高光谱场景辐亮度模型仿真研究

高光谱遥感的地面场景是高光谱遥感系统中影响因素最复杂多变的部分。首先基于星载高光谱遥感成像的辐射传输过程,对非均匀的朗伯表面的入瞳处大气辐亮度传输模型进行了研究,得到只需要考虑目标与邻近像元反射率,大气传输因子的辐亮度简化模型。之后介绍了大气中光子扩散原理,并采用蒙特卡洛方法对大气点扩散函数进行仿真;联合地表目标像元反射率数据计算得到基于非均匀朗伯面地表的邻近像元反射率;然后总结了大气传输模型软件MODTRAN计算入瞳处辐亮度数据的原理步骤,并利用其反演了朗伯表面的相关大气传输参数。最终利用基于传感器入瞳处的辐亮度数据表征了高光谱地面场景。     

ETM+数据绝对反射率反演方法分析

将传感器原始记录信号反演为地面反射率对于遥感定量化应用非常重要。为了寻找一种简单、精度高的绝对反射率反演方法，本文以新疆哈密为试验区，使用经验线方法和基于模拟大气状况的辐射传输模型方法对1999年8月的ETM 数据进行了反演研究。通过将各反演方法得到的反射率值与地面实测值及星上反射率进行对比来验证反演效果，发现经验线方法精度最高，中误差仅为0．063，而基于模拟大气状况的辐射传输模型方法精度差，甚至不如未经大气校正的数据。这是由于星上定标系数误差、辐射传输方程自身误差以及模拟大气误差综合的结果，本研究也说明今后在使用辐射传输模型进行大气校正时要慎重考虑。     

基于多个稳定目标的吉林一号光谱02卫星多光谱成像仪的在轨绝对辐射定标

卫星在轨后精准的绝对辐射定标是对其观测数据进行定量化应用的基础。与传统的场地定标法、交叉定标法相比，基于稳定场景目标的定标方法具有成本低、频次高、可实现历史数据定标等优点。使用中分辨率成像光谱仪（MODIS）的MCD19A1产品、MCD43A1产品和MOD03(MYD03)产品模拟出MODIS band8在光谱02卫星观测条件下的方向反射率，再结合MCD43A1产品中MODIS band1～band5的方向反射率、MCD19A2产品中的气溶胶参数、水汽参数以及MOD07(MYD07)产品中的臭氧参数，对2021年10月光谱02卫星多光谱成像仪对多个稳定目标成像的入瞳辐亮度进行辐射传输模拟，实现对其在轨绝对辐射定标。对该定标系数的真实性检验结果表明：与发射前定标系数相比，基于重新标定系数进行大气校正的结果与哨兵2号反射率产品的差异明显减小；重新标定的观测辐亮度与包头地基自动辐射定标数据的平均相对差异为3.18%，说明定标结果具有较高的精度。研究结果可以为使用稳定目标对中高空间分辨率光学遥感卫星在轨绝对辐射定标提供方法支撑。     

成像光谱仪的定标

成像光谱仪是同时获取地物的影像和光谱信息的新一代遥感器。定量遥感技术要求对其进行精确的光谱定标与辐射定标，并在执行任务期间考察定标精度。综述了对航空与航天成像光谱仪进行发射前地面上的绝对定标的方法与在飞行中进行相对定标和绝对定标的各种方法，并探讨了提高其精度的可行途径     

基于Sentinel-3A的北部湾海域叶绿素a浓度遥感反演研究

以北部湾为研究对象,基于Sentinel-3A卫星搭载的OCLI水色传感器,探索了叶绿素浓度的遥感反演方法。通过利用实测光谱数据对北部湾海域进行了分区,结合实测的叶绿素a浓度和Sentinel-3A遥感数据尝试不同的反演因子,包括波段比值、波段差值和波段差比,构建了叶绿素a浓度的遥感反演模型。研究结果表明：(1)北部湾海域的遥感反射率曲线呈现明显的分区的特征,结合光谱特征将北部湾海域分为近岸水体、过渡水体和离岸水体;(2)不同水体类型适用不同的反演因子构建模型,其中R_rs(764.375)/R_rs(681.25)用于近岸水体,[1/R_rs(620)-1/R_rs(708.75)]/R_rs(753.75)用于过渡水体,R_rs(708.75)-R_rs(764.375)用于离岸水体,均取得了较好的拟合效果,相应的R2值分别为0.67、0.80和0.8;(3)分区的方法有效的提高了遥感反演北部湾叶绿素浓度模型的适用性和精度。研究基于Sentin...     

