论文部分内容阅读
基于不同氮素水平与品种类型的多个田间试验,综合分析了水稻冠层高光谱植被指数与叶层氮浓度的定量关系.结果表明:对氮反应最敏感的波段为红光665～675nm、蓝光490～500nm和红边区域波段680～760nm.400～2500nm波段范围内两波段植被指数与水稻叶层氮浓度相关性最好的是550～600nm与500～550nm,属绿光波段组合,决定系数(R2)最高的是比值指数SR(533,565).以3个蓝光波段构建的光谱参数R434/(R496+R401)(蓝光氮指数)与水稻叶层氮浓度呈极显著的直线相关关系,与SR(533,565)相比,该参数显著提高了对叶层氮浓度的预测性.独立资料检验结果显示,R434/(R496+R401)对水稻叶层氮浓度具有较好的预测性,检验根均方差(RMSE)和相对误差(RE)值分别为9.67%和8%,是一种适合于水稻叶层氮浓度估测的良好高光谱植被指数.
Based on a series of field experiments with different nitrogen levels and types, the quantitative relationship between the canopyopy index and leaf nitrogen concentration in rice was comprehensively analyzed.The results showed that the most sensitive band for nitrogen was 665-675nm, The correlation between the two-band vegetation index and the leaf nitrogen concentration in the range of 490-500 nm and red-fringe region 680-760 nm in the range of 400-2500 nm is 550-600 nm and 500-550 nm, which is a combination of green band The highest coefficient (R2) was the ratio index SR (533,565). The spectral parameters R434 / (R496 + R401) (blue nitrogen index) constructed in three blue bands showed a significant linear correlation with leaf nitrogen concentration in rice SR (533, 565), this parameter significantly increased the predictability of leaf nitrogen concentration.Individual data test results showed that R434 / (R496 + R401) had a good predictive value for leaf nitrogen concentration in rice, the root mean square The RMSE and RE values were 9.67% and 8%, respectively. It was a good hyperspectral vegetation index suitable for estimating leaf nitrogen concentration in rice.