基于角果期高光谱的冬油菜产量预测模型研究  

Prediction Models of Winter Oilseed Rape Yield Based on Hyperspectral Data at Pod-filling Stage

作　　者：李岚涛 任涛[1,2] 汪善勤 明金 刘秋霞[1,2] 鲁剑巍[1,2] 

机构地区：[1]华中农业大学微量元素研究中心,武汉430070 [2]农业部长江中下游耕地保育重点实验室,武汉430070

出　　处：《农业机械学报》2017年第3期221-229,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金项目(31471941);国家油菜产业体系建设专项(CARS-13)

摘　　要：以连续3 a田间氮肥水平试验为基础,研究基于高光谱估产的可行性,明确最佳光谱监测方式和有效波段,降低光谱分析维数,提高产量估测时效性。2013—2016年分别于湖北省武穴市和沙洋县进行大田试验,通过测试角果期冠层光谱反射率、产量构成因子(单株角果数、每角粒数和千粒质量)和成熟期产量,利用偏最小二乘回归(PLS)分别对油菜原初光谱(RSR)和一阶微分光谱(FDR)与其产量及构成因子间构建定量分析模型并筛选有效波段。结果表明,基于全波段的FDR-PLS模型预测精度显著优于R-PLS,其最佳监测指标是冬油菜产量和角果数,验证集决定系数(R2)分别为0.90和0.91,均方根误差(RMSE)分别为379 kg/hm2和66个/株,相对分析误差(RPD)分别为3.11和3.12。基于各波段变量重要性投影(VIP)值,确定冬油菜产量有效波段分别为628、753、882、935、1061、1 224 nm;角果数有效波段分别为628、758、935、1 063、1 457、1 600 nm。此后,再次构建基于上述有效波段的冬油菜产量和角果数监测模型,决定系数分别为0.91和0.87,均方根误差分别为504 kg/hm2和82个/株,相对分析误差分别为2.34和2.52,估算精度较为理想。Hyperspectral remote sensing can provide a non-destructive and effective approach for assessing the yield and yield components of oilseed rape timely. A quantitative technique was developed to estimate oilseed rape yield accurately depending on ground-based canopy reflectance spectra. Field experiments were conducted over three growing seasons at different sites （ Wuxue and Shayang） in Hubei Province, China. The key parameters, including canopy hyperspectral reflectance during pod-filling period, seed yield and yield components （pod numbers per plant, seed numbers per pod and 1 000 seed weight） were monitored. A partial least square （PLS） regression analysis was employed to perform the relationship between raw spectral reflectance （RSR）, the first derivative reflectance （FDR） and seed yield and yield components. According to the calibration dataset, the best results were obtained with the FDR -PLS model for the prediction of yield and pod number, which yielded the highest coefficient of determination （R^2 cal） of 0.96 and 0.98, and the lowest root mean square error （RMSEcal） of 158 kg/hm^2 and 17 pods/plant, respectively. The tests using the independent validation dataset also showed that the FDR - PLS model could well forecast yield and pod number of winter oilseed rape, with values of R^2val of 0.90 and 0.91, RMSEval of 379 kg/hm^2 and 66 pods/plant, and RPD of 3.11 and 3.12, respectively. The variable importance in projection （VIP） scores resulted from the PLS regression analysis were used to determine the effective wavelengths and reduce the dimensionality of the spectral reflectance data. The newly-developed FDR - PLS model using the effective wavelengths （628 nm, 753 nm, 882 nm, 935 nm,1 061 nm and 1 224 nm） performed well in yield prediction with Rcal of 0.91 , RMSEval of 504 kg/hm2 and RPDval of 2. 34; Similar results were also obtained for pod number prediction with R^2val, of 0. 87, RMSEval, of 82 pods/plant and RPDval of 2.52 using the effective wavelengths （628 nm

关 键 词：冬油菜 角果期 产量 预测模型 高光谱 偏最小二乘回归 

分 类 号：S565.4[农业科学—作物学] S127