本文關(guān)鍵詞： 作物生長模型 遙感數(shù)據(jù)同化 葉面積指數(shù) 不確定性 估產(chǎn)　出處：《中國農(nóng)業(yè)科學院》2016年博士論文　論文類型：學位論文

【摘要】：及時、準確、大范圍地進行區(qū)域作物生長監(jiān)測和產(chǎn)量預測對于指導農(nóng)業(yè)生產(chǎn)、保障糧食安全、促進農(nóng)業(yè)可持續(xù)發(fā)展具有重要意義。作物生長模型遙感數(shù)據(jù)同化方法是解決區(qū)域作物生長模擬的有效途徑。但作物生長模擬遙感數(shù)據(jù)同化模擬過程復雜,在實際應用中還存在很多不確定因素。深入研究和分析這些不確定性問題對作物模型區(qū)域化、提高區(qū)域同化模擬的精度具有重要理論研究價值和實際應用需求,有助于提高區(qū)域農(nóng)情遙感監(jiān)測的技術(shù)能力。本研究以作物模型遙感數(shù)據(jù)同化的不確定性為研究核心,從同化系統(tǒng)模型初始條件和模擬過程、同化算法、關(guān)鍵參數(shù)葉面積指數(shù)(LAI)遙感反演、氣象驅(qū)動及遙感觀測誤差、時空尺度等方面出發(fā),首先在作物模型本地化基礎(chǔ)上,分別考慮模型模擬過程和初始條件擾動的不同模擬效果;然后,對比粒子濾波(PF)和本特征正交分解的四維變分(POD4DVAR)同化模擬結(jié)果,分析粒子/集合維數(shù)和擾動方差影響;接著,利用GF-1 WFV、HJ-1 CCD和Landsat-8 OLI影像數(shù)據(jù),應用PROSAIL模型進行時序LAI遙感反演;最后進行區(qū)域同化模擬,并分析氣象驅(qū)動、觀測誤差、時空尺度等方面的不確定性影響。主要研究結(jié)論如下:(1)通過對比初始條件優(yōu)化、PF同化和初始條件擾動同步PF同化三種同化方案,得出第一種方案未考慮同化模擬過程不確定性,無法提高同化估產(chǎn)精度,其余方案估產(chǎn)精度較高,其中第三種同化方案最優(yōu),相對誤差(RE)和均方根誤差(RMSE)分別為6.00%和544kg/ha。研究發(fā)現(xiàn)觀測誤差的增加會使同化模擬精度降低;還發(fā)現(xiàn)同化時間點的選擇對同化模擬的精度具有影響,在冬小麥孕穗期、抽穗期和拔節(jié)期同化觀測數(shù)據(jù)可明顯提高同化模擬精度。(2)PF和POD4DVAR兩種同化方案均可以提高模擬精度。其中以POD4DVAR同化精度最高,RE為5.65%,RMSE為523kg/ha。研究發(fā)現(xiàn)粒子/集合維數(shù)從50增加到200時,同化模擬精度提高較小,但計算代價增加超過8倍;隨著擾動方差降低,同化模擬精度增大。因此,在實際應用中選擇合適同化算法、粒子/集合數(shù)和擾動方差,對于減少冬小麥生長同化模擬至關(guān)重要作用。(3)研究利用同步的GF-1 WFV、HJ-1 CCD和Landsat-8 OLI數(shù)據(jù),在分析反射率和植被指數(shù)一致性的基礎(chǔ)上,應用PROSAIL模型反演得到衡水冬小麥時序LAI結(jié)果,經(jīng)人工和儀器測量兩種實測LAI驗證,總體RE分別為5.72%和9.44%,RMSE為0.26和0.39,表明時序LAI反演結(jié)果滿足區(qū)域同化研究需求。(4)研究基于最優(yōu)POD4DVAR同化方案進行區(qū)域同化估產(chǎn),利用官方統(tǒng)計數(shù)據(jù)進行驗證,估產(chǎn)精度較高,RE為8.32%,RMSE為452kg/ha。分析氣象驅(qū)動不確定性影響,發(fā)現(xiàn)單氣象站數(shù)據(jù)同化結(jié)果,無法反映各區(qū)域?qū)嶋H產(chǎn)量變化特性。研究還發(fā)現(xiàn)隨著遙感LAI誤差增大,估產(chǎn)精度降低,但減少趨勢較小,同化在一定程度上消減了部分觀測誤差影響造成的同化模擬不確定性。時空尺度方面,同化觀測頻率增大,模擬精度提高;同化孕穗、抽穗和拔節(jié)三個生育期觀測均可明顯提高模擬精度;同化前、中期物候階段觀測也可顯著提高估產(chǎn)精度。遙感觀測同化空間分辨率降低,模擬精度降低,但計算效率提高。因此需綜合考慮估產(chǎn)精度和計算代價,選擇合理的時空尺度,以滿足實際區(qū)域冬小麥生同化估產(chǎn)需求。
[Abstract]:Timely, accurate, large range of regional crop growth monitoring and yield prediction to guide agricultural production, food security, is of great significance to promote the sustainable development of agriculture. The crop growth model of remote sensing data assimilation method is an effective way to solve the regional crop growth simulation. But the crop growth simulation of remote sensing data assimilation process is complex, in the practical application there are still many uncertain factors. Research and analysis of these uncertain problems of regional crop model, has important theoretical value and practical application needs to improve the Regional Assimilation and the accuracy of the simulation, is helpful to improve the technical ability of regional agricultural remote sensing monitoring. This study focusing on crop model assimilation of remote sensing data uncertainty. From the initial conditions of model assimilation system and the simulation process, assimilation algorithm, key parameters of leaf area index (LAI) remote sensing inversion, The driving meteorological and remote sensing observation error, spatial scales and other aspects, first in the localization based on crop model, considering the model simulation of different simulation results of disturbance process and the initial conditions; then, compared with particle filter (PF) and the orthogonal decomposition feature of four-dimensional variational assimilation (POD4DVAR) simulation results, analysis of the particle / dimension set and then, the effect of perturbation variance; GF-1 WFV, HJ-1 CCD and Landsat-8 OLI image data, PROSAIL model was applied to temporal