摘要
为选择合适的光谱预处理方法,提高叶片SPAD值的预测精度,本研究分析冬小麦不同叶位叶片SPAD值与光谱的响应关系,对原始光谱数据进行归一化处理(NC)、多元散射校正(MSC)和基线校正(BC)及组合处理,并结合连续投影算法(SPA)和逐步多元线性回归(SMLR)构建SPAD值光谱估测模型。结果表明:增加施氮量能提高冬小麦不同叶位叶片的SPAD值,生育前期顶二叶在近红外光谱区域反射率最大,生育后期则为顶一叶最大;单一光谱预处理中基于NC处理的光谱预测模型最好(R2=0.770、RMSE=1.483),MSC处理降低了模型预测效果,组合预处理中,以BC+NC建模效果最好,R2和RMSE分别为0.755和1.540;部分预处理在一定程度上可以消减噪音,但不当的预处理方法会降低模型的预测能力;预处理方法并不是越多越好,顺序不同预测模型精度不同。
To select the appropriate pre-treatment method and thus to improve the predictive accuracy of leaf SPAD value,the leaf spectra were processed with normalized correction（ NC）,multiple scatter correction（ MSC）,baseline correction（ BC） and their combinations. Successive projection algorithm（ SPA） and stepwise multiple linear regression（ SMLR） were used for establishing the predictive model and determining the optimal spectral pre-treatments. The results showed that increased N application rate could improve the SPAD values. The second top leaf and the first top leaf had the highest value of near-infrared spectral reflectance at early growth stage and after the flowering stage,respectively. The pre-treatment of NC method had the highest predictive model performance with R2= 0.770 and RMSE = 1.483,whereas the MSC pre-treatment resulted in poorer model performance. The validated model with the pre-treatment of BC ＋ NC combination had the best prediction（ R^2= 0. 755,RMSE = 1. 540）. Pre-treatment obviously increased the prediction; however,improper pre-treatment would reduce the prediction ability.Our results indicated the single pre-treatments or the combinations did not always improve the model performance.
作者
武改红
冯美臣
杨武德
王超
孙慧
贾学勤
张松
乔星星
WU Gai-hong,FENG Mei-chen,YANG Wu-de,WANG Chao,SUN Hui,JIA Xue-qin,ZHANG Song,QIAO Xing-xing(Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu 030801, Shanxi, China)
出处
《生态学杂志》
CAS
CSCD
北大核心
2018年第5期1589-1594,共6页
Chinese Journal of Ecology
基金
国家自然科学基金项目(31201168
31371572)
山西省科学技术发展计划项目(201603D221037-3)
山西省归国留学人员重点科研项目(2014-重点4)
山西省研究生创新项目(2017SY035)资助
