本文關(guān)鍵詞：基于多變量和數(shù)據(jù)同化算法的冬小麥單產(chǎn)估測(cè)　出處：《中國農(nóng)業(yè)大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 冬小麥 產(chǎn)量估測(cè) CERES-Wheat模型 數(shù)據(jù)同化 條件植被溫度指數(shù)

【摘要】：作物長勢(shì)監(jiān)測(cè)及產(chǎn)量的準(zhǔn)確估測(cè)、預(yù)測(cè)是糧食安全的重要保障。遙感技術(shù)的發(fā)展為大面積、實(shí)時(shí)、動(dòng)態(tài)的作物產(chǎn)量估測(cè)、預(yù)測(cè)提供了有效途徑,其中,基于數(shù)據(jù)同化算法耦合作物生長模型和遙感數(shù)據(jù),能充分考慮到作物生長的內(nèi)在機(jī)理過程以及環(huán)境因素對(duì)作物生長發(fā)育的影響,同時(shí)能有效地解決作物生長模型區(qū)域參數(shù)獲取困難的問題。以陜西省關(guān)中平原為研究區(qū)域,獲取冬小麥生長季的Landsat遙感數(shù)據(jù),利用Landsat數(shù)據(jù)計(jì)算歸一化植被指數(shù)(NDVI)和反演條件植被溫度指數(shù)(VTCI),分析NDVI和實(shí)測(cè)葉面積指數(shù)(LAI)、NDVI和實(shí)測(cè)地上生物量(β)以及VTCI和實(shí)測(cè)0～20 cm層土壤含水量(θ)間的相關(guān)性,從而構(gòu)建回歸模型以估測(cè)區(qū)域LAI、β和θ。通過田間實(shí)測(cè)和調(diào)查的LAI、β、θ、小麥單產(chǎn)和收獲日期對(duì)CERES-Wheat模型的遺傳參數(shù)進(jìn)行標(biāo)定,對(duì)標(biāo)定的CERES-Wheat模型的模擬結(jié)果進(jìn)行驗(yàn)證,結(jié)果表明,模擬β與實(shí)測(cè)β的平均相對(duì)誤差(MRE)及模擬θ與實(shí)測(cè)θ的MRE均小于10%,模擬單產(chǎn)和實(shí)測(cè)單產(chǎn)的MRE小于15%,模擬小麥?zhǔn)斋@日期與實(shí)際日期的偏差小于4 d,說明標(biāo)定的CERES-Wheat模型的模擬精度較高。利用四維變分(4DVAR)、集合卡爾曼濾波(EnKF)和粒子濾波(PF)算法同化CERES-Wheat模型模擬的和Landsat數(shù)據(jù)反演的LAI、β和θ,以獲取冬小麥主要生育時(shí)期的LAI、β和θ同化值,通過田間實(shí)測(cè)數(shù)據(jù)分別檢驗(yàn)和對(duì)比3種算法同化LAI、β和θ的精度,結(jié)果表明,基于3種算法的同化變量均能有效地表達(dá)CERES-Wheat模型模擬LAI、β和θ在小麥不同生育時(shí)期的變化特征,同時(shí)在遙感反演變量的影響下,同化變量比模擬變量更接近實(shí)測(cè)值。PF算法同化LAI和β的精度高于EnKF和4DVAR算法同化LAI和β的精度,且基于PF算法能較好地表達(dá)模擬LAI和β的變化特征,因此,PF算法同化LAI和β的效果最優(yōu)。EnKF算法在表達(dá)模擬LAI和β的變化特征方面,效果優(yōu)于4DVAR算法的同化效果,然而,EnKF算法同化LAI和β的精度低于4DVAR算法同化LAI和β的精度。4DVAR算法在表達(dá)模擬LAI和β的變化特征方面效果較好,且同化LAI和β的精度也較高,然而,4DVAR算法由于需要設(shè)置同化時(shí)間窗口而缺少同化時(shí)間段后10天的同化值。將小麥各生育時(shí)期的LAI、β和θ同化值與實(shí)測(cè)單產(chǎn)進(jìn)行線性回歸分析,構(gòu)建單個(gè)生育時(shí)期的估產(chǎn)模型,利用熵值的組合預(yù)測(cè)法計(jì)算單個(gè)生育時(shí)期估產(chǎn)模型的權(quán)系數(shù),構(gòu)建組合估產(chǎn)模型,通過實(shí)測(cè)單產(chǎn)檢驗(yàn)和對(duì)比不同估產(chǎn)模型的精度,結(jié)果表明,在4DVAR、EnKF和PF三種算法中,基于PF算法的估產(chǎn)模型的精度大于EnKF和4DVAR算法的估產(chǎn)模型的精度。進(jìn)一步對(duì)比和分析小麥不同生育時(shí)期的LAI、β和θ同化值與實(shí)測(cè)單產(chǎn)間的相關(guān)性,選取與實(shí)測(cè)單產(chǎn)相關(guān)性較大的變量作為最優(yōu)同化變量,分別在小麥各生育時(shí)期同化雙變量(LAI和β、或LAI和θ、或θ和β)、同化多變量(LAI、β和θ)和同化最優(yōu)變量構(gòu)建估產(chǎn)模型,結(jié)果表明,在返青期θ為最優(yōu)同化變量,在乳熟期β為最優(yōu)同化變量,在拔節(jié)期和抽穗-灌漿期LAI、β和θ為最優(yōu)同化狀態(tài)量。在小麥各生育時(shí)期同化最優(yōu)變量的估產(chǎn)模型的精度(R2=0.81,RMSE=317.85 kg·ha-1)大于同化多變量的估產(chǎn)模型的精度(R2=0.76,RMSE=348.64 kg·ha-1),同時(shí)同化LAI、β和θ的估產(chǎn)模型的精度大于同化雙變量的估產(chǎn)模型的精度,且同化雙變量的估產(chǎn)模型的精度大于同化單個(gè)變量的估產(chǎn)模型的精度。因此,在作物不同生育時(shí)期同化和籽粒產(chǎn)量相關(guān)性較大的變量,能夠有效地提高作物單產(chǎn)估測(cè)精度。關(guān)中平原冬小麥地分為灌溉地和旱地,區(qū)分灌溉地和旱地的狀態(tài)變量(LAI、β和θ)與實(shí)測(cè)單產(chǎn)間的相關(guān)性大于不區(qū)分灌溉地和旱地的狀態(tài)變量與實(shí)測(cè)單產(chǎn)間的相關(guān)性,因此,在灌溉地和旱地分別構(gòu)建同化估產(chǎn)模型,其單產(chǎn)估測(cè)精度(R2=0.85,RMSE=287.55kg·ha-1)大于不區(qū)分灌溉地和旱地的估產(chǎn)模型精度。利用區(qū)分灌溉地和旱地的模型估測(cè)2007—2008年以及2013—2016年的關(guān)中平原冬小麥單產(chǎn),分析小麥單產(chǎn)的區(qū)域分布特征,結(jié)果表明,關(guān)中平原中部和西部的小麥地分布密集,且小麥平均單產(chǎn)較高;關(guān)中平原北部和東部的小麥地分布較零散,且小麥平均單產(chǎn)低于中部和西部的小麥平均單產(chǎn)。
