FY-3/VIRR及MERSI与EOS/MODIS植被指数比较与差异原因分析

为了论证FY-3/VIRR、FY-3/MERSI及EOS/MODIS影像之间协同反演的可行性,这些数据生成植被指数间的比较来说明。选取2012年覆盖黑河上游附近的多对同日过境的晴空数据,利用统计方法对比分析了FY-3/VIRR、FY-3/MERSI与EOS/MODIS的归一化植被指数。结果表明:FY-3/VIRR和FY-3/MERSI的NDVI与EOS/MODIS存在显著的相关性,相关系数均超过0.99;但3种数据所生成的NDVI也存在显著的差异。对引起NDVI差异的原因进行了详细分析,发现光谱响应函数、大气水汽、辐射定标、观测角度等都会对植被指数产生一定的影响;这些分析可以为以后多源遥感数据协同反演提供参考。     

FY3A/MERSI地表温度反演

MERSI是我国第二代极轨气象卫星上的重要传感器,可获取高空间分辨率和高时间分辨率的对地观测影像。为使Jimènez-Mu珘nozSobrino算法更适用于FY3A/MERSI传感器通道特性,更新了大气函数的估算系数,并引入观测角度因子,以获取更为精确像元间更为平滑的地表温度。用MODTRAN4模拟验证该算法精度,得引入角度因子后反演精度显著提升,所有角度下平均误差为-0.6±2.2K。用实测的敦煌戈壁地表温度和MODIS地表温度产品评价MERSI反演结果,显示MERSI地表温度的空间分布准确,结果精度也较高。与实测温度对比,平均误差为1.74K,均方根误差小于1.9K。研究区域与MODIS地表温度间差异平均为2.6307K。虽然会受云检测精度和观测亮温偏高的影响,由MERSI反演的高精度地表温度在相关科研和业务方面仍然具有极好的应用前景。     

传感器光谱响应差异对NDVI的影响

传感器光谱响应差异是导致不同来源NDVI观测数据之间差异的因素之一,在进行多源遥感数据之间的对比和同化处理时需要对其影响加以分析和校正。基于此,该研究将地物的反射波谱曲线和传感器的光谱响应函数进行卷积,以此来模拟不同传感器在可见光和近红外通道的等效地表反射率并计算相应的NDVI,分别用绝对误差和相对误差两个指标来描述不同传感器观测结果相对于MODIS的差异并建立校正模型。结果表明,光谱响应差异引起的不同传感器观测结果差异可以通过二次多项式模型进行校正,基于相对误差的校正模型比基于绝对误差的校正模型效果更好一些,整体上传感器光谱响应差异对NDVI观测值的影响有限,还需要进一步考虑大气状况及观测几何等其他因素的影响。     

滞尘叶片反射光谱的Hapke双层散射模型模拟北大核心CSCD

针对粉尘附着于植被表面干扰植被光谱信息的纯洁性,从而影响植被正常遥感监测和管理问题,提出一种基于Hapke双层散射模型对滞尘叶片反射光谱进行正演仿真模拟的方法,为定量剔除粉尘对植被光谱的干扰奠定理论基础。通过设定粉尘粒径、堆积密度等物理参数定量模拟滞尘叶片反射光谱,再利用实际测得的反射光谱评价模型模拟精度,从而实现滞尘叶片反射光谱的高精度正演模拟。研究表明:随着粉尘沉积质量的增加,Hapke双层散射模型得到的理论误差呈先增大后减小的趋势,最终理论值和实验值接近一致;在不同波段模拟反射光谱误差影响不一,其中近红外波段误差最大;模型模拟的反射光谱曲线与实验光谱曲线拟合精度高达95%,反射率偏差值控制在5%以内,模拟精度较高。     