机载成像光谱遥感器场地外定标规范的初步研究

根据作者近几年来的实际工作经验,在参照国内外场地外定标实验方法的基础上,从概念研究入手,概括总结机载成像光谱遥感器定标研究内容和总体实施方案,对机载成像光谱遥感器场地外定标规范进行了初步研究。     

珠海一号欧比特高光谱数据交叉辐射定标初探

搭载于“珠海一号”卫星星座的欧比特高光谱OHS（Orbit Hyper Spectral）传感器,以较高的光谱分辨率和空间分辨率,在近岸及内陆湖泊水色遥感应用方面具有很大潜力。然而OHS缺乏星上定标系统,目前在轨定标采用陆地定标场的资料,其定标结果在水体等低反射率地物误差较大。因此提出一种基于传感器入瞳总辐亮度的交叉辐射定标法,该方法结合QAA（Quasi-Analytical Algorithm）准分析算法和6SV2.1辐射传输模型,利用GOCI（Geostationary Ocean Color Imager）多光谱数据对OHS高光谱数据进行交叉辐射定标。研究结果表明：①GOCI和OHS传感器获取的地物辐射相关性好,在可见光波段范围内,R²均高于0.84;②重新定标后的数据能明显改善不同传感器之间的辐射差异,在可见光波段范围内,定标误差小于9%。实验为高光谱传感器的辐射定标提供了一种新的方法,对建立高光谱定量化、业务化水色遥感处理系统,特别对OHS数据在水域的各种应用具有重要意义。     

不同含水量黄棕壤反射光谱特征研究

采用多光谱辐射仪(MSR～16R)对自然条件下不同水分含量黄棕壤光谱特征进行了研究，试验结果表明在可见光部分(460～710nm)，土壤含水量与光谱反射率相关性差，而在红外部分(760～1650nm）土壤含水量与光谱反射率达到极显著负相关，模式方程拟合度都在0．86以上，因此通过测定土壤光谱反射率来推算土壤含水量是可行的。应用地面光谱测量试验的结果，本文讨论了由地面光谱测量来推算土壤含水量向由卫星遥感影像反演土壤含水量过渡的可能性，进而对采用TM遥感影像对黄棕壤分布区土壤水分状况实施遥感监测的可行性作了一些探访。     

基于MODIS大气产品的天宫一号高光谱成像仪数据大气校正研究

天宫一号(TG-1)搭载的高光谱成像仪获取了大量的高光谱数据,可用于国土资源、农林业和油气矿产等领域的研究。但由于遥感成像时会受到大气的干扰,因此需要首先进行大气校正,消除大气的影响,才能进行遥感定量分析与应用。利用准同步的中分辨率成像光谱仪MODIS(Moderate Resolution Imaging Spectroradiometer)大气参数产品,结合6S辐射传输模型对天宫一号高光谱成像仪数据进行大气校正,并利用地面测量光谱和同步MODIS反射率数据对结果进行了验证。结果表明:经过大气校正后,天宫一号高光谱成像仪数据和地面测量光谱一致性较好,所有样点的相关系数都大于0.97,最大均方根误差为0.088。和MODIS反射相比,各波段回归直线的斜率接近1,且R²都大于0.8。     

利用Landsat-8 OLI反演大气气溶胶的可见光谱段地表反射率估算

准确估算地表反射率的贡献一直是遥感反演大气气溶胶光学厚度过程中的重点和难点。为了促进Landsat-8OLI传感器在地表参数定量化特别是大气遥感领域的应用,本文提出一种利用OLI 1.6μm、2.2μm短波红外谱段数据估算遥感影像可见光地表反射率的方法。该方法依托于MOD04产品地表反射率估算模式,通过光谱归一化和构建新的短波红外植被指数等过程,建立OLI地表反射率估算模式,通过误差分析发现该模式能够有效地降低由于传感器光谱响应不同对估算结果的影响,对应用在OLI遥感影像的计算结果与同时间同区域MOD04产品地表反射率进行比较,表明其结果有较高的相关性和可靠性。     

A comparison of empirical and neural network approaches for estimating corn and soybean leaf area index from Landsat ETM+ imagery

