Simultaneous determination of BTX and total hydrocarbons in water employing near infrared spectroscopy and multivariate calibration

A method for the simultaneous determination of benzene, toluene and xylenes (BTX) in water based on the use of a silicone sensing phase, detection by near infrared spectroscopy and multivariate calibration is described. A Doehlert design was employed to define the calibration and external validation sets, which were prepared in the concentration ranges of 2.0–30 mg L⁻¹ for benzene, 4.0–40 mg L⁻¹ for toluene and 1.0–15 mg L⁻¹ for xylenes; these ranges were selected after evaluation of the concentration of these compounds in a water sample contaminated by gasoline. The determinations were performed by inserting a polydimethylsiloxane (PDMS) rod (3.2 mm diameter and 5.0 mm long) into a flask containing the aqueous BTX solution (without headspace), made 3.0 mol L⁻¹ with sodium chloride in order to improve the extraction of the hydrocarbons by the polymer. After extraction, the cylinder was placed in a home-made cell for absorbance measurements from 800 to 2500 nm. The calibration models were constructed using Partial Least Square (PLS) regression and Multiple Linear Regression (MLR), with variables selected by the Successive Projection Algorithm (SPA). PLS models provided relative errors of prediction of 10.9%, 13.3%, 21.9% and 9.6% for benzene, toluene, xylenes and total hydrocarbons, respectively. These values can be considered acceptable for the concentration range studied, indicating that the proposed method can be useful for simultaneous determination of aromatic compounds in water employing NIR.     

An optimization approach to estimate and calibrate column water vapour for hyperspectral airborne data

The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases.     

Remote sensing of leaf water content in the near infrared

A stochastic leaf radiation model was used to predict leaf spectral reflectance as a function of leaf water content for a dicot leaf. Simulated spectral reflectances were analyzed to quantify reflectance differences between different equivalent water thicknesses. Simulated results coupled with consideration of atmoshperic transmission properties and the incident solar spectral irradiance at the earth's surface resulted in the conclusion that the 1.55–1.75 μm region was the best-suited wavelength interval for satellite—platform remote sensing of plant canopy water status in the 0.7–2.5 μm region of the spectrum.     

Sensitivity of guided mode resonance filter-based biosensor in visible and near infrared ranges

The sensitivity of guided mode resonance filter (GMRF) biosensors was analyzed when the initial peak wavelength (IPW) changed from 400 to 1600 nm. The sensitivity of the GMRF to the sample refractive index and thickness was analyzed using rigorous coupled wave analysis method. Results indicate that for a certain IPW, the sensitivity of the GMRF did not change when the sample refractive index varied, but drastically decreased and achieved constant sensitivity with increased sample thickness. When the IPW increased, the sensitivity improved in relation to both sample refractive index and sample thickness. Furthermore, the capability of sample thickness improved with longer IPW. These results are helpful in obtaining an optimal GMRF biosensor.     

Preliminary estimate of calibration of the moderate resolution imaging spectroradiometer thermal infrared data using Lake Titicaca

The absolute radiometric accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) data was evaluated with in situ data collected in a vicarious calibration field campaign conducted in Lake Titicaca, Bolivia during May 26 and June 17, 2000. The comparison between MODIS TIR data produced by the version 2.5.4 Level-1B code and the band radiances calculated with atmospheric radiative transfer code MODTRAN4.0 based on lake surface kinetic temperatures measured by five IR radiometers deployed in the high-elevation Lake Titicaca and the atmospheric temperature and water vapor profiles measured by radiosondes launched on the lake shore on June, 15 2000, a calm clear-sky day, shows good agreements in bands 31 and 32 (within an accuracy of 0.4%) in the daytime overpass case. Sensitivity analysis indicates that the changes on the measured atmospheric temperature and water vapor profiles result in negligible or small effects on the calculated radiances in the atmospheric window bands (bands 20–23, 29, and 31–32). Therefore, comparisons for these bands were made for cases when lake surface temperature measurements were available but no radiosonde data were available and in subareas of 10×16 pixels where there was no in situ measurement but MODIS brightness temperatures in band 31 vary within ±0.15 K by using the validated band 31 to determine lake surface temperatures through the MODTRAN4.0 code. Comparisons and error analysis show that the specified absolute radiometric accuracies are reached or nearly reached in MODIS bands 21, 29, and 31–33 and that there is a calibration bias of 2–3% in bands 20, 22, and 23. The error analysis also shows that the radiosondes cannot provide accurate atmospheric temperature and water vapor profiles to estimate the calibration accuracies in the atmospheric sounding bands (bands 24–25, 27–28, and 34–36) at the specified 1% level and that the calibration accuracy in the ozone band 30 cannot be estimated without in situ measurements of ozone.     