基于标准探测器的FY-2(05)星扫描辐射计可见通道外场定标

为了对FY-2号05星扫描辐射计可见光通道进行发射前外场辐射定标,在采用了传统的辐亮度定标方法的同时,提出了一种新型的独立于扫描辐射计可见光通道光谱响应函数的基于标准探测器的反射比定标法(Detector Based Reflectance Calibration)。该方法通过筛选波长得出在一定的精度范围内FY-2号可见光通道的漫射板反射辐亮度与漫射板的某一单波长反射辐亮度成正比,从而可以用8通道高精度辐亮度标准探测器中对应波段的探测器的响应替代FY-2号05星扫描辐射计宽波段响应直接求出定标系数。传统辐亮度定标方法结果的不确定度为6.0%～6.4%,而利用基于标准探测器的反射比定标法定标结果的不确定度达到5.4%。实验结果表明基于标准探测器的反射比定标法由于采用了直接由高精度低温绝对辐射计传递的标准探测器,与传统的基于辐射源传递相比其测量精度得到有效提高,并且受现场大气条件的影响小。     

利用风云卫星MERSI数据反演叶面积指数的研究

风云三号A星上搭载的中分辨率成像光谱仪(Medium Resolution Imaging Spectrometer)MERSI从2008年5月底开始对地球观测,其中5个波段250m分辨率的数据包含了丰富的植被信息,在全球同类传感器数据中独具特色,在其基础上反演的陆表植被数据产品目前还不多见。利用2013年生长季在河北固城观测获取的冬小麦光谱数据,结合MERSI 250m数据计算的NDVI值,建立二者NDVI之间的线性转换模型Y=1.1458 X+0.1916;同时利用地物光谱NDVI与实测叶面积指数构建了NDVI-LAI指数模型Y=0.0899e4.459 X;然后,利用MERSI 250m数据反演出华北太行山前平原区冬小麦的叶面积指数,经与大田观测的叶面积指数以及同期MODIS的叶面积指数产品对比验证,结果表明:反演的MERSI-LAI与实际观测叶面积指数接近且具有很好的线性关系,其空间分布与MODIS的叶面积指数相近,但MODIS-LAI数值明显偏小。     

基于深度学习的风云四号卫星绿光通道构建方法

风云四号A星是中国第二代静止气象卫星的首发星，其上搭载的新一代静止轨道成像仪具有14个光谱通道，但该仪器未设置绿光通道，给RGB图像合成应用带来了一定困难，因此用其他光谱通道来构建绿光通道对提升成像仪观测的综合应用具有重要意义。提出了一种基于深度学习的光谱转换方法，通过建立光谱通道间的关系，实现对FY-4A/AGRI绿光通道的模拟。该方法考虑了FY-4A/AGRI的可见光通道特性，以AQUA/MODIS数据为光谱转换基准，首先建立FY-4A/AGRI与AQUA/MODIS可见光通道的光谱校正关系，将FY-4A/AGRI的可见光通道校准到AQUA/MODIS的基准上；其次，通过大量AQUA/MODIS数据的训练，基于深层感知器网络建立绿光通道与其他可见光通道的光谱转换模型；最后通过迁移学习将其应用到FY-4A/AGRI上。结果显示，通过模型构建的MODIS绿光通道反射率与实际的MODIS绿光通道反射率偏差在0.01以内，构建的FY-4A/AGRI绿光通道反射率与MODIS绿光通道实际反射率偏差在0.02以内。研究结果表明，该方法能很好地实现FY-4A/AGRI绿光通道的构建，为FY-4A...     

