Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression
Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression
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DOI：
10.1017/s0021859602002320
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发表时间：
2002-11-01
影响因子：
2
通讯作者：
Thomsen, A
中科院分区：
文献类型：
--
作者：
Hansen, PM;Jorgensen, JR;Thomsen, A
通过提供空间和时间信息，遥感可作为特定地点作物管理的重要数据来源。这项技术已被用于氮肥施用策略，以获得最佳产量和谷物品质。本文的目的是利用早期重复遥感多光谱数据预测冬小麦（Triticum aestivum L.）产量和品质。和春大麦（Hordeum vulgare L.）。作物播种两种不同的播种率和广泛的氮策略。采用多因素偏最小二乘回归（N-PLS）方法对籽粒产量和蛋白质含量进行预测。使用来自最后测量日的反射率数据将结果与展开的PLS 1和PLS 1进行比较。同时使用单一的反射波长和选定的植被指数。结果表明，所有模型对两种作物的产量都有较好的预测效果，其中展开PLS 1和N-PLS模型的预测效果最好。然而，在收获时的谷物蛋白质含量的估计是非常差的确定在大麦中，反射率测量和大麦蛋白质含量之间没有关系。在小麦中，反射率测量值与蛋白质含量之间的关系稍好，特别是N-PLS改善了对籽粒蛋白质含量的预测。本实验的总体结论是，从重复测量的反射率的数据用于多向偏最小二乘回归抽穗前提高小麦和大麦的籽粒产量和蛋白质含量的预测。
By providing both spatial and temporal information remote sensing may function as an important source of data for site-specific crop management. This technology has been used for nitrogen application strategies to obtain optimum yield and grain quality. Here, the objective was to use early repeated remotely sensed multi-spectral data to predict grain yield and quality for winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). The crops were sown with two different seeding rates and a wide range of nitrogen strategies were applied. Multi-way partial least squares regression (N-PLS) was used to predict grain yield and protein content. The results were compared with unfold-PLS1 and PLS1 using reflectance data from the last measurement day. Both single reflectance wavelengths and selected vegetation indices were used simultaneously. The results reveal that all models can make a good prediction of yield in both crops with unfold-PLS1 and N-PLS as the best. However, estimation of grain protein content at harvest was very poorly determined in barley, as no relation between the reflectance measurements and barley protein content was obtained. The relation between reflectance measurements and protein content was slightly better in wheat, where especially N-PLS improved the prediction of grain protein content. The overall conclusion of the present experiments is that data from repeated measurements of reflectance used in multi-way partial least squares regression before heading improved the prediction of grain yield and protein content in wheat and barley.