[Abstract]:The accurate estimation of crop growth monitoring and yield prediction, is an important guarantee of food safety. The development of remote sensing technology for large area, real-time, dynamic estimation of crop yield, which provides an effective way of prediction, data assimilation algorithm coupled crop growth model and remote sensing data based on, can fully take into account the intrinsic mechanism and the process of crop growth environmental factors influence the growth and development of crops, and can effectively solve the regional crop growth model parameters acquisition problem. The Guanzhong Plain in Shaanxi Province as the study area, Landsat remote sensing data acquisition of winter wheat growth season, the calculation of normalized difference vegetation index (NDVI) by using Landsat data and inversion of vegetation temperature condition index (VTCI) analysis. NDVI and the measured leaf area index (LAI), NDVI and measured aboveground biomass (beta) and the VTCI and measured 0 ~ 20 cm soil moisture (0) and the relationship between. Build a regression model to estimate the regional LAI, beta and theta. Through field measurement and investigation of LAI, beta theta, genetic parameters of the CERES-Wheat model of wheat yield and harvest date of calibration, the simulation results of CERES-Wheat model calibration is verified, the results show that the average relative error of simulated and measured beta beta (MRE) and the simulated and measured theta theta MRE were less than 10%, the yield and yield of the simulated measured MRE less than 15%. The deviation of the simulated wheat harvest date and the actual date of less than 4 D, indicating a higher simulation accuracy of CERES-Wheat model calibration. Using the four-dimensional variational (4DVAR), a collection of Calman filter (EnKF) and particle filter (PF CERES-Wheat) algorithm assimilation model simulation and inversion of Landsat data LAI, beta and theta, to obtain the main growth stages of winter wheat LAI, and beta theta value by field measured data assimilation, respectively test and comparison of 3 algorithms of LAI assimilation, and beta 0 precision, results show that the 3 algorithms can effectively assimilate the variable expression of CERES-Wheat model based on LAI, beta and theta changes in the characteristics of wheat in different growth stages, while the influence of remote sensing variables, assimilation variables than analog variables is more close to the measured value of.PF algorithm has higher accuracy than LAI and beta assimilation EnKF and 4DVAR algorithm and the accuracy of the assimilation of LAI beta, and based on the characteristics of PF algorithm can better simulate the expression of LAI and beta, therefore, the PF algorithm and the effect of assimilation of LAI beta.EnKF optimal algorithm in the expression of LAI and beta simulation, but the effect is better than 4DVAR algorithm, EnKF algorithm, assimilation, assimilation LAI and beta is less precise than the accuracy of the.4DVAR algorithm 4DVAR algorithm LAI assimilation and beta good results in changes of expression of LAI and beta simulation, and the assimilation of LAI and beta, the precision is higher, however, because of the need to set the same 4DVAR algorithm The time window of assimilation and lack of assimilation time after 10 days. The value of wheat in different growth periods of LAI, beta and theta value