Day 41 利用NDVI区分小麦的基因型和生产力

Abstract: Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.

Keywords: NDVI; proximal sensor; genotypes; k-means clustering; dryland; irrigated

遥感指数如归一化差异植被指数(NDVI)对作物冠层生物量和氮的变化很敏感。本研究的目的是为了（1）确定基于近端传感器的NDVI读数能否区分冬小麦(Triticum aestivum L.)基因型的产量;（2）确定NDVI读数能否用于将小麦基因型划分为粮食产量类别。使用了K-means聚类方法来将平均NDVI和平均产量分为三个等级，并分析NDVI和产量之间的总体准确性。结果表明，当NDVI < 0.9时，NDVI可以较准确地评估粮食产量。这项研究证明了利用NDVI读数作为鉴别和鉴定优良小麦基因型的工具的潜力。

传统的培育方式很大程度上依赖产量来选择培育新的谷物多样性，在收获前对粮食产量进行无损评估，将有利于作物育种家在优等品种的选择过程中进行选择。将近红外波段(NIR:与叶片结构相关)和红色波段(与叶绿素含量相关)的差值比值归一化得到NDVI，NDVI为植被归一化指数。本研究的总体目标是评估利用近端冠层传感器测量的NDVI作为工具来区分和分类两种灌溉条件下半干旱气候下小麦基因型产量的可能性

方法

本研究使用系统无对齐网格抽样设计来获得研究区域的相关信息。

（生物学实验和我没啥关系关注一下数据分析吧）

利用皮尔逊积差相关系数(r)衡量NDVI与粮食产量之间的关系强度，确定NDVI与粮食产量之间的关系。利用三次重复实验的平均NDVI或籽粒产量将24个小麦基因型分为三类。

结论

近端冠层感知的NDVI可以作为小麦基因型产量评价的重要工具。

在旱地条件下，24个基因型的冬小麦籽粒产量与NDVI值之间存在很强的相关性。这项研究的结果表明，NDVI可以评估旱地条件下的粮食产量，但在灌溉条件下表现出局限性。此外，聚类分析的结果也可用于基于管理分区的精准农业。为了帮助农民选择最好的基因型，，还需要更多试验来证明NDVI和基因型特征之间的关系从而判断小麦生长过程中最重要的时期。

TCL碎碎念

这篇文章看似简单，只是研究了一下NDVI和小麦产量之间的关系，但是我个人觉得最大的意义在于它的实用性。这个点是很少有人会去关注但是意义非常重大的东西，他可以帮助农民在不破坏作物的情况下，选择最好的小麦基因型提高产量。但是本文的局限性也是有的，必须要检验过多个农地类型下的准确度才能让这个研究结果更好的应用于实践。