NIR-red spectral space based new method for soil moisture monitoring

Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.

基于NIR-Red光谱特征空间的作物水分指数

水分含量是表征作物水分胁迫生理状况的重要指标,及时有效地监测作物水分含量对于评估作物水分亏缺平衡,指导农业生产灌溉具有重要意义。基于NIR-Red二维光谱特征空间,尝试构建一种新的作物水分监测指数PWI来估算作物水分含量。以冬小麦作物植被水分含量估算为尝试对象。首先,利用地面实测小麦冠层高光谱数据,结合对应卫星光谱响应函数,模拟当前常用卫星HJ-CCD和ZY-3多光谱数据;然后,对基于NIR-Red二维光谱特征空间的现有植被指数PDI(垂直干旱指数)和PVI(垂直植被指数)进行改进,通过比值变换的方法构建新的指数PWI来估算冬小麦植株含水量(VWC)。结果显示:基于模拟的HJ-CCD和ZY-3卫星宽波段多光谱数据生成的PWI估算小麦VWC具有良好的效果,R2分别达到0.684和0.683,均达到了极显著水平。利用检验样本得到冬小麦VWC估算的R2和RMSE分别为0.764和0.764,3.837%和3.840%,这表明应用提出的新指数PWI估测作物含水量具有一定可行性。同时,也为当前利用主要国产卫星遥感数据HJ-CCD和ZY-3开展作物水分遥感监测应用提供了一种新方法。

Low-dimensional multi-spectral space for color reproduction based on nonnegative constrained principal component analysis

In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.

一种基于NIR-RED光谱特征空间的干旱监测新方法

土壤水分作为干旱监测的重要指标,一直是干旱遥感监测研究的重要内容。本研究利用MODIS数据的EVI、红波段反射率、近红外波段反射率数据构建了基于Nir-Red反特率光谱特征空间的EPDI模型进行土壤水分的反演。利用野外同步测量数据对PDI、MPDI、EPDI三种干旱指数模型获取的拉萨河流域土壤水分进行了验证和对比分析,研究结果表明EPDI能够更准确地反演土壤水分,其样本点的相对误差仅为0.1040,线性相关系数为0.9181,反演精度相比PDI、MPDI(0.1646、0.1472)分别提高了36.83%和29.35%,为利用遥感影像数据进行大尺度的干旱动态监测提供了新途径。并且相比MPDI,EPDI模型参数更容易获取,模型构建受人为因素影响小,从而为模型的大范围推广提供了可能,具有很好的应用意义。

Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.

DP-DQPSK Optical Access System Integrated with Fiber and Free-Space Downlink for High Spectral Efficiency Application

In this letter, we present the generation, the balanced detection, and the transmission performance evaluation of dual polarization differential quadrature phase shift keying (DP-DQPSK) signals in optical access system integrated with fiber and free-space downlink. Polarization-multip- lexed (POLMUX) technique is introduced in the system for high spectral efficiency access utilization. 10 Gb/s DP-DQPSK downlink signals are successfully transmitted over 50 km SMF-28 and a 800 m wireless optical channel under the bad weather condition, such as fog and haze. The results show that the potentiality of DP-DQPSK optical access system is integrated with fiber and free- space downlink for providing flexible user access with high bandwidth efficiency.

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively provide information on soil salinity.

UNIVERSAL INTERPOLATING SEQUENCES ON SPACES OF ANALYTIC FUNCTIONS

This article derives the relation between universal interpolating sequences and some spectral properties of the multiplication operator by the independent variable z in case the underlying space is a Hilbert space of functions analytic on the open unit disk.

腰骶丛神经根成像中T23D SPACE序列的技术优化研究

目的:探讨T23D SPACE序列在腰骶丛神经根中的应用及其参数优化。方法:前瞻性招募并收集46名正常志愿者的临床与影像资料,所有志愿者均行常规T23D SPACE(方法A)、优化后T23D SPACE(方法B)磁共振扫描,并比较2种方法的扫描时间,第4~5腰神经、第1~4骶神经、坐骨神经的神经显示情况,第5腰神经根节中、节前、节后的信噪比(SNR)、对比度噪声比(CNR)_(神经-肌肉)、对比度(CR)_(神经-肌肉值)。优化后T23D SPACE序列(方法B)主要优化了TR、TE、加速因子(回波链)、脂肪抑制方式、血液抑制方式、层厚等参数。结果:(1)方法A扫描时间为356 s,方法B扫描时间为229 s。(2)在神经显示评分比较中,第4、5腰神经,第1~4骶神经和坐骨神经的显示评分在2种方法之间差异无统计学意义。(3)第5腰神经根的节中SNR值(286.842±75.822 vs 376.784±111.880)、CNR_(神经-肌肉值)(389.199±106.824 vs 522.683±159.883)、CR_(神经-肌肉值)(0.798±0.037 vs 0.830±0.038)在2种方法间比较,差异均有统计学意义(P<0.05)。(4)第5腰神经根的节前SNR值(198.758±52.966 vs 260.378±79.631)、CNR_(神经-肌肉值)(254.720±74.904 vs344.948±112.041)、CR_(神经-肌肉值)(0.718±0.070 vs 0.762±0.056)在2种方法间比较,差异均有统计学意义(P<0.05)。(5)第5腰神经根的节后SNR值(161.400±46.883 vs 206.849±59.706)、CNR_(神经-肌肉值)(197.684±63.776 vs 263.240±80.910)、CR_(神经-肌肉值)(0.663±0.068 vs 0.711±0.058)在2种方法间比较,差异均有统计学意义(P<0.05)。结论:常规T23D SPACE序列与优化后的序列均可以显示腰骶神经,优化后扫描更快,神经显示中第5腰神经根的信噪比、对比度均比常规T23D SPACE序列高。