Plant foliage density expressed as leaf area index (LAI) is used in many ecological, meteorological, and agronomic models, and as a means of quantifying crop spatial variability for precision farming. LAI retrieval using spectral vegetation indices (SVI) from optical remotely sensed data usually requires site-specific calibration values from the surface or the use of within-scene image information without surface calibrations to invert radiative transfer models. An evaluation of LAI retrieval methods was conducted using (1) empirical methods employing the normalized difference vegetation index (NDVI) and a new SVI that uses green wavelength reflectance, (2) a scaled NDVI approach that uses no calibration measurements, and (3) a hybrid approach that uses a neural network (NN) and a radiative transfer model without site-specific calibration measurements. While research has shown that under a variety of conditions NDVI is not optimal for LAI retrieval, its continued use for remote sensing applications and in analysis seeking to develop improved parameter retrieval algorithms based on NDVI suggests its value as a “benchmark” or standard against which other methods can be compared. Landsat-7 ETM+ data for July 1 and July 8 from the Soil Moisture EXperiment 2002 (SMEX02) field campaign in the Walnut Creek watershed south of Ames, IA, were used for the analysis. Sun photometer data collected from a site within the watershed were used to atmospherically correct the imagery to surface reflectance. LAI validation measurements of corn and soybeans were collected close to the dates of the Landsat-7 overpasses. Comparable results were obtained with the empirical SVI methods and the scaled SVI method within each date. The hybrid method, although promising, did not account for as much of the variability as the SVI methods. Higher atmospheric optical depths for July 8 leading to surface reflectance errors are believed to have resulted in overall poorer performance for this date. Use of SVIs employing green wavelengths, improved method for the definition of image minimum and maximum clusters used by the scaled NDVI method, and further development of a soil reflectance index used by the hybrid NN approach are warranted. More importantly, the results demonstrate that reasonable LAI estimates are possible using optical remote sensing methods without in situ, site-specific calibration measurements.     

Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data

Imaging spectrometers operating in the solar spectrum measure the upwelling reflected solar radiation, and are an important tool in the bio/geochemical characterization of the Earth system. Surface reflectance is usually the starting point for the retrieval of biophysical parameters from remote measurements. Reliable radiometric and spectral calibrations and accurate atmospheric correction are mandatory in the interpretation of the surface reflectance. A complete surface reflectance retrieval scheme specifically designed for ultra-fine spectral resolution (bandwidth from 10 to 2 nm) and spatial resolution (pixel size less than 10 m) imaging spectrometers is presented in this work. The assessment of the spectral calibration is coupled to the removal of the atmospheric distortion so that maps of surface reflectance are derived, as well as columnar water vapor (CWV) maps, estimations of aerosol optical thickness (AOT) and updated sensor gain coefficients and spectral calibration. Radiative transfer calculations are performed by an optimized version of the MODTRAN4 code, which is run before processing each image. The method is tested against Compact Airborne Spectrographic Imager (CASI) 1500 images acquired during the ESA SENtinel-2 and FLuorescence EXperiment (SEN2FLEX) campaigns held in the Barrax (La Mancha, Spain) study site during June and July 2005. A peak-to-peak spectral shift variation of up to 2.3 nm is detected in CASI. Concerning atmospheric products and surface reflectance retrievals, an extensive validation is performed using ground-based measurements. A good correlation between ground measurements and CASI-derived AOT and CWV is found, with a Pearson correlation coefficient r² up to 0.71 and 0.74, respectively. The subsequent surface reflectance retrievals also hold a good correspondence with ground-based measurements.     

Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data

A synthetic data spectral cube that represents at-sensor radiance data of AVIRIS was used to examine the accuracy of several methods to recover absolute surface reflectance data of terrestrial targets. Soil and vegetation targets, selected to represent the images of ground variation and their spectra, were retrieved using HATCH, Empirical Line (EL) and their hybrids methods. After a synthetic radiance data cube was generated, reflectance recovery was carried out and compared with the true (input) reflectance information. It was found that even under controlled and ideal conditions, the spectral recovery using HATCH code provided differences of up to 40%. The EL methods, using the two end-members that represent the scene reduced this difference to about 4%, and in some cases, even to 0.1% It was found that selecting the calibration targets over low water vapor content improved the results. Applying EL on radiance data provided a severe difference of more than 200% in areas located outside the calibration target water vapor zone. Only over similar water vapor zones were the EL methods found to reasonably recover the surface reflectance. Examining the spectral variability in the calibration targets showed that using of spectral features targets with relative spectral similarity is almost as effective as using spectrally featureless targets for the EL process. Applying EL, using external spectral information of possible known targets, revealed a relatively high difference, as compared to the true reflectance data. However, thematic analysis using a SAM classifier proved that even under non-ideal conditions, the EL correction can yield a reasonable spatial mapping capability relative to those obtained under real reflectance domains. It was concluded that EL must be run on reflectance data (generated from absolute based method) over low water vapor zones to provide the most precise reflectance information. Also, it was found that it is not mandatory to select calibration targets that are totally featureless or characterized by low or high albedo response.     

