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Estimating corn nitrogen status using ground-based and satellite multispectral data

机译:使用地面和卫星多光谱数据估算玉米氮素状况

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In-season N management of irrigated corn requires frequent acquisition of plant N estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech and QuickBird satellite multispectral data using the normalized GNDVI to produce N status maps of a study site on three days during the corn vegetative growth period. Scale factors to represent N sufficient and N deficient corn were determined for both systems from relationships between the normalized GNDVI and the NSI. A third classification was required for this study to classify areas that exhibited leaf chlorosis that was not caused by N deficiency, i.e., not N related. N status maps generated from normalized GNDVI values showed similar patterns between the two systems for the three corn growth stages (V10, V12, and V15) investigated. However, the extent of the pattern varied between systems. On 2 July (V10), six of six sample sites were correctly classified using leaf N content to indicate plant N status. On the other two days (7 July and 15 July), four of six sites were correctly classified. Two not N related areas were classified as N deficient when leaf N content was adequate.
机译:灌溉玉米的季节性N管理需要频繁获取植物N估算值,以便及时评估田间作物N缺乏的发生及其空间变异性。这项研究使用标准化的GNDVI比较了地面Exotech和QuickBird卫星多光谱数据,从而在玉米营养生长期间的三天内生成了研究地点的N个状态图。通过归一化的GNDVI和NSI之间的关系,确定了两个系统的代表N充足和N不足玉米的比例因子。这项研究需要进行第三次分类,以对表现出不是由氮缺乏引起的叶绿化的区域进行分类,即与氮无关。从归一化的GNDVI值生成的N个状态图显示,在所研究的三个玉米生长阶段(V10,V12和V15)的两个系统之间,模式相似。但是,模式的范围在系统之间有所不同。在7月2日(V10),使用叶片N含量正确分类了六个采样点中的六个,以指示植物N的状态。在其他两天(7月7日和7月15日),正确分类了六个站点中的四个。当叶片中的氮含量足够时,将两个与氮无关的区域归类为氮不足。

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