基于多源遥感数据的积雪反照率反演研究

积雪反照率在全球气候系统中的作用显著。由于目前遥感手段的限制,积雪反照率遥感产品存在显著的数据缺失和误差不确定性。研究对遥感积雪反照率反演模型进行精度评估,开展以渐进辐射传输理论(ART)为代表的积雪反照率遥感反演算法验证工作,分别比较MODIS、TM/ETM+数据在反演积雪反照率时的差异和准确性。结果表明:利用ART模型对积雪反射率进行各向异性校正后反演得到的积雪反照率精度优于MOD10A1积雪反照率;高分辨率遥感影像在反演积雪反照率时精度明显高于低分辨率遥感影像;针对地形复杂的高寒山区,尺度效应对积雪反照率的反演会产生极大影响。     

基于渐进辐射传输模型的雪粒径与积雪覆盖面积反演算法

渐进辐射传输模型(Asymptotic Radiative Transfer,ART)广泛应用于雪粒径反演,但是ART模型忽略了像元混合的影响。在本文中,ART模型被应用于混合像元分解之中,考虑积雪粒径变化的影响,提出了两种混合像元分解算法,用以同时获取雪粒径和积雪面积。第一种算法是采用均方根误差指标获取每个像元的最优雪粒径,使用全约束线性分解算法(FCLS)反演积雪面积。另一种算法使用通用梯度下降算法(GRG)获取每个像元的最优粒径,仍然采用全约束线性分解算法获取积雪面积。为了提高模型的运行效率,单波段ART算法用来获取影像的雪粒径,将之作为两种算法的先验知识。同时,MODSCAG模型反演的结果也被用于进行交叉对比,实验结果表明,两种算法比MODSCAG模型的反演结果更为精确。     

MODIS卫星数据地表反照率反演的简化模式

以内蒙西部地区的MODIS遥感图像数据和地表野外同步观测的光谱数据为例,在野外数据量较少且有定标数据的条件下反演地表反照率。使用6S大气1辐射传输模型进行大气校正,并通过MODTRAN4.0模型获取各波段地表入射光通量和窄波段的地表反照率;在窄波段反照率与宽波段反照率之间存在线性关系的前提下,以各波段的入射光通量占总入射通量的比例作为反演参数,实现窄波段到宽波段的反演。反演结果证明此方法简便可行。     

HJ-1A/B星地表反照率生成算法与检验

提出了适合环境与灾害监测预报小卫星-A、B星(简称HJ-1A/B星)CCD相机的大气订正算法,并基于不同地表特性和大气条件下的辐射传输模拟数据,建立HJ-1A/B星的窄波段向宽波段反照率转换的模型.利用多级灰阶靶标实测数据、敦煌检验场实测数据验证了大气订正算法以及转换模型的可靠性和精度,并将HJ-1A/B星影像数据计算的反照率产品与同时相的MODIS反照率产品进行对比分析.结果表明:文章提出的HJ-1A/B星CCD相机大气订正算法可有效校正大气影响;窄波段向宽波段反照率转换模型反演的反照率精度可靠;基于研究成果生成的HJ-1A/B星地表反照率与MODIS反照率产品一致性较好,满足后续遥感数据定量化模型研究的精度需要.     

冰雪反照率测量和反演及其应用研究进展

总结了反照率的相关概念和2种主要的测量方法,分析了诸如雪粒径、含水量、烟尘、雪密度、雪深、太阳天顶角、大气状况和新降雪等因素对反照率的影响,介绍了遥感反演反照率通用的基本方法步骤,包括辐射校正、大气校正、各向异性校正和窄带转宽带反照率。最后阐述了反照率的研究动态和研究应用,如地表能量平衡、冰雪面积制图、确定雪粒径和反演雪线等。     

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。     

被动微波遥感反演雪水当量不确定性因素分析

雪水当量定义为积雪融化后液态水的高度，是描述季节性积雪储量的关键参数。星载被动微波遥感适用于长时间序列、全球尺度的雪水当量监测。但目前的微波辐射传输模型大多忽略或简化了自然界垂直分层结构中的土壤、植被和大气等要素对积雪辐射亮温的影响，特别是植被参数（例如透过率、覆盖度、单次散射反照率）引起的微波亮温变化仍然不清晰。本研究通过构建土壤—积雪—森林—大气微波辐射模型，重点开展被动微波遥感反演雪水当量的不确定性机理研究。通过模型敏感性分析发现：(1)冠层透过率是森林参数中影响微波亮温最敏感的因子，其次是森林覆盖度，而单次散射反照率影响最小；(2)微波亮温随着森林覆盖度的增加而升高，但随着冠层透过率和雪粒径参数的增加而降低，即三者之间存在“抵偿效应”。通过构建的模型模拟数据库和卫星观测对风云三号B星（FY-3B）和The Advanced Microwave Scanning Radiometer 2(AMSR2)雪水当量反演算法进行亮温噪声测试发现：(1)亮温噪声对AMSR2雪水当量反演算法影响较大，特别是在森林像元尤为严重，与算法中表征积雪参数演化的极化因子和森林下雪深校正方法不确定性有关...     

