【摘要】：為明晰江淮區(qū)域大田冬小麥赤霉病的發(fā)生特征,建立冬小麥赤霉病遙感估測模型,該文分析了冬小麥赤霉病病情指數(shù)與氣候因素(不同時(shí)間尺度日均氣溫和日均空氣相對濕度)、生長參數(shù)(生物量、葉面積指數(shù)和葉片葉綠素含量)和光譜信息(NDVI、RVI和DVI)之間的互作關(guān)系。結(jié)果表明:1)不同時(shí)間尺度日均氣溫之間存在較好相關(guān)性,5日均氣溫與冬小麥赤霉病病情指數(shù)間的相關(guān)系數(shù)最大為0.77。與日均氣溫相類似,不同時(shí)間尺度日均空氣相對濕度之間也存在不同程度的相關(guān)性,5日均空氣相對濕度與赤霉病病情指數(shù)間的相關(guān)性最大,其相關(guān)性高于5日均氣溫。2)冬小麥生物量、葉面積指數(shù)和葉片葉綠素含量與赤霉病病情指數(shù)之間均呈線性正相關(guān)關(guān)系,且均達(dá)到顯著水平,說明冬小麥群體密度大、郁閉程度高以及長勢過旺是赤霉病易發(fā)的主要農(nóng)學(xué)誘因。3)遙感植被指數(shù)NDVI(normalized difference vegetation index)、RVI(ratio vegetation index)和DVI(difference vegetation index)分別與冬小麥葉面積指數(shù)、生物量和葉片葉綠素含量之間有較好相關(guān)性,可以利用NDVI、RVI和DVI分別替換葉面積指數(shù)、生物量和葉片葉綠素含量參與建模。4)綜合5日均氣溫、5日均空氣相對濕度、NDVI、RVI和DVI 5個(gè)敏感因子,構(gòu)建基于溫濕度與遙感植被指數(shù)的冬小麥赤霉病病情指數(shù)估測模型,模型的估測值與實(shí)測值較為一致,RMSE為5.3%,相對誤差為9.54%。說明本研究所建立的估測模型可以實(shí)現(xiàn)對冬小麥?zhǔn)蓟ㄆ诔嗝共〉挠行Ч罍y,該研究可為江淮區(qū)域冬小麥生產(chǎn)中防病減災(zāi)的信息獲取提供方法參考。
[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.
【作者單位】： 江蘇省農(nóng)業(yè)科學(xué)院農(nóng)業(yè)信息研究所;中國科學(xué)院遙感與數(shù)字地球研究所;
【基金】：國家自然科學(xué)基金項(xiàng)目(41171336) 江蘇省重點(diǎn)研究計(jì)劃(BE2016730) 中科院數(shù)字地球重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(2016LDE007)
【分類號】：S435.121.45;TP79
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本文編號：2393821