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。     

暗目标法从风云三号数据反演陆地气溶胶

针对2013年发射升空的FY-3C星的中分辨率光谱成像仪(MERSI)应用气溶胶反演较少的不足,开展了暗目标法反演陆地气溶胶的应用研究,为研究气候变化、大气环境监测等提供数据支撑。在MODIS暗目标法基础上,针对FY-3C/MERSI数据反演陆地气溶胶,使用6SV完成辐射传输计算建立大气参数查找表,采用IDL的HDF5读写接口完成数据提取与辐射定标,利用蓝光波段(470nm)与短波红外波段(2 130nm)的线性关系分离出大气信息,插值大气参数查找表得到气溶胶光学厚度(aerosol optical depth,AOD)。2014年5月15日华北地区的算法应用表明,该算法能较好地监测空气污染的分布。2014年5月的MERSI数据反演结果与同期AERONET香河站的气溶胶产品比对表明,该算法与地面观测结果有着较好的一致性,相关系数优于0.8。     

基于TDR的多孔介质中含水/水合物饱和度测量方法仿真研究

为了优化设计时域反射技术（TDR）中的探针结构,建立基于TDR响应的含水/水合物测量模型,利用有限元数值模拟方法建立了TDR测量过程数值仿真模型。分别以空气、不同浓度氯化钠溶液以及不同水合物含量的石英砂为被测介质验证了模型的正确性、研究了被测介质电导率和介电常数对反射波形的影响规律。通过改变被测介质的介电常数来模拟含水/水合物饱和度不同的石英砂,随着石英砂中水合物含量的增加,含水量逐渐降低,表观介电常数随之减小,电磁波传播速度随之以非线性形式增加;与理论值相比较,仿真计算所得到的传播速度最大误差处于5%以内。下一步需要在数值模型中的被测区域中填充各相异性材料来更加真实地模拟含水合物沉积物被测介质。     

Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters

Mapping of total suspended matter concentration (TSM) can be achieved from space-based optical sensors and has growing applications related to sediment transport. A TSM algorithm is developed here for turbid waters, suitable for any ocean colour sensor including MERIS, MODIS and SeaWiFS. Theory shows that use of a single band provides a robust and TSM-sensitive algorithm provided the band is chosen appropriately. Hyperspectral calibration is made using seaborne TSM and reflectance spectra collected in the southern North Sea. Two versions of the algorithm are considered: one which gives directly TSM from reflectance, the other uses the reflectance model of Park and Ruddick (2005) to take account of bidirectional effects.Applying a non-linear regression analysis to the calibration data set gave relative errors in TSM estimation less than 30% in the spectral range 670-750 nm. Validation of this algorithm for MODIS and MERIS retrieved reflectances with concurrent in situ measurements gave the lowest relative errors in TSM estimates, less than 40%, for MODIS bands 667 nm and 678 nm and for MERIS bands 665 nm and 681 nm. Consistency of the approach in a multisensor context (SeaWiFS, MERIS, and MODIS) is demonstrated both for single point time series and for individual images.     

Comparison between in situ and MODIS-derived spectral reflectances of snow and sea ice in the Amundsen Sea,Antarctica

The spectral albedo and directional reflectance of snow and sea ice were measured on sea ice of various types, including nilas, grey ice, pancake ice, multi-year pack ice, and land-fast ice in the Ross, Amundsen and Bellingshausen seas during a summer cruise in February through March 2000. Measurements were made using a spectroradiometer that has 512 channels in the visible and near-infrared (VNIR) region in which 16 of the 36 bands of the Moderate Resolution Imaging Spectroradiometer (MODIS) are covered. Directional reflectance is also retrieved from the MODIS radiometrically calibrated data (Level 1B) concurrently acquired from the first National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) satellite, Terra. The locations of the ground ice stations are identified accurately on the MODIS images, and the spectral albedo and directional reflectance values at the 16 VNIR MODIS bands are extracted for those pixel locations. MODIS-derived reflectance is then corrected for the intervening atmosphere whose parameters are retrieved from the MODIS atmospheric profiles product (MOD07_L2) for the same granule. The corresponding spectral albedo and directional reflectance with the same viewing geometry as MODIS are derived from our ground-based spectroradiometer measurements. Because the footprint of the ground spectroradiometer is much smaller than the pixel sizes of MODIS images, the averaged spectral reflectance and albedo in the vicinity of each ice station are simulated for the corresponding MODIS pixel from the ground spectral measurements by weighting over different surface types (various ice types and open water). An accurate determination of ice concentration is important in deriving ground reflectance of a simulated pixel from in situ measurements. The best agreement between the in situ and MODIS measurements was found when the ground had 10/10 ice concentration (discrepancy range 0.2–11.69%, average 4.8%) or was oneice-type dominant (discrepancy range 0.8–16.9%, average 6.2%). The more homogeneous the ground surface and the less variable the ground topography, the more comparable between the in situ and satellite-derived reflectance is expected.     