A simplified method for retrieval of ground level reflectance of targets from airborne video imagery

This paper describes an efficient method for retrieval of ground reflectance characteristics of targets from calibrated multispectral airborne video data for routine operational airborne missions. The method uses a simplified atmospheric scattering model in combination with a dark-object subtraction procedure to estimate the effect of the atmosphere in the path between the target and the sensor, as well as the adjacent environmental effect, on the radiation signal received by an airborne sensor. The simplicity of the atmospheric scattering model is maintained by the assumption that the air density within the targetsensor path in the lower atmosphere is sufficiently uniform for operations of the Charles Sturt University's (CSU) Multispectral Airborne Video System (MAVS). The MAVS acquires imagery in blue, green, red and near-infrared (NIR) narrow spectral bands. The MAVS is radiometrically calibrated and has a consistent radiometric response in-flight. An important feature of the new method is the coupling of the image based brightness data (DN) of a dark-object and the system radiometric calibration coefficients to determine the path reflectance and the environmental reflectance of the target. The sum of the path reflectance and the environment reflectance is known as haze reflectance. The haze reflectance indicates the amount of atmospheric haze in the airborne imagery. The simplified atmospheric model is then employed to determine the actual ground reflectance of the targets using the haze subtracted apparent (total) reflectance of the target at the altitude of the airborne sensor. The apparent reflectance of the target at the sensor altitude is obtained directly from the image based DN data and the system radiometric calibration coefficients. An interesting aspect of this simplified method is that an estimate of the environmental reflectance can be obtained as a by-product of the atmospheric haze calculation using a dark-object subtraction technique. The retrieved ground reflectance characteristics from calibrated MAVS imagery are now being used routinely for remote quantitative monitoring of agricultural and environmental targets.     

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.     

On the temporal stability of ground calibration targets: implications for the reproducibility of remote sensing methodologies

Ground calibration targets (GCT) fulfil an essential role in vicarious calibration and atmospheric correction methodologies. However, assumptions are often made about the temporal stability of GCT reflectance. This letter presents results from a multi‐year study aimed at testing the temporal stability of a typical weathered concrete GCT in southern England. Very accurate measurements of hemispherical‐directional reflectance factors in the 400–1000 nm range were collected using a mobile dual‐beam spectroradiometer. Results demonstrated that the calibration surface was subject to seasonal growth of a biological material, which caused the reflectance factor to vary by a factor of two during the year (range = 16.4% reflectance at 670 nm). The spectral effect of this was most noticeable in field spectra collected in April. As environmental conditions became drier throughout the summer, concrete reflectance factors increased. Over multiple seasons the same patterns in reflectance factors repeated, indicating the predictable nature of the biological signature. The research also suggested that the biological material was affected to a small but measurable extent on a daily basis by changes in relative humidity occurring after onset of a local sea breeze. The research highlights the dynamic nature of weathered GCTs, and has wider implications for those using similar sites for vicarious calibration or atmospheric correction purposes.     

Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data

A full-chain process approach to extracting reflectance information from hyperspectral (HRS) data which is valid for all sensor qualities is proposed. This method is based on a mission-by-mission approach, followed by a unique vicarious calibration stage. As the HRS sensor's performance may vary in time and space, a vicarious calibration method to retrieve accurate at-sensor radiance values is necessary. In fact, vicarious calibration solutions usually rely on natural, well-known, bright and dark targets that are large in size and radiometrically homogeneous. Since such targets are not commonly found in the field for every mission and their spectral features can sometimes resemble artifacts in the corrected radiance, a new vicarious calibration approach is needed. This paper describes a new method that uses artificial agricultural black polyethylene nets of various densities as vicarious calibration targets that are set up along the airplane's trajectory (preferably near the airfield). The different densities of the nets combined with any bright background afford full coverage of the sensor's dynamic range. We show that these artificial targets can be used to assess data quality and correct at-sensor radiance within a short time. Several case studies are presented using Aisa-DUAL sensor data taken at different times from different locations. We found that even “lost data” (in terms of radiance drift) could be recovered by the suggested method. We term the suggested vicarious calibration approach supervised vicarious calibration (SVC) and demonstrate its performance in terms of spectral accuracy. The limitations of the method are also discussed but the overall conclusion is that the suggested procedure is functional, valuable and practical for sensors with questionable or uncertain laboratory-determined radiometric parameters.     

