热点

背景效应影响无人机图像估算叶片氮浓度（LNC）。

背景去除削弱了水稻LNC估计中对观测时间的敏感性。

来自阳光像素的AACIre（AACIre sunlit）优于来自所有像素的AACIre。

AACIre sunlit在绿色像素方面的精度高于SAVI和CIre。 

摘要

背景效应是利用无人机（UAV）多光谱图像监测作物叶片氮浓度（LNC）的一个关键限制。为了提高LNC的估计，已经开发了一些背景去除方法，但在研究中没有对它们的性能进行比较，也不清楚它们是否对无人机图像的观测时间敏感。本研究评估了三种背景去除方法，即土壤调整植被指数法（SAVI）、绿色像素植被指数法（GPVI）和丰度调整植被指数法（AAVI），用于从基于无人机的多光谱图像中估算水稻在各个生长阶段和一天中不同观测时间的LNC。选择红边叶绿素指数（CIre）作为后两种方法的共同基础。特别是，AAVI方法经过了改进，增加了端部构件的数量，实现了端部构件的自动提取，并进一步评估了将光照部位与树冠阴影部位分离的效果。

我们的研究结果表明，非正午观测时间的植被指数（VIs）与LNC的关系在个体和整个生长阶段都优于正午观测时间的植被指数（VIs）。与SAVI和CIre green相比，AACIre for all pixels（AACIre all）对观察时间的灵敏度最弱，并且在单阶段（接合：r2=0.70，启动：r2=0.76，标题：r2=0.70）和跨阶段（r2=0.66）模型中产生了最佳关系。在三类像素衍生的AAVIs中，AACIre sunlit（R2=0.90，RMSE=0.17%，Bias=0.03%）在LNC估计精度方面显著优于AACIre all（R2=0.85，RMSE=0.23%，Bias=0.08%）和AACIre shaded（R2=0.38，RMSE=0.49%，Bias=0.40%）。这项研究表明，改进的AAVI方法在减少背景效应、更准确地监测生长参数方面具有重大价值，并可推广到其他作物和地区，以改进精确的作物管理和基于田间的高通量表型分析。

An assessment of background removal approaches for improved estimation of rice leaf nitrogen concentration with unmanned aerial vehicle multispectral imagery at various observation times 

Highlights

Background effect impacted leaf N concentration (LNC) estimation with UAV imagery.

Background removal weaked sensitivity to observation time in rice LNC estimation.

AACIre from sunlit pixels (AACIre-sunlit) outperformed AACIre from all pixels.

AACIre-sunlit yielded higher accuracies than SAVI and the CIre from green pixels.

Abstract

Background effect is a crucial limitation for the monitoring of leaf nitrogen concentration (LNC) in crops with unmanned aerial vehicle (UAV) multispectral imagery. Some background removal approaches have been developed for improve the estimation of LNC, but their performances are not compared in one study and it is unclear whether they are sensitive to the observation time of UAV imagery. This study evaluated three background removal approaches, i.e., the soil-adjusted vegetation index (SAVI) approach, the green pixel vegetation index approach (GPVI) and abundance adjusted vegetation index (AAVI), for estimating rice LNC from UAV-based multispectral imagery at individual and across growth stages as well as different observation times of the day. The red edge chlorophyll index (CIre) was chosen as the common basis for the last two approaches. In particular, the AAVI approach was refined with a higher number of endmembers and automated endmember extraction, and further evaluated for assessing the effect of separating sunlit components from shaded components of the canopy.

Our results demonstrated that the vegetation indices (VIs) for off-noon observation times showed better relationships with LNC than those for noon at individual and across growth stages. Compared to both SAVI and CIre-green, the AACIre for all pixels (AACIre-all) exhibited the weakest sensitivity to observation time and yielded the best relationships for single-stage (jointing: r2=0.70, booting: r2=0.76, heading: r2=0.70) and across-stage (r2=0.66) models. Among the AAVIs derived from three categories of pixels, the AACIre-sunlit (R2 =0.90, RMSE=0.17%, Bias=0.03%) outperformed AACIre-all (R2 =0.85, RMSE=0.23%, Bias=0.08%) and then AACIre-shaded (R2 =0.38, RMSE=0.49%, Bias=0.40%) remarkably for the estimation accuracy of LNC. This study suggests that the refined AAVI approach has great value in reducing the background effect for more accurate monitoring of growth parameters and could be extended to other crops and regions for improved precision crop management and field-based high-throughput phenotyping.