Optimal directional view angles for remote-sensing missions

Directional reflectance measurements spanning the entire exitance hemisphere for a number of vegetation and bare soil sites were measured from the ground in NOAA-7 AVHRR bands 1 (0-58-0-68 μm) and 2 (0-73-1-1 μm) for various solar zenith angles. The normalized difference and ratio transformations of these bands were calculated. The effects of the atmosphere on the directional radiance above the atmosphere was explored by applying the average mid-latitude atmospheric model of Dave to a series of ground measurements. These data and simulations were analysed for the optimal directional view angles with respect to two strategies. The first strategy views the utility of off-nadir measurements as extending spatial and temporal coverage while the second strategy views the utility of off-nadir measurements as increasing information content about the physical characteristics of the target(s). The optimum view angles for the first strategy are view angles which minimize the change in sensor response of target(s) relative to the sensor response to the nadir direction. The optimum view angles for the second strategy are view angles which, relative to a nadir view, provide superior and/or additional information about the physical characteristics of the target(s).

For the first strategy the results indicate that, from the ground level, the optimum view angles are in the azimuth direction perpendicular to the principal plane of the Sun for all targets, bands and Sun angles. The differences in reflectance between the nadir and off-nadir view angles in this azimuth plane decreased significantly with decreasing solar zenith angle for both bands and all targets. The normalized difference transformation of bands 1 and 2 is significantly less sensitive to variations of view angles than the individual bands for all Sun angles and targets. The results for the second strategy from the ground level showed that off-nadir view angles with an aximuth in the principal plane of the Sun were optimum for all targets and both bands. Finally, the results of the atmospheric simulation study indicated that a mid-latitude summer atmosphere does not significantly change the optimum directional view angles for either the first strategy or the second strategy. However, the atmosphere increased off-nadir sensor responses relative to the ground-level responses particularly in the forwardscattering direction. The normalized difference transformation of bands 1 and 2 was a good transformation for the first strategy for off-nadir viewing angles of less than 45°.

The study provides valuable information for interpreting present remotely sensed off-nadir data and in designing future systems with off-nadir capabilities.     

AERONET-based surface reflectance validation network (ASRVN) data evaluation: Case study for railroad valley calibration site

The AERONET-based Surface Reflectance Validation Network (ASRVN) is an operational processing system developed for validation of satellite derived surface reflectance products at regional and global scales. The ASRVN receives 50 × 50 km² subsets of MODIS data centered at AERONET sites along with AERONET aerosol and water vapor data, and performs an atmospheric correction. The ASRVN produces surface bidirectional reflectance factor (BRF), albedo, parameters of the Ross-Thick Li-Sparse (RTLS) BRF model, as well as Hemispherical-Directional Reflectance Factor (HDRF), which is required for comparison with the ground-based measurements. This paper presents a comparison of ASRVN HDRF with the ground-based HDRF measurements collected during 2001-2008 over a bright calibration Railroad Valley, Nevada site as part of the MODIS land validation program. The ground measurements were conducted by the Remote Sensing Group (RSG) at the University of Arizona using an ASD spectrometer. The study reveals a good agreement between ASRVN and RSG HDRF for both MODIS Terra and Aqua with rmse ~ 0.01-0.025 in the 500 m MODIS land bands B1-B7. Obtained rmse is below uncertainties due to the spatial and seasonal variability of the bright calibration 1 km² area. While two MODIS instruments have a similar rmse in the visible bands, MODIS Aqua has a better agreement (lower rmse) with the ground data than MODIS Terra at wavelengths 0.87-2.1 μm. An independent overall good agreement of two MODIS instruments with the ground data indicates that the relative calibration of MODIS Terra and Aqua at medium-to-bright reflectance levels for the stated time period is significantly better than uncertainties of the ASRVN and ground data.     

