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QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize

机译:QuickBird卫星与基于地面的多光谱数据估计灌溉玉米的氮状况

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In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (E0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10-V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.
机译:灌溉玉米(Zea mays L.)的季节性氮(N)管理需要频繁获取植物氮素状态估计值,以便及时评估田间作物氮素缺乏的发生及其空间变异性。这项研究比较了地面Exotech天底视图传感器数据和QuickBird卫星多光谱数据,以评估几种绿色波段植被指数,以评估灌溉玉米的N状况。它还试图确定QuickBird多光谱图像是否可以像地面传感器系统所产生的那样准确地用于绘制N工厂状态图。归一化为参考区域(NGNDVI)的绿色归一化差异植被指数将QuickBird和Exotech数据之间的三个晴天数据采集的数据聚在一起,产生斜率,并且在统计上截距在各天以及分别分别为1和0。用于合并的数据。 NGNDVI和N充足指数的比较产生了良好的相关系数,对于V12和V15玉米生长阶段及其组合数据,相关系数在0.91至0.95之间。对于两个传感器系统,基于NGNDVI的氮充足图表明N充足(E0.96)或N不足(<0.96)玉米。对V10-V15作物生长阶段的这些N个充足图的定量评估,基于区域一致性,相似度从79%到83%不等;基于kappa统计数据,中等至基本一致。我们的研究结果表明,QuickBird卫星多光谱数据可用于评估V12及以后生长阶段灌溉的玉米N状况及其在季节内N管理领域的变异性。 NGNDVI补偿了大底偏和与频繁采集QuickBird相关的目标方位角变化的问题。

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