农业工程学报
農業工程學報
농업공정학보
Transactions of the Chinese Society of Agricultural Engineering
2015年
20期
147-156
,共10页
李岚涛%马驿%魏全全%汪善勤%任涛%李小坤%丛日环%王振%王少华
李嵐濤%馬驛%魏全全%汪善勤%任濤%李小坤%叢日環%王振%王少華
리람도%마역%위전전%왕선근%임도%리소곤%총일배%왕진%왕소화
光谱%氮%模型%植株氮素积累量%高光谱参数%最优光谱比值
光譜%氮%模型%植株氮素積纍量%高光譜參數%最優光譜比值
광보%담%모형%식주담소적루량%고광보삼수%최우광보비치
hyperspectral%nitrogen%models%plant N accumulation%spectral parameters%optimum reflectance ratios
为无损和定量研究高光谱技术在冬油菜植株氮素积累量(PNA, plant nitrogen accumulation)时空变化监测的适宜性及准确性,该文以两年田间氮肥水平试验为基础,采用单变量线性和非线性回归方法,建立基于特征光谱参数的冬油菜P NA高光谱估算模型.结果表明,采用比值光谱的方法可显著提高冬油菜冠层光谱反射率与PNA间的相关性,其最佳的波段组合为1 259 nm与492 nm处光谱反射率比值(R1259/R492),决定系数R2为0.85.高光谱参数间,以比值植被指数(RVI-5)、归一化光谱指数(NDSI)、线性内插法红边位置(REIP)、三角植被指数(TVI)、742 nm处一阶微分光谱值(FD742)和红边面积(SDR)等光谱参数与PNA相关性较好(平均R2和标准误SE分别为0.69和42.70),且以FD742表现最优(R2=0.79,SE=35.66).精度分析结果显示,以光谱参数R1259/R492和FD742为自变量的指数方程模型作为高光谱监测油菜PNA的最佳模型,各生育期Noise Equivalent(NE)均较低且表现稳定,同时模型估测精度较高,R2分别为0.98和0.98,相对均方根误差RRMSE分别为0.73和0.72,相对误差MRE分别为14.42%和10.31%.该方法为快捷和精确评估冬油菜PNA提供了新的研究思路.
為無損和定量研究高光譜技術在鼕油菜植株氮素積纍量(PNA, plant nitrogen accumulation)時空變化鑑測的適宜性及準確性,該文以兩年田間氮肥水平試驗為基礎,採用單變量線性和非線性迴歸方法,建立基于特徵光譜參數的鼕油菜P NA高光譜估算模型.結果錶明,採用比值光譜的方法可顯著提高鼕油菜冠層光譜反射率與PNA間的相關性,其最佳的波段組閤為1 259 nm與492 nm處光譜反射率比值(R1259/R492),決定繫數R2為0.85.高光譜參數間,以比值植被指數(RVI-5)、歸一化光譜指數(NDSI)、線性內插法紅邊位置(REIP)、三角植被指數(TVI)、742 nm處一階微分光譜值(FD742)和紅邊麵積(SDR)等光譜參數與PNA相關性較好(平均R2和標準誤SE分彆為0.69和42.70),且以FD742錶現最優(R2=0.79,SE=35.66).精度分析結果顯示,以光譜參數R1259/R492和FD742為自變量的指數方程模型作為高光譜鑑測油菜PNA的最佳模型,各生育期Noise Equivalent(NE)均較低且錶現穩定,同時模型估測精度較高,R2分彆為0.98和0.98,相對均方根誤差RRMSE分彆為0.73和0.72,相對誤差MRE分彆為14.42%和10.31%.該方法為快捷和精確評估鼕油菜PNA提供瞭新的研究思路.
