶绿素含量参与建模。4）综合5日均气温、5日均空气相对湿度、NDVI、RVI和DVI5个敏感因子，构建基于温湿度与遥感植被指数的冬小麦赤霉病病情指数估测模型，模型的估测值与实测值较为一致，RMSE为5.3%，相对误差为9.54%。说明本研究所建立的估测模型可以实现对冬小麦始花期赤霉病的有效估测，该研究可为江淮区域冬小麦生产中防病减灾的信息获取提供方法参考。 |
|---|---|
| AbstractList | 为明晰江淮区域大田冬小麦赤霉病的发生特征，建立冬小麦赤霉病遥感估测模型，该文分析了冬小麦赤霉病病情指数与气候因素（不同时间尺度日均气温和日均空气相对湿度）、生长参数（生物量、叶面积指数和叶片叶绿素含量）和光谱信息（NDVI、RVI和DVI）之间的互作关系。结果表明：1）不同时间尺度日均气温之间存在较好相关性，5日均气温与冬小麦赤霉病病情指数间的相关系数最大为0.77。与日均气温相类似，不同时间尺度日均空气相对湿度之间也存在不同程度的相关性，5日均空气相对湿度与赤霉病病情指数间的相关性最大，其相关性高于5日均气温。2）冬小麦生物量、叶面积指数和叶片叶绿素含量与赤霉病病情指数之间均呈线性正相关关系，且均达到显著水平，说明冬小麦群体密度大、郁闭程度高以及长势过旺是赤霉病易发的主要农学诱因。3）遥感植被指数NDVI（normalized difference vegetation index）、RVI（ratio vegetation index）和DVI（difference vegetation index）分别与冬小麦叶面积指数、生物量和叶片叶绿素含量之间有较好相关性，可以利用NDVI、RVI和DVI分别替换叶面积指数、生物量和叶片叶绿素含量参与建模。4）综合5日均气温、5日均空气相对湿度、NDVI、RVI和DVI5个敏感因子，构建基于温湿度与遥感植被指数的冬小麦赤霉病病情指数估测模型，模型的估测值与实测值较为一致，RMSE为5.3%，相对误差为9.54%。说明本研究所建立的估测模型可以实现对冬小麦始花期赤霉病的有效估测，该研究可为江淮区域冬小麦生产中防病减灾的信息获取提供方法参考。 S127; 为明晰江淮区域大田冬小麦赤霉病的发生特征,建立冬小麦赤霉病遥感估测模型,该文分析了冬小麦赤霉病病情指数与气候因素(不同时间尺度日均气温和日均空气相对湿度)、生长参数(生物量、叶面积指数和叶片叶绿素含量)和光谱信息(NDVI、RVI和DVI)之间的互作关系.结果表明:1)不同时间尺度日均气温之间存在较好相关性,5日均气温与冬小麦赤霉病病情指数间的相关系数最大为0.77.与日均气温相类似,不同时间尺度日均空气相对湿度之间也存在不同程度的相关性,5日均空气相对湿度与赤霉病病情指数间的相关性最大,其相关性高于5日均气温.2)冬小麦生物量、叶面积指数和叶片叶绿素含量与赤霉病病情指数之间均呈线性正相关关系,且均达到显著水平,说明冬小麦群体密度大、郁闭程度高以及长势过旺是赤霉病易发的主要农学诱因.3)遥感植被指数NDVI(normalized difference vegetation index)、RVI(ratio vegetation index)和DVI(difference vegetation index)分别与冬小麦叶面积指数、生物量和叶片叶绿素含量之间有较好相关性,可以利用NDVI、RVI和DVI分别替换叶面积指数、生物量和叶片叶绿素含量参与建模.4)综合5日均气温、5日均空气相对湿度、NDVI、RVI和DVI 5个敏感因子,构建基于温湿度与遥感植被指数的冬小麦赤霉病病情指数估测模型,模型的估测值与实测值较为一致,RMSE为5.3％,相对误差为9.54％.说明本研究所建立的估测模型可以实现对冬小麦始花期赤霉病的有效估测,该研究可为江淮区域冬小麦生产中防病减灾的信息获取提供方法参考. |
| Abstract_FL | Scab is one of the main diseases of winter wheat in Yangtze-Huaihe River region in China, whose monitoring and forecasting timely in large area will help to adjust the pesticide spraying measures reasonably and realize the purpose of reducing disaster and increasing yield. In this study, we carried out remote monitoring tests of winter wheat scab in 4 counties (Donghai, Lianshui, Taixing and Dafeng) of Jiangsu Province in Yangtze-Huaihe River region, analyzed the interaction and relationship between winter wheat scab characteristics, climatic factors, growth parameters and spectral information, selected major scab's sensitive factors, and established the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The results showed that: 1) There is a good correlation between the daily mean temperatures in different time scales, of which the correlation between the daily mean temperature of 7 days and the daily mean temperature of 10 days is the highest, and the correlation coefficient is 0.9665. The correlation coefficient between the daily mean temperature of 5 days and the winter wheat scab disease index is the largest, which is 0.7726, indicating that the daily average temperature of 5 days in different time scales has the most obvious effect on the occurrence of scab in winter wheat. 2) Similar to the daily mean temperature, there are different degrees of correlation between the daily mean relative humidity at different time scales. The correlation coefficient is the largest between daily mean relative air humidity of 7 days and daily mean relative air humidity of 10 days, and its value is 0.9337. The daily mean relative air humidity of 5 days has the highest correlation with winter wheat scab disease index, and its correlation coefficient is 0.7842, higher than the daily mean temperature of 5 days, which shows that the daily mean relative air humidity of 5 days has a higher influence on winter wheat scab than the daily mean temperature of 5 days. 3) There is a positive linear correlation between winter wheat biomass, leaf area index (LAI) and leaf chlorophyll content and