assimilation by the linear regression analysis with the measured yield estimation model, construction of single growth period, yield coefficient of single period model using entropy combination forecasting method, constructs the combined estimation model through the measurement, analysis and comparison of different yield estimation model accuracy, results showed that in 4DVAR, EnKF and PF three algorithm, the estimation model estimation model PF algorithm accuracy is greater than EnKF and 4DVAR algorithm. Based on the accuracy of further comparison and analysis of different growth stages of wheat LAI, beta and theta value assimilation the correlation between yield and measured, and the measured yield significantly larger correlation variables as the optimal variables respectively in the assimilation and assimilation of wheat at different growth stages of two variables (LAI and beta, or LAI and theta, or theta and beta), and assimilation The amount of (LAI, beta and theta) and assimilation of optimal variables to construct yield estimation model, the results show that the optimal assimilation in the regreening period theta is variable in the milk stage beta is the optimal assimilation variable, in the jointing stage and heading filling LAI, beta and theta is the optimal state estimation model. The assimilation of wheat at different growth stages the precision of the optimal assimilation variables (R2=0.81, RMSE=317.85 kg HA-1) estimation model of multi variable precision than assimilation (R2=0.76, RMSE=348.64, kg, HA-1) and LAI assimilation, yield estimation model and the estimation model of beta theta assimilation accuracy is higher than the double variable precision estimation model and estimation model of a bivariate assimilation the accuracy is greater than the single variable assimilation accuracy. Therefore, the crop in different growth period of assimilation and grain yield correlated variables, can effectively improve the accuracy of crop yield estimation of Winter Wheat in Guanzhong Plain. Divided into irrigation and dry land irrigation, distinguish The state variable irrigation and dry land (LAI, beta and theta) is greater than the correlation between state variables, and the measured yield does not distinguish between irrigation and dry land between the measured and correlation between the yield estimation models were constructed, assimilation in irrigated and dry land, the yield estimation accuracy (R2=0.85, RMSE=287.55kg, HA-1) is greater than the yield estimation accuracy the model does not distinguish between irrigation and dry land. The distinction between irrigation and dryland estimation model of 2007 - 2008 and 2013 - 2016 winter wheat yield in Guanzhong Plain, the analysis of regional distribution, the results showed that the wheat yield in Guanzhong Plain, central and Western wheat distribution intensive, and wheat average yield of wheat in Guanzhong Plain in northern high; and the eastern part of the distribution is scattered, and the average yield of wheat is lower than the average yield of wheat in the central and western regions.

【學(xué)位授予單位】：中國農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：S512.11
，

本文編號(hào)：1435240