基于高光谱的甜菜冠层氮素遥感估算研究

利用野外便携式ASD Qualityspec光谱仪,实测了田间甜菜冠层光谱数据,采用植被指数对氮含量进行预测,发现估算精度较低,分析NDVI与VLOPT与氮含量的相关性,得出氮含量在很小的时候就达到饱和水平。根据4种预处理下的甜菜冠层光谱,分别采用偏最小二乘回归(PLSR)和主成分回归(PCR)建立甜菜氮含量估算模型,比较不同预处理和不同回归方法对估算精度的影响。结果表明:对PLSR来说,一阶导数处理的光谱数据建立的模型精度最好(RMSE=2.34g/kg,RE=19.6%),平滑、MSC和SNV建立的估算模型次之;对PCR来说,平滑处理的光谱数据建立的模型精度最好(RMSE=2.34g/kg,RE=19.4%)。总的看来,不同预处理对估算模型精度有一定的差异,但PLSR和PCR两种回归方法对甜菜氮含量估算模型影响不大。

Models of Estimating Sugar Beet Nitrogen Using Hyperspectral

This paper analyzes the beet canopy spectra under four pretreatment were used partial least squares regression ( PLSR) and principal component regression ( PCR) to establish beet nitrogen content estimation model , compare differ-ent methods of pretreatment and different regression estimation accuracy impact on PLSR , the first order derivative of the spectral data processing model established best accuracy ( RMSE=2.34g/kg, RE=19.6%), smoothing, estimation model followed by MSC and SNV established;for PCR toHe said precision spectral data smoothing model established best (RMSE=2.34g/kg,RE=19.4%).Overall, there are some different pre-treatment model to estimate the accuracy of differences , but the two regression PLSR and PCR methods to estimate the nitrogen content of beet little effect model .