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Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2019-09-01 , DOI: 10.1016/j.rse.2018.09.011
Sylvain Jay , Frédéric Baret , Dan Dutartre , Ghislain Malatesta , Stéphanie Héno , Alexis Comar , Marie Weiss , Fabienne Maupas

Abstract The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (Beta vulgaris L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (Cab), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents. Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GFGREENPIX and VICAB) extracted from the images are shown to play a major role in these performances. GFGREENPIX is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (VARI) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VICAB is the mNDblue index value averaged over the darkest green pixels, and provides critical information on Cab. When exploited within uni- or multivariate empirical models, these two variables improve the GF, GAI, Cab, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (R2 = 0.90) are obtained by multiplying Cab and GAI estimates respectively derived from VICAB and a log-transformed version of GFGREENPIX, log(1-GFGREENPIX). The GFGREENPIX and VICAB variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.

中文翻译:

利用无人机多光谱图像的厘米分辨率改进甜菜作物冠层结构和生物化学的遥感估计

摘要 最近无人机(UAV)的出现为植被遥感开辟了新的视野,特别是在农业应用方面。然而,与从卫星或飞机获取的较粗分辨率数据相比,无人机厘米级图像的优势仍不清楚。本研究旨在 (i) 提出从无人机多光谱观测中检索冠层变量的新方法,以及 (ii) 研究使用此类厘米级图像在多大程度上可以改进糖中叶片和冠层变量的估计。甜菜作物(Beta vulgaris L.)。考虑了五个重要的结构和生化植物性状:绿色分数 (GF)、绿地指数 (GAI)、叶叶绿素含量 (Cab) 以及冠层叶绿素 (CCC) 和氮 (CNC) 含量。基于包含冠层结构和生物化学变化很大的综合数据集,针对每个目标性状获得的结果表明,厘米分辨率方法优于应用于平均值的两种标准遥感方法(即植被指数和 PROSAIL 反演)。树冠反射率。从图像中提取的两个变量(表示为 GFGREENPIX 和 VICAB)显示在这些性能中发挥着重要作用。GFGREENPIX 是通过对可见大气阻力指数 (VARI) 图像进行阈值处理获得的 GF 估计值,并且显示为甜菜冠层结构(即 GF 和 GAI)的准确和稳健(例如,针对可变光照条件)代理。VICAB 是在最深绿色像素上平均的 mNDblue 指数值,并提供有关 Cab 的关键信息。当在单变量或多变量经验模型中使用时,这两个变量改进了使用标准方法获得的 GF、GAI、Cab、CCC 和 CNC 估计,估计精度分别提高了 24、8、26、37 和 8%。例如,最好的 CCC 估计值 (R2 = 0.90) 是通过将分别来自 VICAB 的 Cab 和 GAI 估计值与 GFGREENPIX 的对数转换版本 log(1-GFGREENPIX) 相乘获得的。GFGREENPIX 和 VICAB 变量只能从厘米级图像访问,有助于更好地识别冠层结构和叶片生物化学的影响,在考虑较粗分辨率的观测时,它们的影响可能会混淆。这些结果强调了厘米级无人机图像相对于卫星或机载遥感的强大优势,
更新日期:2019-09-01
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