基于温湿度与遥感植被指数的冬小麦赤霉病估测
发布时间：2018-12-28 10:12
【摘要】：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]:In order to understand the occurrence characteristics of winter wheat scab in the field of Jianghuai region, a remote sensing estimation model of winter wheat scab was established. The index of scab disease and climatic factors (mean daily air temperature and relative humidity of daily air), growth parameters (biomass, leaf area index and leaf chlorophyll content) and spectral information (NDVI,) of winter wheat were analyzed in this paper. The interaction between RVI and DVI. The results showed that: 1) there was a good correlation between daily mean temperature at different time scales, and the correlation coefficient between daily mean temperature and scab disease index of winter wheat was the largest 0.77. Similar to the daily mean temperature, the correlation between daily average air relative humidity and scab disease index was the highest in different time scales, and the correlation between daily mean air relative humidity and scab disease index was the highest. The correlation was higher than that of 5 days mean temperature. 2) there was a linear positive correlation among biomass, leaf area index, chlorophyll content and scab disease index of winter wheat, which indicated that the population density of winter wheat was high. High canopy closure and overgrowth were the main agronomic inducements for the susceptibility to scab. 3) NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) of remote sensing vegetation index and leaf area index of winter wheat, respectively. There is a good correlation between biomass and chlorophyll content in leaves. NDVI,RVI and DVI can be used to replace leaf area index, biomass and chlorophyll content participate in modeling. 4) combined 5 days mean air temperature, 5 day average air relative humidity, 5 day average air relative humidity, NDVI, Five sensitive factors, RVI and DVI, were used to establish a model for estimating the scab disease condition index of winter wheat based on temperature, humidity and remote sensing vegetation index. The estimated value of the model was in good agreement with the measured value, the RMSE was 5.3 and the relative error was 9.54. The results show that the estimation model established in this paper can effectively estimate the scab of winter wheat at the beginning of flowering, and this study can provide a method reference for obtaining the information of disease prevention and mitigation in the production of winter wheat in Jianghuai region.
【作者单位】： 江苏省农业科学院农业信息研究所;中国科学院遥感与数字地球研究所;
【基金】：国家自然科学基金项目(41171336) 江苏省重点研究计划(BE2016730) 中科院数字地球重点实验室开放基金项目(2016LDE007)
【分类号】：S435.121.45;TP79
，
本文编号：2393821
[Abstract]:In order to understand the occurrence characteristics of winter wheat scab in the field of Jianghuai region, a remote sensing estimation model of winter wheat scab was established. The index of scab disease and climatic factors (mean daily air temperature and relative humidity of daily air), growth parameters (biomass, leaf area index and leaf chlorophyll content) and spectral information (NDVI,) of winter wheat were analyzed in this paper. The interaction between RVI and DVI. The results showed that: 1) there was a good correlation between daily mean temperature at different time scales, and the correlation coefficient between daily mean temperature and scab disease index of winter wheat was the largest 0.77. Similar to the daily mean temperature, the correlation between daily average air relative humidity and scab disease index was the highest in different time scales, and the correlation between daily mean air relative humidity and scab disease index was the highest. The correlation was higher than that of 5 days mean temperature. 2) there was a linear positive correlation among biomass, leaf area index, chlorophyll content and scab disease index of winter wheat, which indicated that the population density of winter wheat was high. High canopy closure and overgrowth were the main agronomic inducements for the susceptibility to scab. 3) NDVI (normalized difference vegetation index), RVI (ratio vegetation index) and DVI (difference vegetation index) of remote sensing vegetation index and leaf area index of winter wheat, respectively. There is a good correlation between biomass and chlorophyll content in leaves. NDVI,RVI and DVI can be used to replace leaf area index, biomass and chlorophyll content participate in modeling. 4) combined 5 days mean air temperature, 5 day average air relative humidity, 5 day average air relative humidity, NDVI, Five sensitive factors, RVI and DVI, were used to establish a model for estimating the scab disease condition index of winter wheat based on temperature, humidity and remote sensing vegetation index. The estimated value of the model was in good agreement with the measured value, the RMSE was 5.3 and the relative error was 9.54. The results show that the estimation model established in this paper can effectively estimate the scab of winter wheat at the beginning of flowering, and this study can provide a method reference for obtaining the information of disease prevention and mitigation in the production of winter wheat in Jianghuai region.
【作者单位】： 江苏省农业科学院农业信息研究所;中国科学院遥感与数字地球研究所;
【基金】：国家自然科学基金项目(41171336) 江苏省重点研究计划(BE2016730) 中科院数字地球重点实验室开放基金项目(2016LDE007)
【分类号】：S435.121.45;TP79
，
本文编号：2393821
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