Sensitivity of feedforward neural networks to weight errors

An analysis is made of the sensitivity of feedforward layered networks of Adaline elements (threshold logic units) to weight errors. An approximation is derived which expresses the probability of error for an output neuron of a large network (a network with many neurons per layer) as a function of the percentage change in the weights. As would be expected, the probability of error increases with the number of layers in the network and with the percentage change in the weights. The probability of error is essentially independent of the number of weights per neuron and of the number of neurons per layer, as long as these numbers are large (on the order of 100 or more).     

Radiometric calibration of satellite sensors in the visible and near infrared: History and outlook

Documentation for radiometric calibration of visible and near infrared sensors viewing the earth has been quite limited in the past. Recent quantitative use of satellite radiometry suggests the need for better information concerning instrumentation, estimation of atmospheric effects, and ground verification of satellite measured radiances. An example from the thermal infrared spectral region illustrates that analysis algorithms provide the link between satellite-derived results and the required accuracy of satellite calibration.     

红外目标模拟器校准装置构建和校准技术研究

在对传统红外目标模拟器校准装置分析的基础上,结合辐射照度校准理论、方法和误差来源三方面,构建了红外目标模拟器校准装置,开展了辐照度测量系统内部杂散辐射建模仿真和抑制的研究、红外大气传输修正方法的研究和测试结果数据拟合算法的研究,分别提出了基于复合蒙特卡洛法的内部杂散辐射数学模型、基于编码器-解码器结构的卷积神经网络红外辐射大气传输校准算法和基于粒子群优化的极限学习机算法,并在实验室条件下进行了辐照度计量校准实验。分别验证了在追迹光线数相等的情况下复合蒙特卡洛法的精度更高,复合蒙特卡洛法标准偏差为6.1940×10-8,平均误差为0.19293,且仿真结果体现了红外辐射计各部分杂散辐射对探测器入瞳面接收到的杂散辐射造成的影响;基于编码器-解码器结构的卷积神经网络算法能够较好的预测大气透过率和大气程辐射,在三个波段下的平均误差为3.0783%,3.8186%,5.3452%,低于传统方法,降低了大气透过率和大气程辐射的影响;通过与GA-ELM模型、ELM模型进行对,验证了与传统数据拟合方法相比,基于PSO-ELM的方法在1μm~3μm、3μm~5μm、8μm~14μm三个波段下的拟合精度都有所提高,决定系数分别为0.9925、0.9913、0.9814,平均相对误差分别为0.1242%、0.7157%、0.7471%有效提高了红外辐射测量准确度。     

近红外光谱信号的优化处理

用实验室常规方法测定竹材样品的木质素含量,漫反射方式采集样品的近红外光谱信号,偏最小二乘法(PLS)和完全交互验证方式以建立毛竹木质素含量的定量分析模型。研究主成分数、建模谱区、求导和平滑预处理技术对定量分析模型的影响。结果表明,预处理技术压缩和恢复的近红外光谱信号效果良好,提高了模型的预测能力,优化近红外定量分析模型有重要参考价值。     

Satellite remote sensing of atmospheric water vapour

Abstract

The Advanced Very High Resolution Radiometer (AVHRR-2) has two channels in the 10-13/mi window region. These channels are used for remote sensing of the sea surface temperature corrected for atmospheric absorption. The brightness temperature difference between the channels can be directly related to the atmospheric absorption due to water vapour. The problem of water-vapour retrieval from satellite data is examined in detail. The best evaluation of the water-vapour content is obtained from the spectrometric data of the Infrared Interferometer Spectrometer (IRIS), with an error of about + 3kg/m². It is, however, feasible to obtain the water-vapour content from AVHRR data with an algorithm derived from radiative transfer model simulations. The retrieved water vapour has an error of ±5kg/m² when compared with ship data. It is possible to use the remotely sensed water vapour for the inference of the boundary-layer structure. The information is, however, limited for water vapour contained near the surface.     

