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江西鹰潭早稻关键生育期的NDVI诊断指标
引用本文:薛利红,覃 夏,李刚华,王绍华,杨林章.江西鹰潭早稻关键生育期的NDVI诊断指标[J].农业工程学报,2009,25(13):223-227.
作者姓名:薛利红  覃 夏  李刚华  王绍华  杨林章
作者单位:中科院南京土壤研究所,南京 210008;1.中科院南京土壤研究所,南京 210008; 2.南京农业大学农学院,南京 210095;南京农业大学农学院,南京 210095;南京农业大学农学院,南京 210095;中科院南京土壤研究所,南京 210008
基金项目:中国科学院知识创新工程重大项目资助(KSCX1-YW-09-08)
摘    要:确定水稻不同生育期的适宜和临界光谱诊断指标,是进行水稻生长光谱诊断从而判断施肥量的关键。该研究采取主动式的Greenseeker光谱仪,测定了不同氮肥处理下水稻早稻关键生育期的NDVI,并以水稻产量为判断依据,结合判别式分析计算了江西鹰潭早稻关键施肥期适宜的和临界NDVI诊断指标。结果表明,早稻中选181分蘖期、拔节期(穗分化期)和抽穗期的适宜NDVI值分别为0.37~0.55,0.76~0.80和0.72~0.75,其临界值分别为0.346、0.703和0.654。同品种不同新型肥料共27个小区的试验数据对NDVI临界值的检验表明,分蘖期、拔节期和抽穗期光谱诊断的正确率分别为63%(17个)、81.5%(22个)和96.3%(26个)。表明根据关键生育期的冠层NDVI 能够预测水稻的预期产量水平并用来指导施肥。但还需要开展相关试验来确定不同冠层NDVI下的适宜施肥量,建立基于NDVI的追肥模型。

关 键 词:判别式分析,遥感,光谱分析,冠层NDVI,精确施肥,诊断指标
收稿时间:2009/6/30 0:00:00
修稿时间:2009/9/12 0:00:00

Diagnostic indexes of NDVI at key growth stages for early rice at Yingtan, Jiangxi Province
Xue Lihong,Qin Xi,Li Ganghu,Wang Shaohua and Yang Linzhang.Diagnostic indexes of NDVI at key growth stages for early rice at Yingtan, Jiangxi Province[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(13):223-227.
Authors:Xue Lihong  Qin Xi  Li Ganghu  Wang Shaohua and Yang Linzhang
Abstract:Determination of the critical and optimum values of spectral diagnosis indexes at the different growth stages of rice is crucial for precise fertilization. NDVI values of early rice of different nitrogen rate treatments at key growth stages were measured using active spectrometer Greenseeker. Taken the grain yield as criteria, the optimum NDVI ranges and the critical NDVI values for early rice variety Zhongxuan 181 at key growth stages at Yingtan, Jiangxi province were determined with the discriminant analysis. The results showed that the optimum NDVI ranges at tillering, jointing and heading stages were 0.37-0.55, 0.76-0.80 and 0.72-0.75, respectively. According to the discriminant function, the critical NDVI values at tillering, jointing and heading stage were 0.346, 0.703 and 0.654, respectively. The critical NDVI values were tested using a dependent field experiments with 27 plots, and the correct classification ratios of yield based on the NDVI at tillering, jointing and heading stage were 63.0% (17 out of 27), 81.5% (22 out of 27) and 96.3% (26 out of 27), respectively. The results indicate that canopy NDVI at key growth stages can be used to forecast the yield potential level and as a guideline for fertilization determination. But further studies should be carried out to explore the optimum fertilizer rate for high yield under different NDVI values and establish the fertilization model based on the NDVI.
Keywords:discriminant analysis  remote sensing  spectrum analysis  canopy NDVI  precision fertilization  diagnosis index
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