Power and Cross-Spectra for the Turbulent Atmospheric Motion and Transports in the Domain of Wave Number Frequency Space: Theoretical Aspects

The study of large-scale atmospheric turbulence and transport processes is of vital importance in the general circulation of the atmosphere. The governing equations of the power and cross-spectra for the atmospheric motion and transports in the domain of wave number frequency space have been derived. The contributions of the nonlinear interactions of the atmospheric waves in velocity and temperature fields to the conversion of kinetic and potential energies and to the meridional transports of angular momentum and sensible heat in the atmosphere have been discussed.

Efficiency Improvement of Space Time Block Codes

Unlike most of the existing methods in Space Time coding (STC) system which focus on design of STC gaining full rate and/or maximum diversity, we propose an approach to improve spectral efficiency of the code. The proposed scheme carries more information symbols in each transmission block as compare to its counterpart code, and yet retains the property of simple decoding. Simulation results show that transmit diversity is retained with improvement of code efficiency. We mainly focus on Four transmit antenna scheme but it can be generalized for any number of transmit antennas.

Mitigation of spectral leakage for single carrier, block-processing cognitive radio receivers

Cognitive Radio （CR） is a multiuser, wireless communication concept that allows for a dynamic and adaptive assignment of spectral resources. The coexistence of multiple users, often transmitting at significantly different power levels, makes CR receivers vulnerable to spectral leakage caused by components＇ nonlinearities and timetruncation of the processed signal records. In this work we propose a method for mitigating the latter with an adaptive choice of the length of the processing block size. With simulations we show that a significant leakage reduction that leads to receiver dynamic range improvement of around 10 dB can be achieved with the proposed method.

A Unified Petrov-Galerkin Spectral Method and Fast Solver for Distributed-Order Partial Differential Equations

Fractional calculus and fractional-order modeling provide effective tools for modeling and simulation of anomalous diffusion with power-law scalings.In complex multi-fractal anomalous transport phenomena,distributed-order partial differential equations appear as tractable mathematical models,where the underlying derivative orders are distributed over a range of values,hence taking into account a wide range of multi-physics from ultraslow-to-standard-to-superdiffusion/wave dynamics.We develop a unified,fast,and stable Petrov–Galerkin spectral method for such models by employing Jacobi poly-fractonomials and Legendre polynomials as temporal and spatial basis/test functions,respectively.By defining the proper underlying distributed Sobolev spaces and their equivalent norms,we rigorously prove the well-posedness of the weak formulation,and thereby,we carry out the corresponding stability and error analysis.We finally provide several numerical simulations to study the performance and convergence of proposed scheme.

Flavour and Colour of Quarks in Spin Topological Space

An assumption that all the six flavour quarks are attributed to be the components of a same, a common isospin multiplets space named STS is proposed. Base on Pauli Exclusion Principle, every quark is assigned to different flavour marks in STS. Every flavour quark possesses its own colour spectral line array specially appointed. The collection of colour spectral line arrays of the six flavour quarks constructs together the CSDF, Colour Spectrum Diagram of Flavour, further baryons and mesons could be constructed from CSDF. STS, Spin Topological Space is a math frame with infinite dimensional matrix representation for spin angular momentum. Flavours is an isospin angular momentum coupling phenomena of the three-colour-quarks.

Performance of Cross-Layer Design with Power Allocation in Space-Time Coded MIMO Systems

A cross-layer design(CLD)scheme with combination of power allocation,adaptive modulation(AM)and automatic repeat request(ARQ)is presented for space-time coded MIMO system under imperfect feedback,and the corresponding system performance is investigated in a Rayleigh fading channel.Based on imperfect feedback information,a suboptimal power allocation(PA)scheme is derived to maximize the average spectral efficiency(SE)of the system.The scheme is based on a so-called compressed SNR criterion,and has a closed-form expression for positive power allocation,thus being computationally efficient.Moreover,it can improve SE of the presented CLD.Besides,due to better approximation,it obtains the performance close to the existing optimal approach which requires numerical search.Simulation results show that the proposed CLD with PA can achieve higher SE than the conventional CLD with equal power allocation scheme,and has almost the same performance as CLD with optimal PA.However,it has lower calculation complexity.

