Yao Xia, Liu Xiao-Jun, Tian Yong-Chao, Cao Wei-Xing, Zhu Yan, Zhang Yu
Jiangsu Province Key Laboratory for Information Agriculture, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China.
Ying Yong Sheng Tai Xue Bao. 2013 Feb;24(2):431-7.
Using space-borne remote sensing information to monitor the crop canopy nitrogen status and crop productivity in a large-scale is of great significance and application prospect i1 modern agriculture. With the hyper-spectral reflectance data from the wheat canopy under different nitrogen fertilization levels, this paper constructed the spectral indices (including the single wavelength, ratio spectral index, and normalized difference spectral index) simulated by satellite channels, and established the nitrogen estimation equations by quantifying the relationships between the simulated channels spectral indices and the leaf nitrogen index. The results indicated that the spectral indices based on NDVI (MSS7, MSS5), NDVI (RBV3, RBV2), TM4, CH2, MODIS1, and MODIS2 could be reliably used for estimating the leaf nitrogen content (LNC), with R2 over 0.60, and the spectral indices based on NDVI (PB4, PB2), NDVI (CH2, CHl1), NDVI (MSS7, MSS5), RVI (MSS7, MSS5), MODIS1, and MODIS2 could be accurately used for predicting the leaf nitrogen accumulation (LNA), with R2 greater than 0.86. Comparatively, NDVI (MSS7, MSS5) and NDVI (PB4, PB2) could be the more suitable spectral indices for predicting the wheat canopy LNC and LNA, respectively.
利用星载遥感信息在大尺度上监测作物冠层氮素状况和作物生产力在现代农业中具有重要意义和应用前景。本文利用不同施氮水平下小麦冠层的高光谱反射率数据，构建了由卫星通道模拟的光谱指数（包括单波长、比值光谱指数和归一化差异光谱指数），并通过量化模拟通道光谱指数与叶片氮素指数之间的关系建立了氮素估算方程。结果表明，基于NDVI（MSS7，MSS5）、NDVI（RBV3，RBV2）、TM4、CH2、MODIS1和MODIS2的光谱指数能够可靠地用于估算叶片氮含量（LNC），R2超过0.60；基于NDVI（PB4，PB2）、NDVI（CH2，CHl1）、NDVI（MSS7，MSS5）、RVI（MSS7，MSS5）、MODIS1和MODIS2的光谱指数能够准确地用于预测叶片氮积累量（LNA），R2大于0.86。相比之下，NDVI（MSS7，MSS5）和NDVI（PB4，PB2）可能分别是预测小麦冠层LNC和LNA更合适的光谱指数。
