模拟多光谱卫星宽波段反射率的冬小麦叶片氮含量估算

Estimation of Wheat Leaf Nitrogen Content Based on Simulated Multi-spectral Broadband Reflectance

基于多年大田和小区试验下的实测小麦冠层高光谱信息,利用传感器光谱响应函数模拟Landsat 8、SPOT 6、HJ-1A、HJ-1B、GF-1和ZY-3卫星可见光-近红外波段的冠层光谱反射率,构建基于光谱指数的全生育期叶片氮含量(Leaf nitrogen concentration,LNC)估算模型。结果表明,基于不同传感器模拟的宽波段光谱反射率、光谱指数之间存在差异,但差异不显著;所有筛选的光谱指数和叶片氮含量都在P<0.01水平显著相关,基于各光谱指数所构建的全生育期叶片氮含量估算通用模型均通过显著性检验;基于综合指数(TCARI/OSAVI)、转化叶绿素吸收反射指数(TCARI)、比值植被指数(RVI)的叶片氮含量估算模型具有较高的敏感性,噪声等效误差(NE)均小于1.6,其中以TCARI/OSAVI建立的叶片氮含量估算通用模型具有最好的拟合、检验精度和适用性,模型决定系数为0.62,NE为1.26。

Crop nitrogen content estimation by remote sensing technique is a topic research in remote sensing monitoring of agricultural parameters. Monitoring of crop nitrogen content based on multi-spectral satellite data is still at the exploratory stage. Ground-based canopy spectral reflectance and leaf nitrogen content of winter wheat were measured in field,and plot experiments consisted of varied nitrogen fertilization levels and winter wheat varieties across the whole growth stage. Multi-spectral broadband reflectance data of six satellites were simulated using the measured hyper-spectral reflectances and spectral response functions of Landsat 8,SPOT 6,HJ-1A,HJ-1B,GF-1 and ZY-3. Spectral indices derived from simulated broadband spectral reflectance data across the visible and near infrared bands were used to construct the LNC estimation models. The results showed that there were no significant differences between simulated broadband reflectances and spectral indices among six satellite platforms; all the selected spectral indices were significantly related with the LNC in the whole wheat growth period and all the estimation models based on the ten spectral indices passed the significance test respectively;transformed chlorophyll absorption in reflectance index / optimized soil-adjusted vegetation index（ TCARI /OSAVI）,chlorophyll absorption in reflectance index（ TCARI） and ratio vegetation index（ RVI） were more sensitivity than the other spectral indices in LNC estimation with the noise equivalent less than 1. 6;TCARI / OSAVI was proved to be the best spectral index for LNC estimation with determination coefficient R2 of 0. 62 and noise equivalent of 1. 26.