欧亚国际

较近来自法国农业科学院的科学家以及Hiphen公司首席科学在业界先进期刊Remote Sensing of Environment上发表了Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet的文章，探讨了高精度（cm级）多光谱表型成像系统在植物表型领域的应用。文章全文请参考Remote Sensing of Environment。

北京欧亚国际科技有限公司是Hiphen公司中国区总代理，全面负责其系列多光谱表型产品在中国市场的推广、销售和售后服务。

Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet

crops

Article  in  Remote Sensing of Environment · September 2018

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 ( variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VICAB is the 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 (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.

Keywords: Chlorophyll content, Field phenotyping, Green fraction, Green area index, Nitrogen content,Remote sensing, Sugar beet, UAV.