scab disease index, and the r values of the linear trend fitting are 0.6084, 0.5845 and 0.5746, respectively, which reach the significant level, and indicate the large population density, high canopy density and over vigorous growth of winter wheat are the main incentive for scab. 4) Remote sensing vegetation index such as NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) has a good correlation with winter wheat LAI, biomass and leaf chlorophyll content respectively, and their correlation coefficients are 0.8516, 0.8549 and 0.7727 respectively. NDVI, RVI and DVI can be used to replace LAI, biomass and leaf chlorophyll content to participate in modeling. 5) Combining 5 sensitive factors i.e. NDVI, RVI, DVI, mean daily temperature of 5 days and average daily relative humidity of 5 days, we establish the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors. The estimated value of the model is consistent with the measured value, root mean square error (RMSE) is 5.3％, and the estimation accuracy is 90.46％. It shows that the estimation model in this study can effectively estimate winter wheat scab in Yangtze-Huaihe River region in China. |
| Author | 李卫国;黄文江;董莹莹;陈华;王晶晶;单婕 |
| AuthorAffiliation | 江苏省农业科学院农业信息研究所,南京210014;中国科学院遥感与数字地球研究所,北京100094 |
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| Author_FL | Li Weiguo Wang Jingjing Chen Hua Shan Jie Dong Yingying Huang Wenjiang |
| Author_FL_xml | – sequence: 1 fullname: Li Weiguo – sequence: 2 fullname: Huang Wenjiang – sequence: 3 fullname: Dong Yingying – sequence: 4 fullname: Chen Hua – sequence: 5 fullname: Wang Jingjing – sequence: 6 fullname: Shan Jie |
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| DocumentTitleAlternate | Estimation on winter wheat scab based on combination of temperature,humidity and remote sensing vegetation index |
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| Keywords | 病害 growth parameters relative humidity winter wheat scab 冬小麦赤霉病 diseases Yangtze-Huaihe river region 温度 estimation 相对湿度 spectral information 生长参数 temperature 评估 光谱信息 江淮区域 |
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| Notes | Scab is one of the main diseases of winter wheat in Yangtze-Huaihe River region in China,whose monitoring and forecasting timely in large area will help to adjust the pesticide spraying measures reasonably and realize the purpose of reducing disaster and increasing yield.In this study,we carried out remote monitoring tests of winter wheat scab in4counties(Donghai,Lianshui,Taixing and Dafeng)of Jiangsu Province in Yangtze-Huaihe River region,analyzed the interaction and relationship between winter wheat scab characteristics,climatic factors,growth parameters and spectral information,selected major scab’s sensitive factors,and established the remote sensing estimation model of winter wheat scab disease index based on interactions between spectral information and climatic factors.The results showed that:1)There is a good correlation between the daily mean temperatures in different time scales,of which the correlation between the daily mean temperature of7days and the daily mean temperature of10days is the highes |
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| Publisher | 江苏省农业科学院农业信息研究所,南京,210014%中国科学院遥感与数字地球研究所,北京,100094 |
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| Snippet | 为明晰江淮区域大田冬小麦赤霉病的发生特征，建立冬小麦赤霉病遥感估测模型，该文分析了冬小麦赤霉病病情指数与气候因素（不同时间尺度日均气温和日均空气相对湿度）、生长... S127; 为明晰江淮区域大田冬小麦赤霉病的发生特征,建立冬小麦赤霉病遥感估测模型,该文分析了冬小麦赤霉病病情指数与气候因素(不同时间尺度日均气温和日均空气相对湿度)、生... |
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| StartPage | 203 |
| SubjectTerms | 病害;评估;温度;冬小麦赤霉病;光谱信息;相对湿度;生长参数;江淮区域 |
| Title | 基于温湿度与遥感植被指数的冬小麦赤霉病估测 |
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