基于5 μm厚向列相液晶的高效圆极化相控阵系统的设计、校准和实验验证

This paper presents a systematic investigation and demonstration of a K-band circularly polarized liquidcrystal-based phased array(LCPA),including the design,over-the-air(OTA)in-array calibration,and experimental validation.The LCPA contains 16 phase-shifting radiating channels,each consisting of a circularly polarized stacked patch antenna and a liquid-crystal-based phase shifter(LCPS)based on a loaded differential line structure.Thanks to its slow-wave properties,the LCPS exhibits a maximum phase-shifting range of more than 360°with a figure of merit of 78.3(°)·dB^(-1)based on a liquid crystal layer with a thickness of only 5μm.Furthermore,an automatic OTA calibration based on a state ergodic method is proposed,which enables the extraction of the phase-voltage curve of every individual LCPA channel.The proposed LCPA is manufactured and characterized with a total profile of only 1.76 mm,experimentally demonstrating a scanned circularly polarized beam from-40°to+40°with a measured peak gain of 12.5 dBic and a scanning loss of less than 2.5 dB.The bandwidth of the LCPA,which satisfies the require-ments of port reflection(|S_(11)|)<-15 dB,an axial ratio(AR)<3 dB,beam squinting<3°,and a gain variation<2.2 dB,spans from 25.5 to 26.0 GHz.The total efficiency is about 34%,which represents a new state of the art.The use of the demonstrated low-profile LCPA to support circularly polarized scanning beams,along with the systematic design and calibration methodology,holds potential promise for a variety of millimeter-wave applications.

基于数字化植物表型平台(D3P)的田间小麦冠层光截获算法开发

冠层光截获能力是反映作物品种间差异的重要功能性状,高通量表型冠层光截获对提高作物改良效率具有重要意义。本研究以小麦为研究目标,利用数字化植物表型平台(D3P)模拟生成了100种冠层结构不同的小麦品种在5个生育期的三维冠层场景,记录了从原始冠层结构中提取的绿色叶面积指数(GAI)、平均倾角(AIA)和散射光截获率(FIPAR_(dif))信息作为真实值,进一步利用上述三维小麦场景开展了虚拟的激光雷达(LiDAR)模拟实验,生成了对应的三维点云数据。基于模拟的点云数据提取了其高度分位数特征(H)和绿色分数特征(GF)。最后,利用人工神经网络(ANN)算法分别构建了从不同LiDAR点云特征(H、GF和H+GF)输入到FIPAR_(dif)、GAI和AIA的反演模型。结果表明,对于GAI、AIA和FIPAR_(dif),预测精度从高到低对应的点云特征输入为GF+H> H> GF。由此可见,H特征对提高目标表型特性的估算精度起到了重要作用。输入GF+H特征,在中等测量噪音(10%)情况下,FIPAR_(dif)和GAI的估算均获得了满意精度,R^2分别为0.95和0.98,而AIA的估算精度(R^2=0.20)还有待进一步提升。本研究基于D3P模拟数据开展,算法的实际表现还有待通过田间数据进一步验证。尽管如此,本研究验证了D3P协助表型算法开发的能力,展示了高通量LiDAR数据在估算田间冠层光截获和冠层结构方面的较高潜力。

Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hypertemporal, and large-volume light detection and ranging(Li DAR) and multispectral data to(i) identify the best machine learning method and prediction stage for wheat yield estimation,(ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and(iii) elucidate the contribution of time-series data fusion and 3 D spatial information to yield estimation. Wheat yield could be accurately(R^(2)= 0.891) and timely(approximately-two months before harvest) estimated from fused Li DAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits(such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits(such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3 D points than from canopy surface points and from integrated multi-stage(especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and timeseries information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield.Recently,deep-learning-based object detection methods have been used for this purpose,where plant counts are estimated from the number of bounding boxes detected.However,these methods suffer from 2 issues:(a)The scales of maize tassels vary because of image capture from varying distances and crop growth stage;and(b)tassel areas tend to be affected by occlusions or complex backgrounds,making the detection inefficient.In this paper,we propose a multiscale lite attention enhancement network(MLAENet)that uses only point-level annotations(i.e.,objects labeled with points)to count maize tassels in the wild.Specifically,the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates,capturing rich contextual information at different scales more effectively.In addition,a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds.Finally,a new up-sampling module,UP-Block,is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process.Extensive experiments on 2 publicly available tassel-counting datasets,maize tassels counting and maize tassels counting from unmanned aerial vehicle,demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods.The model is publicly available at.