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Ground-based single-frequency microwave radiometric measurement of water vapour

The integrated water vapour content of the atmosphere is measured by a ground-based radiometer at 22.234 GHz at the Instituto Nacional de Pesquisas Espaciais (INPE), Brazil. The data set has been partitioned into two sets: one for no cloud and the other for cloudy conditions. The attenuation (dB) under the no cloud condition was always found to vary within 1.0–1.5 dB, except at around 1400 hrs (local time) through 1800 hrs (local time), and it reached a value of more than 1.5 dB with a maximum at 1700. This is in conformity with the theoretically calculated values of water vapour density over the same location. An effort has been made to get the regression relation between the measured and calculated water vapour content, for which the RMS error was found to be 0.49 kg m^?2.     

红外和近红外光谱法在中药定性分析中的应用

目的:为中药的光谱研究提供参考.方法:综述了近年来红外和近红外光谱法在中药定性分析领域的应用,并对2种光谱技术的特点进行了比较a结果与结论:红外和近红外光谱法具有全面、快速、客观、无损等优点,为中药质量控制提供了新的思路.     

Evaluation of measurement errors in ground surface reflectance for satellite calibration

Abstract

One of the more efficient methods used for in-flight calibration of Earth resource satellites is based on measurements performed at ground level on a test site. An experimental study has been conducted in La Crau Sèche (south east France), where a calibration site for SPOT satellites is intended. The accuracy of the calibration depends, critically, on the accuracy of ground bidirectional reflectance factor (BDRF) measurements.

All of the different sources of error are analysed. These are due to two series of factors depending on the characteristics of the radiometer (electronic charac teristics, absolute calibration, angular setting of the radiometer) and of the ground surface (the spectral, spatial, angular and temporal variability of the BDRF). The relative weight of these different causes of error is determined from experimental data. This analysis shows that, besides the well-known disturbing factors such as the calibration of the radiometer and the spatial variability of the BDRF, two other factors can introduce large measurement errors: the spectral and angular variability of reflectance of the site.

This detailed analysis of the different causes of error is not only valid for the calibration of a satellite, but it can also be used to draw up guidelines for performing accurate BDRF measurements in natural conditions for any application.     

Effects of camera calibration errors on static-eye and hand-eye visual servoing

Effects of camera calibration errors for the point-to-point task are investigated in static-eye and hand-eye visual servoing realized with position-based and image-based control laws. For these four configurations, the effect of uncertainty on intrinsic and extrinsic parameters is analyzed. The results show local stability for all configurations under small calibration errors. However, a steady-state error is found in the hand-eye position-based configuration. Simulations have been carried out in order to confirm the theoretical results and evaluate the effects of the uncertainty in terms of the stability region. Another contribution of the paper consists of providing a method for estimating the stability region robust against uncertainty directions for the static-eye position-based case with uncertainty on the camera centers.     

Radiometric studies of transportation of water vapour over Calcutta

A characteristic feature of the lingering of post-rain attenuation over Calcutta, India, has been observed by 22·235 GH_z radiometer. This feature is presumably due to the injection of extra water vapour during the post-rain period in the presence of large ambient humidity and low wind activity. Experimentally observed post-rain attenuation was found to be mostly due to the vertical diffusion of water vapour into the raining zone, the contribution due to horizontal diffusion being small compared to that of the vertical diffusion.     

近红外光谱干涉仪高精度运动控制器设计

介绍一种以DSP TM320VC5402为核心的近红外光谱干涉仪中的音圈电机数字闭环控制方法.采用增量式数字PID控制策略,实现音圈电机匀速运动控制.该系统通过实验验证,达到速度偏差为满量程小于0.5%的控制精度要求,能够满足近红外光谱干涉仪动镜的精确匀速运动控制要求.     

基于ARM嵌入式近红外光谱仪器的研制

本文介绍了基于ARM微处理器的嵌入式近红外光谱仪器设计,并给出了仪器的软件和硬件的设计方案.设计应用了最先进的ARM嵌入式技术,利用ARM丰富的内部设备,实现了光谱数据的传输和基于触摸屏的人机交互平台.实现近红外光谱仪器操作简单化,体现了ARM微处理器的优胜之处.