LAI remote sensing inversion; finally, Regional Assimilation and simulation, and analysis of meteorological observation error, drive, and other aspects of the impact of spatial and temporal scales of uncertainty. The main conclusions are as follows: (1) by comparing the optimized initial conditions PF, assimilation and initial condition perturbations three assimilation scheme of synchronous PF assimilation, that the first scheme does not consider the assimilation process uncertainty, to improve the estimating accuracy of assimilation, The scheme of estimating precision is higher, of which third kinds of optimal assimilation scheme, the relative error (RE) and the root mean square error (RMSE) were 6% and 544kg/ha. study found increasing observation error will reduce the precision of the model is that assimilation; assimilation time point selection has an impact on the assimilation of simulation accuracy in winter wheat at booting stage observation, data assimilation heading stage and jointing stage can significantly improve the simulation accuracy of assimilation. (2) two PF and POD4DVAR assimilation scheme can improve the simulation precision. The POD4DVAR assimilation of the highest accuracy, RE 5.65%, RMSE 523kg/ ha. found that the particle / set dimension increased from 50 to 200, the smaller increase assimilation the accuracy, but the computational cost increases more than 8 times; with the perturbation variance reduction, assimilation simulation accuracy increases. Therefore, choosing appropriate Assimilation Algorithm in practical application, the particle / set number and perturbation variance, to reduce the winter Simulation of vital wheat growth. Assimilation (3) by GF-1 WFV HJ-1 and Landsat-8 CCD synchronization, OLI data, based on the analysis of reflectance and vegetation index consistency, PROSAIL model is applied to inversion of Winter Wheat in Hengshui time LAI results by artificial and instrument measuring two kinds of measured LAI verification, overall RE was 5.72% and 9.44%, RMSE was 0.26 and 0.39, indicating that the timing of LAI inversion results meet the requirements of Regional Assimilation research. (4) research on Regional Assimilation Scheme Based on the optimal estimation of POD4DVAR assimilation, verified by the official statistics, estimation accuracy, RE 8.32%, RMSE 452kg/ha. analysis of weather driven uncertainty, found data assimilation results the weather station, cannot reflect the actual change of output characteristics of each region. The study also found that with the increase of LAI remote sensing error, estimation accuracy decreases, but the decreasing trend is small, in a certain range of assimilation The degree by assimilation effects caused by partial observation uncertainty of error. Temporal and spatial scales, assimilation frequency increases, improve simulation accuracy; assimilation and jointing booting, heading three growth period of observation can significantly improve simulation accuracy; assimilation, mid stage phenological observations can significantly improve the estimation accuracy. The remote sensing spatial assimilation lower resolution, simulation accuracy is reduced, but the calculation efficiency is improved. Therefore the need to consider the estimation accuracy and computational cost, reasonable selection of temporal and spatial scales, to meet the actual regional winter wheat production yield assimilation needs.

【學位授予單位】：中國農(nóng)業(yè)科學院
【學位級別】：博士
【學位授予年份】：2016
【分類號】：S127;S512.11
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本文編號：1554683