Perich Gregor, Hund Andreas, Anderegg Jonas, Roth Lukas, Boer Martin P, Walter Achim, Liebisch Frank, Aasen Helge
Group of Crop Science, Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland.
Biometris, Wageningen University and Research Centre, Wageningen, Netherlands.
Front Plant Sci. 2020 Feb 25;11:150. doi: 10.3389/fpls.2020.00150. eCollection 2020.
Canopy temperature (CT) has been related to water-use and yield formation in crops. However, constantly (e.g., sun illumination angle, ambient temperature) as well as rapidly (e.g., clouds) changing environmental conditions make it difficult to compare measurements taken even at short time intervals. This poses a great challenge for high-throughput field phenotyping (HTFP). The aim of this study was to i) set up a workflow for unmanned aerial vehicles (UAV) based HTFP of CT, ii) investigate different data processing procedures to combine information from multiple images into orthomosaics, iii) investigate the repeatability of the resulting CT by means of heritability, and iv) investigate the optimal timing for thermography measurements. Additionally, the approach was v) compared with other methods for HTFP of CT. The study was carried out in a winter wheat field trial with 354 genotypes planted in two replications in a temperate climate, where a UAV captured CT in a time series of 24 flights during 6 weeks of the grain-filling phase. Custom-made thermal ground control points enabled accurate georeferencing of the data. The generated thermal orthomosaics had a high spatial accuracy (mean ground sampling distance of 5.03 cm/pixel) and position accuracy [mean root-mean-square deviation (RMSE) = 4.79 cm] over all time points. An analysis on the impact of the measurement geometry revealed a gradient of apparent CT in parallel to the principle plane of the sun and a hotspot around nadir. Averaging information from all available images (and all measurement geometries) for an area of interest provided the best results by means of heritability. Correcting for spatial in-field heterogeneity as well as slight environmental changes during the measurements were performed with the R package SpATS. CT heritability ranged from 0.36 to 0.74. Highest heritability values were found in the early afternoon. Since senescence was found to influence the results, it is recommended to measure CT in wheat after flowering and before the onset of senescence. Overall, low-altitude and high-resolution remote sensing proved suitable to assess the CT of crop genotypes in a large number of small field plots as is required in crop breeding and variety testing experiments.
冠层温度（CT）与作物的水分利用和产量形成有关。然而，持续变化（如太阳光照角度、环境温度）以及快速变化（如云）的环境条件使得即使在短时间间隔内进行的测量也难以比较。这给高通量田间表型分析（HTFP）带来了巨大挑战。本研究的目的是：i）建立基于无人机（UAV）的CT高通量田间表型分析工作流程；ii）研究不同的数据处理程序，将来自多个图像的信息组合成正射镶嵌图；iii）通过遗传力研究所得CT的重复性；iv）研究热成像测量的最佳时间。此外，还将该方法与其他CT高通量田间表型分析方法进行了比较（v）。该研究在一个冬小麦田间试验中进行，在温带气候下种植了354个基因型，分两个重复，在灌浆期的6周内，无人机通过24次飞行的时间序列获取CT。定制的热地面控制点实现了数据的精确地理配准。生成的热正射镶嵌图在所有时间点都具有很高的空间精度（平均地面采样距离为5.03厘米/像素）和位置精度[平均均方根偏差（RMSE）=4.79厘米]。对测量几何形状影响的分析表明，表观CT在与太阳主平面平行的方向上存在梯度，并且在天底周围有一个热点。通过遗传力，对感兴趣区域的所有可用图像（以及所有测量几何形状）的信息进行平均可提供最佳结果。使用R包SpATS对测量期间的田间空间异质性以及轻微的环境变化进行校正。CT遗传力范围为0.36至0.74。在下午早些时候发现遗传力值最高。由于发现衰老会影响结果，建议在小麦开花后且衰老开始前测量CT。总体而言，低海拔和高分辨率遥感被证明适合在作物育种和品种测试实验所需的大量小田间地块中评估作物基因型的CT。