遥感在生物多样性研究中的应用进展

随着人口的持续增长,人类经济活动对自然资源的利用强度不断升级以及全球气候变暖,全球物种正以前所未有的速度丧失,生物多样性成为了全球关注的热点问题。传统生物多样性研究以地面调查方法为主,重点关注物种或样地水平,但无法满足景观尺度、区域尺度以及全球尺度的生物多样性保护和评估需求。遥感作为获取生物多样性信息的另一种手段,近年来在生物多样性领域发展迅速,其覆盖广、序列性以及可重复性等特点使之在大尺度生物多样性监测和制图以及评估方面具有极大优势。本文主要通过文献收集整理,从观测手段、研究尺度、观测对象和生物多样性关注点等方面综述了遥感在生物多样性研究中的应用现状,重点分析不同遥感平台的技术优势和局限性,并探讨了未来遥感在生物多样性研究的应用趋势。遥感平台按观测高度可分为近地面遥感、航空遥感和卫星遥感,能够获取样地–景观–区域–洲际–全球尺度的生物多样性信息。星载平台在生物多样性研究中应用最多,航空遥感的应用研究偏少主要受飞行成本限制。近地面遥感作为一个新兴平台,能够直接观测到物种的个体,获取生物多样性关注的物种和种群信息,是未来遥感在生物多样性应用中的发展方向。虽然遥感技术在生物多样性研究中的应用存在一定的局限性,未来随着传感器发展和多源数据融合技术的完善,遥感能更好地从多个尺度、全方位地服务于生物多样性保护和评估。

An updated Vegetation Map of China(1:1000000)

Vegetation maps are important sources of information for biodiversity conservation,ecological studies,vegetation management and restoration,and national strategic decision making.The current Vegetation Map of China(1:1000000)was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s.However,the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades,and it urgently needs to be updated to better represent the distribution of current vegetation types.Here,we describe the process of updating the Vegetation Map of China(1:1000000)generated in the 1980s using a‘‘crowdsourcing-change detection-classification-expert knowledge"vegetation mapping strategy.A total of 203,024 field samples were collected,and 50 taxonomists were involved in the updating process.The resulting updated map has 12 vegetation type groups,55 vegetation types/subtypes,and 866 vegetation formation/sub-formation types.The overall accuracy and kappa coefficient of the updated map are 64.8%and 0.52 at the vegetation type group level,61%and 0.55 at the vegetation type/subtype level and 40%and 0.38 at the vegetation formation/sub-formation level.When compared to the original map,the updated map showed that 3.3 million km^2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change.We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.

Application of deep learning in ecological resource research:Theories, methods, and challenges

Ecological resources are an important material foundation for the survival,development,and self-realization of human beings.In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society.Advances in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source data.Ecological resource research is entering a new era driven by big data.Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data.Deep learning is a method for automatically extracting complex high-dimensional nonlinear features,which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data.To promote the application of deep learning in the field of ecological resource research,here,we first introduce the relationship between deep learning theory and research on ecological resources,common tools,and datasets.Second,applications of deep learning in classification and recognition,detection and localization,semantic segmentation,instance segmentation,and graph neural network in typical spatial discrete data are presented through three cases:species classification,crop breeding,and vegetation mapping.Finally,challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning.It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms,and enrich applications with the development of hardware.

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment.Terrestrial laser scanning(TLS)is a well-suited tool to study structural rhythm under field conditions.Recent studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been drawn.Meanwhile,whether TLS can capture structural rhythm in crops is unclear.Here,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS.The seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity periods.Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions.Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage.Leaf azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard conditions.Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth.This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing

Wheat yield and grain protein content(GPC)are two main optimization targets for breeding and cultivation.Remote sensing provides nondestructive and early predictions of yield and GPC,respectively.However,whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear.In this study,we made a systematic comparison of different deep learning models in terms of data fusion,time-series feature extraction,and multitask learning.The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with R 2 values of 0.817 and 0.809.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

Plant phenomics(PP)has been recognized as a bottleneck in studying the interactions of genomics and environment on plants,limiting the progress of smart breeding and precise cultivation.High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits.Proximal and remote sensing(PRS)techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis.Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications.This progress covers most aspects of PRS application in PP,including patterns of global spatial distribution and temporal dynamics,specific PRS technologies,phenotypic research fields,working environments,species,and traits.Subsequently,we demonstrate how to link PRS to multi-omics studies,including how to achieve multi-dimensional PRS data acquisition and processing,how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance,and how to link PP to multi-omics association analysis.Finally,we identify three future perspectives for PRS-based PP:(1)strengthening the spatial and temporal consistency of PRS data,(2)exploring novel phenotypic traits,and(3)facilitating multi-omics communication.

Shortwave Radiation Calculation for Forest Plots Using Airborne LiDAR Data and Computer Graphics

Forested environments feature a highly complex radiation regime,and solar radiation is hindered from penetrating into the forest by the 3D canopy structure;hence,canopy shortwave radiation varies spatiotemporally,seasonally,and meteorologically,making the radiant flux challenging to both measure and model.