Predicting Nitrogen Status of Rice Using Multispectral Data at Canopy Scale  

作　　者：ZHANG Jin-Heng WANG Ke J. S. BAILEY WANG Ren-Chao 

机构地区：[1]College of Material and Environmental Science, Qingdao University of Science and Technology, Qingdao 266042（China）. [2]Institute of Agricultural Remote Sensing＆Information System Application, Zhejiang University, Hangzhou 310029（China） [3]Department of Agriculture and Rural Development for Northern Ireland, Agricultural and Environmental Science Division, gewforge Lane, Belfast BT9 5PX （UK）

出　　处：《Pedosphere》2006年第1期108-117,共10页土壤圈（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Nos. 30070444 and 40201021)the British Council (No. SHA/992/308)the Doctor Foundation of Qingdao University of Science and Technology.

摘　　要：Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR (near-infrared) ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS (space imaging) bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index (RVI) and normalized difference vegetation index (NDVI) for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.Two field experiments were conducted in Jiashan and Yuhang towns of Zhejiang Province, China, to study the feasibility of predicting N status of rice using canopy spectral reflectance. The canopy spectral reflectance of rice grown with different levels of N inputs was determined at several important growth stages. Statistical analyses showed that as a result of the different levels of N supply, there were significant differences in the N concentrations of canopy leaves at different growth stages. Since spectral reflectance measurements showed that the N status of rice was related to reflectance in the visible and NIR （near-infrared） ranges, observations for rice in 1 nm bandwidths were then converted to bandwidths in the visible and NIR spectral regions with IKONOS （space imaging） bandwidths and vegetation indices being used to predict the N status of rice. The results indicated that canopy reflectance measurements converted to ratio vegetation index （RVI） and normalized difference vegetation index （NDVI） for simulated IKONOS bands provided a better prediction of rice N status than the reflectance measurements in the simulated IKONOS bands themselves. The precision of the developed regression models using RVI and NDVI proved to be very high with R2 ranging from 0.82 to 0.94, and when validated with experimental data from a different site, the results were satisfactory with R2 ranging from 0.55 to 0.70. Thus, the results showed that theoretically it should be possible to monitor N status using remotely sensed data.

关 键 词：canopy spectral reflectance  multispectral data  nitrogen status RICE vegetation indices 

分 类 号：S511[农业科学—作物学]