以直立和平展2种株型的冬小麦品种为材料，研究了它们的光谱响应以及田间植被覆盖度的差异，探讨了利用冠层光谱反射率、光谱特征参量NDVI及植被覆盖度识别小麦株型的方法。结果表明，（1）小麦不同株型品种在近红外波段（700～1300 nm）光谱反射率有明显差异，生育前期平展型品种高于直立型品种，并以拔节期的差异为最显著，随着生育进程差异逐渐变小。拔节期是进行株型识别的最佳时期，并且此期冠层的敏感波段680 nm和760～900 nm的反射率在2种株型品种之间差异明显。（2）小麦冠层叶面积指数（LAI）与归一化差异植被指数NDVI(680,890)呈正相关，并且不同生育阶段其相关程度有差异，这是利用NDVI和植被覆盖度(COV)识别不同株型的基础。（3）相同COV条件下，直立型品种的NDVI高于平展型品种的NDVI，并且随着COV的增加，差异逐渐变小，二者的变化关系体现了直立型品种株型紧凑和平展型品种株型披散的特点，利用NDVI和COV的关系可以对株型进行识别，以小麦拔节期为最佳识别阶段，此期2种株型品种的NDVI具有显著差异（P＜0.05）。
With the application of remote sensing in estimating crop biochemical composition, the estimation precision has became a research focus. Plant-type is the main influential factor to canopy spectral due to its influence on canopy structure. It is very important to identify plant type of wheat cultivars for improving estimation accuracy. 10 winter wheat cultivars with two plant types were used to discuss their canopy spectral reflectance and the method to identify plant type using spectral characteristics and vegetation coverage (COV). The results were as follows: (1) Their canopy spectral reflectance had obvious difference in near infrared bands at earlier stages, especially at jointing stage (Fig.1). It was proved that Jointing was the best stage to identify plant-type, and in this stage there was a big differences for canopy spectral reflectance between 2 plant types in wavebands of 680 nm and 760－900 nm (Table 2). These bands lied in the position of red edge and near infrared, which were the plant canopy sensitive bands. So it was very useful to improve the measurement accuracy of leaf biochemical composition. (2) Leaf area index (LAI) was positively correlated with normal difference vegetable index [NDVI(680, 890)]. The correlation coefficient was different at different stage(Fig.3,4). The correlation between LAI and NDVI was the foundation to identify plant type by using NDVI and COV(vegetable coverage). (3）With COV increasing, NDVI was increased, but the difference between two plant types became diminishing. It reflected their plant-type characteristics, that is to say, leaves are erect and upwards in erect cultivars but horizontal and downwards in horizontal cultivars. So COV of horizontal cultivars was higher than that of erect ones under the same NDVI. On the other hand, when COV of the two cultivars was equal, NDVI of the erect leaf type varieties was higher than that of horizontal leaf type cultivars (Fig.5). As for wheat, jointing stage with low and different COV in two pant-type cultivars is the best stage to identify plant-type. There was significant difference in NDVI of erect and horizontal cultivars (P＜0.05) at jointing stage. It is feasible to know plant type information using COV and NDVI. This method is fast and non-destructive. It provides the foundation to assess plant biochemical composition quantitatively by remote sensing.
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