Retrieval of surface directional reflectance properties using ground level multiangle measurements

Knowledge of the directional reflectance properties of natural surfaces such as soils and vegetation canopies is essential for classification studies and canopy model inversion. Atmospheric correction schemes, using various levels of approximation, are described to retrieve surface bidirectional reflectance factors (BRFs) and directionalhemispherical reflectances (albedos) from multiangle radiance measurements taken at ground level. The retrieval schemes are tested on simulated data incorporating realistic surface BRFs and atmospheric models containing aerosols. Sensitivity of the atmospherically corrected BRFs and associated directional-hemispherical reflectances to various aerosol properties and the sun-view geometry is illustrated. A measurement strategy for obtaining highly accurate surface reflectance properties also is examined in the context of instrument radiometric calibration, knowledge of the atmospheric properties, and sun-view angular coverage.     

Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationships between spectral reflectance and biophysical variables by more powerful and stable mathematical tools, in particular for the retrieval of LAI and chlorophyll content. Within the overall context of the European Space Agency (ESA) Spectra Barrax Campaign (SPARC) experiment, an extensive field study was carried out in La Mancha, Spain, simultaneously to the overflight of airborne imaging spectrometers (AHS, HyMAP, ROSIS) and the overpass of CHRIS‐PROBA and MERIS sensors. During the SPARC‐2003 and SPARC‐2004 campaigns, numerous ground measurements were made in the Barrax study area (covering LAI, fCover, leaf chlorophyll a+b, leaf water content and leaf biomass), together with other complementary data, and a total of 17 CHRIS‐PROBA images were acquired. Representative points have been selected from a total of nine different crops, and also retrieved from the CHRIS‐PROBA images acquired within the days of the field campaign. About 250 reflectance spectra from five different observation angles have been analysed. Hyperspectral reflectance spectra have been adjusted by means of third‐degree polynomial functions between 500 nm and 750 nm, and correlations observed between LAI values and the coefficients of these polynomials yielded LAI as a result of the mathematical fitting. On the other hand, the area under the spectral reflectance curves has been calculated in the interval from 600 nm to 700 nm, the region of the red spectral interval where strong absorption features for chlorophyll have been observed, though areas under the curves are also strongly correlated to the chlorophyll content of the crops. Furthermore, a linear relationship between these areas and the chlorophyll content is proposed in this work. This relationship allows the retrieval of leaf chlorophyll by satellite data, based on the spectral information. Both of the proposed methods are almost independent of the observation angles employed. The high number of in situ measurements acquired simultaneously to satellite overpasses, and the broad available range of data, have allowed validation of both methods, with a large number of data and in a statistically consistent manner.     

Improving leaf area index retrieval using spectral characteristic parameters and data splitting

ABSTRACT

Spectral variables such as spectral characteristic parameters (SCPs) commonly change with intraday phenology. Empirical retrieval methods, which are generally used in leaf area index (LAI) retrieval due to their simplicity and computational efficiency, typically relate the biophysical parameter of interest to the spectral variable during the whole observation period. Whilst information regarding diurnal changes in spectral variables is necessary and useful in applied contexts. We analysed the diurnal change characteristics of canopy spectral reflectance and SCPs of winter wheat in the jointing stage based on field data collected at fixed sampling points with different vegetation canopies, and validated the effectiveness of data splitting strategy with field data collected in random sample pattern. The key results are as follows: (i) Canopy spectral reflectance of winter wheat in the jointing stage exhibited clear intraday variability, typically presenting a double-peak characteristic occurring from 11:35 to 12:34, where the reflectance changed substantively during this period. (ii) The SCPs of winter wheat in the jointing stage exhibited different diurnal patterns. Specifically, the blue edge position presented ‘blue shifts’, the yellow edge position generally exhibited steady fluctuations, and the red edge position followed divergent trends between the two sampling points due to differences in the vegetation canopy. Amplitude and area parameters exhibited a double-peak characteristic but there were slight differences between them. (iii) By dividing the whole observation period into sub-periods, the coefficient of variation (CV) of each spectral characteristic parameter can be greatly reduced, whilst the coefficient of determination (R²) of LAI retrieval can be greatly increased. Optimal spectral parameters and sub-periods for LAI retrieval were confirmed based on the diurnal variation of SCPs. To optimize LAI retrieval the suggested spectral parameters are blue edge amplitude, red edge amplitude, and red edge area, and the sub-periods are 09:50–11:35, 11:35–12:34, 12:34–13:50, and 13:50–15:00, respectively. The 11:35–12:34 sub-period should be carefully considered due to possible midday depression of photosynthesis.