本文關(guān)鍵詞：基于粒子濾波和多變量權(quán)重的冬小麥估產(chǎn)研究　出處：《農(nóng)業(yè)機(jī)械學(xué)報(bào)》2017年10期 　論文類型：期刊論文

  更多相關(guān)文章： 冬小麥 粒子濾波 數(shù)據(jù)同化 遙感 熵值法 單產(chǎn)估測(cè)

【摘要】：為了構(gòu)建能夠反映作物長(zhǎng)勢(shì)的綜合性指標(biāo)以及準(zhǔn)確估測(cè)作物產(chǎn)量,采用粒子濾波算法同化CERES-Wheat模型模擬和基于Landsat數(shù)據(jù)反演的葉面積指數(shù)(Leaf area index,LAI)、地上生物量和0~20 cm土壤含水率,獲取冬小麥主要生育期以天為尺度的變量同化值,分析不同生育時(shí)期的LAI、地上生物量和土壤含水率同化值與實(shí)測(cè)單產(chǎn)的相關(guān)性,并應(yīng)用熵值的組合預(yù)測(cè)方法確定不同狀態(tài)變量影響籽粒產(chǎn)量的權(quán)重,進(jìn)而生成綜合性指數(shù),并分析其與實(shí)測(cè)單產(chǎn)的相關(guān)性。結(jié)果表明,LAI、地上生物量和土壤含水率同化值和田間實(shí)測(cè)值間的均方根誤差(Root mean square error,RMSE)以及平均相對(duì)誤差(Mean relative error,MRE)均低于這些變量模擬值和實(shí)測(cè)值間的RMSE和MRE,說明數(shù)據(jù)同化方法提高了時(shí)間序列LAI、地上生物量和土壤含水率的模擬精度�；诓煌瑺顟B(tài)變量的權(quán)重生成的綜合性指數(shù)與實(shí)測(cè)單產(chǎn)間的相關(guān)性大于單個(gè)變量與實(shí)測(cè)單產(chǎn)間的相關(guān)性;基于綜合性指數(shù)構(gòu)建小麥單產(chǎn)估測(cè)模型,其估產(chǎn)精度(R2=0.78,RMSE為330 kg/hm2)分別比基于LAI、地上生物量和土壤含水率建立模型的估產(chǎn)精度顯著提高,表明構(gòu)建的綜合性指數(shù)充分結(jié)合了不同變量在作物估產(chǎn)方面的優(yōu)勢(shì),可用于高精度的冬小麥單產(chǎn)估測(cè)。
[Abstract]:In order to construct a comprehensive index which can reflect crop growth and estimate crop yield accurately. The particle filter algorithm was used to assimilate the CERES-Wheat model and the leaf area index leaf area index based on Landsat data. The aboveground biomass and soil moisture content of 0 ~ 20 cm were used to obtain the variable assimilation value of winter wheat in the main growth period, and to analyze the LAI of different growing stages. The correlation between the assimilation value of aboveground biomass and soil moisture content and the measured yield, and the combined prediction method of entropy value was used to determine the weight of different state variables affecting grain yield, and then the comprehensive index was generated. The correlation between the yield and the measured yield was analyzed. Root mean square error is the root mean square error between the assimilation value of aboveground biomass and soil moisture content and the measured value in the field. RMSE) and the mean relative error (mean relative error) are lower than the RMSE and MRE between the simulated and measured values of these variables. The data assimilation method improves the time series LAI. Simulation accuracy of aboveground biomass and soil moisture content. The correlation between the comprehensive index based on the weight of different state variables and the measured yield is greater than that between the single variable and the measured yield. The yield estimation accuracy of wheat yield estimation model based on comprehensive index was 330kg / hm ~ (-2), which was 330kg / hm ~ (2) than that based on LAI, respectively. The precision of yield estimation of aboveground biomass and soil moisture content model was significantly improved, which indicated that the constructed comprehensive index fully combined the advantages of different variables in crop yield estimation, and could be used to estimate yield per unit yield of winter wheat with high precision.
【作者單位】： 中國(guó)農(nóng)業(yè)大學(xué)信息與電氣工程學(xué)院;農(nóng)業(yè)部農(nóng)業(yè)災(zāi)害遙感重點(diǎn)實(shí)驗(yàn)室;陜西省氣象局;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41371390)
【分類號(hào)】：S512.11;TP79
【正文快照】： 引言小麥?zhǔn)俏覈?guó)重要的糧食作物之一,其產(chǎn)量95%以上源于光合作用,而地上生物量是小麥光合作用的最終產(chǎn)物,與籽粒產(chǎn)量形成密切相關(guān),因此,區(qū)域尺度小麥地上生物量的估算能夠?yàn)樽蚜．a(chǎn)量的估測(cè)和預(yù)測(cè)提供重要依據(jù)。隨著空間信息技術(shù)的發(fā)展,利用遙感技術(shù)獲取地表植被信息和相關(guān)參數(shù),，

本文編號(hào)：1423928