星载紫外相机空间目标探测能力研究

卫星在发生由太阳翼导致的季节性闪烁时反射光谱会向短波偏移,波长更短的紫外段在空间目标太阳翼探测中具有潜在优势。针对空间目标探测,建立了空间目标探测信噪比和探测距离模型。针对模型中影响最大的材料特性,测量了典型卫星材料、太阳翼电池材料等紫外和可见光段的光谱反射率,也分析了模型中探测器和深空背景等其它影响因素。以“实践五号”和“天网5D”卫星为例,验证了紫外在太阳翼探测上的确存在显著优势。结果表明,相对于卫星主体可见光探测,太阳翼紫外探测信噪比增强1.3~1.6倍,由紫外和可见光复合探测距离可提升1.5~1.8倍。并且紫外在临边、掩星模式下,以及卫星变轨中发动机尾焰的探测中较可见光具有明显技术优势,因而紫外探测在空间目标探测中具有明显的费效比和探测能力优势。

基于时空知识关联性深度挖掘的频谱能量预测方法研究

频谱能量预测是实现有限频谱资源高效利用的重要途径,而对历史电磁频谱数据进行多尺度、多维度的时空知识提取和关联关系挖掘,从而形成电磁环境画像是实现精准频谱能量预测的重要基础。对于单采集点的时间动态频谱能量数据,时域知识关联关系挖掘受到频谱数据体量大及特征维度高的影响,本文提出了基于并行多模型融合的单点时域特征提取和预测方法;对于多点采集形成的区域频谱能量数据,受到时间动态和空间分布不均的双重影响造成时空知识关联关系挖掘困难,本文首先基于区域电磁环境的相关性构建区域电磁环境的相关关系图,然后基于关系图的关联信息设计了基于图卷积的网络预测模型,研究电磁环境中频谱能量预测问题。通过仿真实验,验证了无论是在单点时间动态场景下还是区域时空场景下,本文提出的方法均优于基线模型,具有良好的预测精度和鲁棒性。

高台YRY钻孔应变仪与BBVS-120地震仪青海玛多M S7.4地震同震对比分析

以高台台秒采样YRY-4钻孔应变仪记录的2021-05-22青海玛多M S7.4地震观测数据及同址观测的BBVS-120地震仪重采样秒数据为研究对象,进行观测曲线变化特征、地震学震相及频谱特征对比分析。结果发现,钻孔应变仪秒采样数据同震记录清晰,满足四分量钻孔应变观测的自检特性。两套仪器均能记录到玛多地震的震相数据,且震相形态相似,到时基本一致。两套仪器的频率谱及时频分布形态相似,频谱成分接近,但频率分布和幅值变化存在差异,这与仪器的观测原理、分量(测向)方位、频带宽度及观测灵敏度等有关。为进一步分析钻孔应变仪的动力学意义,对仪器进行不同时间节点的空间投影,结合震中与台站的相对方位进行分析,发现接近震中指向台站SW-NE向分布的2323、2324分量和垂直于震中指向台站分布的2321、2322分量受力状态相反,可能与P、S波传播规律有关。

融合多工况功率谱密度函数的工作模态分析方法

频域分解(frequency domain decomposition,FDD)方法是开展环境激励下结构工作模态分析(operational modal analysis,OMA)的常用方法。但是,现有的FDD方法存在一些不足:①无法剔除非白激励及谐波激励引起的虚假模态;②无法区分结构密集模态与不相关非白激励产生的虚假模态。通过研究发现功率谱密度(power spectral density,PSD)矩阵的秩是FDD方法性能的决定性因素。在此基础上,提出一种新的工作模态识别OMA方法,该方法结合了不同激励工况下的响应PSD函数矩阵,并分别对单个激励工况下的响应PSD矩阵与多工况下的增广响应PSD矩阵进行奇异值分解,通过对比单工况PSD矩阵与增广PSD矩阵在奇异值峰值处秩的变化,识别出包括密集模态在内的结构模态参数,并消除由非白激励产生的虚假模态。采用桁架结构仿真算例和广州塔的工程数据集验证所提方法的有效性。

有界线性算子的谱半径与扰动

广义逆理论在数值线性代数、数值分析、最优化、控制论、数理统计、微分方程及应用数学中具有引人注目的应用,其中算子广义逆在求解线性算子方程的极值解、最小范数解与最佳逼近解时发挥重要作用.设X为Banach空间,T:X→X为具有闭值域的有界线性算子.利用谱半径代替范数作为刻画扰动算子的工具,在扰动算子是可交换的假设下,减弱了可逆算子方程的经典扰动结果和一般算子方程的最优扰动结果的前提条件.同时,也利用谱半径代替范数作为刻画扰动算子的工具,减弱了Banach空间有界线性算子T的广义逆的经典扰动结果的前提假设,获得了利用谱半径的更加精细的扰动误差估计.