基于鲜叶光谱估测氮素营养的新植被指数(英)

采用田间试验的方法开展利用鲜叶光谱反射率估测水稻氮素营养状况的研究.基于氮素在水稻不同功能叶片之间运转规律的机理,文章重点分析了第一和第三完全展开叶红边斜率和红边位置的变化,并基于红边位置和红边斜率构建了一个新的植被指数(命名为"红边曲线肩夹角植被指数",简称为RSAVI)监测水稻氮素营养状况.为了证明RSAVI在监测水稻氮素营养状况的可行性,分析了不同生育时期氮素含量和RSAVI之间的相关性.结果表明RSAVI和叶片氮素含量显著相关,相关系数介于0.867～0.938之间.并且RSAVI和氮素含量之间建立的回归模型以多项式模型效果最佳.决定系数介于0.7512～0.8796之间,模型均通过0.01水平检验.因此研究结果表明,在本次试验中使用RSAVI估测水稻氮素营养是可行的.

New vegetation index for estimating nitrogen concentration using fresh leaf spectral reflectance

Field experiment was conducted to study the feasibility of estimating the nitrogen status of rice using fresh leaf spectral reflectance. Based on the mechanism of nitrogen transfer among different rice leaves, red edge slope and red edge position of the most upper and the 3rd expanded leaves were investigated specially. And using the two red edge parameters (red edge slope and red edge position), a new vegetation index (red edge curve shoulder angle vegetation index, abbreviation RSAVI) was calculated to detect rice nitrogen nutrition status. The correlation between nitrogen concentration and RSAVI at different rice growth stages was studied in order to prove availability of the new index determining nitrogen nutrition. The conclusion shows that RSAVI is correlated significantly with the nitrogen at leaf level and regression analysis indicates that polynomial models are creditable for determining nitrogen concentration using RSAVI at 0.01 level. The correlation coefficients(R) and determination coefficient (R2) between RSAVI and nitrogen nutrition are all large and highly significant, with the range of R from 0.867 to 0.938 and R2 from 0.7512 to 0.8796. The results support the hypothesis that RSAVI is an effective tool for estimating nitrogen concentration in this experimentation.