Validating MODIS land surface reflectance products using ground-measured reflectance spectra – a case study in semi-arid grassland in Inner Mongolia,China

Validation of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance products is important to effective utilization of such products for earth systems science. Ground-based measurements are normally utilized for such validation. However, the major scale mismatch between the ground ‘point’ measurement and MODIS resolution (500 m and 1 km) makes direct comparison infeasible over many land surface types. In this paper, an indirect comparison between ground ‘point’ measurements and MODIS land surface products via high-resolution remotely sensed imagery (Landsat Thematic Mapper/TM) was utilized in semi-arid grassland of Inner Mongolia in summer 2005, where ground measurements are relatively sparse in comparison with other locations around the world. Within the validation, the TM reflectance imagery was first calibrated by the ground ‘point’ measurements, and then aggregated to MODIS data resolution for determination of their accuracy. Besides common direct spectral band comparison of reflectance between TM and MODIS, empirical/indirect comparison between TM and MODIS was also implemented. Both types of validation showed that the absolute error of bidirectional reflectance from atmospheric correction (MOD09) is less than 9.4%, and for nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (MOD43B4) it is less than 3.1%, in which the error of visible bands of two data sets is less than 1.35% and 0.95%, respectively. This validation will help improve the accuracy of MODIS products used in this area.     

Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces

Remote sensing technique has become the most efficient and common approach to estimate surface vegetation cover. Among various remote sensing algorithms, spectral mixture analysis (SMA) is the most common approach to obtain sub‐pixel surface coverage. In the SMA, spectral endmembers (the number of endmembers may vary), with invariant spectral reflectance across the whole image, are needed to conduct the mixture procedure. Although the nonlinear effect in quantifying vegetation spectral reflectance was noticed and sometimes addressed in the SMA analysis, the nonlinear effect in soil spectral reflectance is seldom discussed in the literature. In this paper, we investigate the effects of vegetation canopy on the inter‐canopy soil spectral reflectance via mathematical modelling and field measurements. We identify two mechanisms that lead to the difference between remotely sensed apparent soil spectral reflectance and actual soil spectral reflectance. One is a canopy blockage effect, leading to a reduced apparent soil spectral reflectance. The other is a canopy scattering effect, leading to an increased apparent soil spectral reflectance. Without correction, the first (second) mechanism causes an overestimated (underestimated) areal coverage of the low‐spectral‐reflectance endmember. The overall effect of canopy to soil, however, tends to overestimate fractional vegetation cover due to the relative significance of the canopy blockage effect, even though the two mechanisms vary with spectral wavelengths and spectral difference between different vegetation and soil. For the SMA of vegetated surface using multiple‐spectral remote sensing imagery (e.g., LandSat), it is recommended that infrared bands of low vegetation spectral reflectance (e.g. band 7) be first considered; if both visible and infrared bands are used, combination of bands 3, 4, and 5 is appropriate, while use of all six bands could overestimate fraction vegetation cover.     