위무손화정량연구고광보기술재동유채식주담소적루량(PNA, plant nitrogen accumulation)시공변화감측적괄의성급준학성,해문이량년전간담비수평시험위기출,채용단변량선성화비선성회귀방법,건립기우특정광보삼수적동유채P NA고광보고산모형.결과표명,채용비치광보적방법가현저제고동유채관층광보반사솔여PNA간적상관성,기최가적파단조합위1 259 nm여492 nm처광보반사솔비치(R1259/R492),결정계수R2위0.85.고광보삼수간,이비치식피지수(RVI-5)、귀일화광보지수(NDSI)、선성내삽법홍변위치(REIP)、삼각식피지수(TVI)、742 nm처일계미분광보치(FD742)화홍변면적(SDR)등광보삼수여PNA상관성교호(평균R2화표준오SE분별위0.69화42.70),차이FD742표현최우(R2=0.79,SE=35.66).정도분석결과현시,이광보삼수R1259/R492화FD742위자변량적지수방정모형작위고광보감측유채PNA적최가모형,각생육기Noise Equivalent(NE)균교저차표현은정,동시모형고측정도교고,R2분별위0.98화0.98,상대균방근오차RRMSE분별위0.73화0.72,상대오차MRE분별위14.42%화10.31%.해방법위쾌첩화정학평고동유채PNA제공료신적연구사로.
Quick, non-destructive and accurate monitoring and diagnosis of plant nitrogen accumulation (PNA) is important for site-specific N management in winter oilseed rape production. To develop a method for determining PNA of winter oilseed rape (Brassica napusL.) with the hyperspectral techniques, field experiments were carried out for two growing seasons (2013-2014 and 2014-2015) at Meichuan town (30°06′47′′ N, 115°35′35′′ E), Hubei province, China. Rapeseed cultivar of Huayouza No. 9 (with low glucosinolate and erucic acid concentrations) was chosen as the test cultivar. Five N (as urea) fertilization rates were applied in the 2013-2014 growing season, i.e., 0 (N0), 90 (N90), 180 (N180), 270(N270) and 360 kg/hm2(N360). Additionally, for further examining the effects of N status on crop growth and spectral reflectance characteristics, three additional N rates, 45 (N45), 135 (N135) and 225 kg/hm2 (N225) were applied in the 2014-2015 growing season. Canopy hyperspectral reflectance and PNA under different N application rates at seedling, budding and flowering stage during the two growing seasons were measured separately using a Field Spec Pro spectrometer (Analytical Spectral Devices Inc. (ASD), Boulder, CO, USA) and chemical assays in the laboratory. Using linear and nonlinear regression methods, the estimate model for PNA of winter oilseed rape was built on the basis of the experiment data in 2013-2014 acted as training data set, and its precision had been evaluated and tested based on the experiment data in 2014-2015 acted as testing data set. The coefficient of determination (R2), relative root mean square error (RRMSE) and mean relative error (MRE) were used to evaluate the fitness between observed and predicted PNA values. The following sensitivity analysis method, Noise Equivalent (NE) model was calculated to assess the sensitivity of the optimal spectral parameters for detecting changes in PNA across different growth stages. The results indicated that PNA in winter oilseed rape increased with N fertilization rates, and changes in canopy hyperspectral reflectance under varied N rates were all highly significant and consistent in patterns across different growth stages and years. Compared with single reflectance measures, the simple reflectance ratio was more satisfied with its sound correlations with the PNA. PNA were highly and linearly correlated with spectral reflectance ratio of 1 259 nm and 492 nm (R1259/R492) with the highestR2 values (0.850). Upon the analysis the linear and nonlinear (logarithm, parabola, power and exponential) regression models for PNA estimation, and the selected optimal spectral parameters, e.g., ratio vegetation index-5 (RVI-5), normalized difference spectral index (NDSI), red-edge position with linear interpolation method (REIP), triangle vegetation index (TVI), first derivative of the reflectance spectra at the given wavelength at 742 nm (FD742) and the sum of first derivative with the red-edge region (SDR) had a good correlation with PNA (averagedR2 and standard error (SE) was 0.69 and 42.70, respectively), and the best spectral parameter was FD742 (R2=0.79,SE=35.66). Based on the results of precision analysis, the model in which the optimum reflectance ratios (R1259/R492) and first derivative of the reflectance spectra at the given wavelength at 742 nm (FD742) as variables would be perfect of estimating PNA of winter oilseed rape using hyperspectral techniques. The two spectral parameters had the relative lower Noise Equivalent (NE) values and would be not affected by growing stages. The model estimation accuracy was high, theR2 values were 0.98 and 0.98, respectively, the RRMSE values were 0.73 and 0.72, and the MRE values were 14.42 % and 10.31 %, respectively. The overall results indicate that the PNA of winter oilseed rape could be reliably estimated with the canopy hyperspectral methods established in this study.