Validation of Narrow-band Surface Albedo Retrieved from FY-3C MERSI Satellite Data

Surface albedo is one of the driving factors in surface radiant energy balance and surface-atmosphere interaction.It is widely used in surface energy balance, medium and long-term weather forecasting and global change research.This study aims to validate the surface albedo retrieved from FY-3C MERSI. This paper selected four regions in Africa and North America as study areas to validate the retrieved albedo from the reflectance data and angle data of FY-3C MERSI at 250 m resolution in 2014. The semi-empirical kernel-driven BRDF(bidirectional reflectance distribution function) model RossThick-LiSparseR and least squares fitting method were used to calculate the parameter of BRDF. Then four narrow-band black-sky albedos and four narrow-band white-sky albedos can be obtained by angle integration. Finally, the cross-validation of FY-3C surface narrow-band albedo products with MODIS albedo products (MCD43A3) was carried out. The results show that theRoot Mean Square Error(RMSE) between the FY-3C narrow-band albedo and the corresponding MODIS narrow-band albedo is in the range of 0.01 ~ 0.04, and the Mean Bias (MBIAS) is 0.09. FY-3C narrow-band albedo has good consistency with the corresponding MODIS narrow-band albedo in the visible and near-infrared bands. So, the methodologyof using the BRDF model to invert the surface albedo of FY-3C medium resolution imaging spectrometer data is feasible and reliable. The further improvement of the inversion accuracy of FY3C-MERSI surface albedo also depends on the improvement of basic data processing quality, including image geometric correction, calibration, and strict data quality control.     

FY-3C中分辨率成像光谱仪数据的窄波段地表反照率验证研究

地表反照率数据对地表能量平衡和全球变化研究具有重要意义。基于2014年FY-3C卫星250 m分辨率的反射率数据和角度数据,选取非洲及北美洲的4个区域作为研究区,采用RossThick-LiSparseR模型作为BRDF(Bidirectional Reflectance Distribution Function)核模型反演了地表窄波段反照率,得到250 m分辨率的4个窄波段黑空、白空反照率。将反演得到的FY-3C地表窄波反照率产品与MODIS反照率产品（MCD43A3）数据进行了交叉验证,结果表明：FY-3C窄波段反照率与对应MODIS窄波段反照率对比的均方根误差在0.01~0.04,平均偏差(MBIAS)为0.09,FY-3C窄波段反照率与对应的MODIS窄波段反照率在可见光波段、近红外波段有较好的一致性。本研究提升了国产风云极轨卫星的应用范围,可为FY-3C地表反照率业务化产品提供算法支撑。     

Semi-empirical approach for estimating broadband albedo of snow

Snow is a medium that exhibits highly anisotropic reflectance throughout the solar spectrum. The anisotropic nature of snow shows more variability in snow metamorphic processes for wavelengths beyond 1.0 μm than in the visible spectrum. This behavior poses challenges for the development of a model that can retrieve broadband albedo from reflectance measurements throughout the snow season. In this paper, a semi-empirical model is presented to estimate near infrared (0.8-2.5 μm) albedo of snow. This model estimates spectral albedo at a wavelength of 1.240 μm using only three variables: solar zenith angle, scattering angle and measured reflectance, which is used to retrieve near infrared albedo. To form a base for such a model, quantification of reflectance patterns and variability in varying snow condition, i.e. snow grain size, and sun-sensor geometry are prerequisite. In this study the DIScrete Ordinate Radiative Transfer (DISORT) model is used to simulate bi-directional reflectance. The performance of the developed model is evaluated by using DISORT simulated spectral albedo for various snow grain sizes and solar zenith angles, as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ measurements. The developed model is shown to be capable of estimating spectral albedo at 1.240 μm with acceptable accuracy. The mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) in the estimates are found to be 0.053, 0.055 and 0.064, respectively, for a wide range of sun-sensor geometries and snow grain sizes. The model shows better accuracy for spectral albedo estimates than for those computed using the Lambertian reflectance assumption for snow, reducing the error in the range and standard deviation by 75% and 65%, respectively. Applying the model to MODIS, the retrieved albedo is found to be in good quantitative agreement (r = 0.82) with in-situ measurements. These improvements in albedo estimation should allow more accurate use of remote sensing measurements in climate and hydrological models.