基于MODIS数据的典型能见度条件下大气透过率计算

现有的辐射传输模型仅考虑气溶胶影响下的大气透过率，在能见度低于5km时，会给大气透过率计算带来较大的误差。本文综合考虑影响大气透过率的气溶胶和水汽因素，并利用中光谱分辨率MODIS（moderate resolution imaging spectmradiometer）数据，在特征参数的空间及时间尺度变化均比较大时，对大气透过率进行了定量反演，并进一步建立了整层大气透过率与行星反照率的关系模型，为近地层大气能见度的遥感监测提供了方法。     

关于地表反照率遥感反演的几个问题

分析了地表反照率对陆面辐射能收支以及区域和全球气候的影响,强调了地表反照率是遥感反演陆面参数时的第一重要参数,地表反照率或多波段遥感中不同谱段的地表反射率的准确反演常常是准确估算其它陆面参数如植被和土地利用/土地覆盖等状况的先决条件。在对当前关于反照率的概念及容易混淆的术语进行阐述和说明的基础上,简述了遥感反演地表反照率的步骤和主要难点的解决方法,进而对常用陆面过程模式计算地表反照率的过程作了分析,并将其结果与MODIS有关产品进行了比较,强调了遥感与陆面过程模式和气候模式的结合。     

基于Ross Thick—LiSparseR及Ross Thick—LiTransit的核驱动BRDF计算与评价

核驱动BRDF模型是MODIS传感器用于计算全球地表反照率的AMBRALS算法系统所使用的模型。目前AMBRALS算法系统的几何光学散射核为LiSparseR核,它不能完全避免反射率为负的情况,并在遥感像元尺度应用互易原理的理论依据不充足。几何光学核LiTransit核兼有LiSparse核向LiDense核过渡的优点,比LiSparseR核更符合几何光学模型的基本原理。本文研究了基于LiSparseR核及LiTransit核驱动的方向反射率计算方法,并进行了比较验证。     

Retrieval of snow grain size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The snow grains are modeled as fractal rather than spherical particles in order to account for their irregular shape. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived grain size shows a good sensitivity to snow melting and snow precipitation events. Pre-processing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust discrimination of clouds over bright snow and ice. Because in-situ grain size measurements over Greenland were not available at the time of this work, the validation was performed using data of Aoki et al. (Aoki, T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K., et al. (2007). ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data. Remote Sensing of Environment, 111, 274-290) collected at Barrow (Alaska, USA), and Saroma, Abashiri and Nakashibetsu (Japan) in 2001-2005. The retrievals correlate well with measurements in the range of radii ~ 0.1-1 mm, although retrieved optical diameter may be about a factor of 1.5 lower than the physical measured diameter. As part of validation analysis for Greenland, the derived grain size from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM/I radiometer which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS grain size was compared with predictions of the snow model CROCUS driven by measurements of the automatic weather stations of the Greenland Climate Network. We found that the MODIS value is on average a factor of two smaller than CROCUS grain size. This result agrees with the direct validation analysis indicating that the snow reflectance model may need a “calibration” factor of ~ 1.5 for the retrieved grain size to match the physical snow grain size. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of snow grain size for four permanent sites, the paper presents maps of this important parameter over the Greenland ice sheet for the March-September period of 2004.     

Accuracy assessment of the MODIS 16-day albedo product for snow: comparisons with Greenland in situ measurements

The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day albedo product (MOD43) is assessed using ground-based albedo observations from automatic weather stations (AWS) over spatially homogeneous snow and semihomogeneous ice-covered surfaces on the Greenland ice sheet. Data from 16 AWS locations, spanning the years 2000-2003, were used for this assessment. In situ reflected shortwave data were corrected for a systematic positive spectral sensitivity bias of between 0.01 and 0.09 on a site-by-site basis using precise optical black radiometer data. Results indicate that the MOD43 albedo product retrieves snow albedo with an average root mean square error (RMSE) of ±0.07 as compared to the station measurements, which have ±0.035 RMSE uncertainty. If we eliminate all satellite retrievals that rely on the backup algorithm and consider only the highest quality results from the primary bidirectional reflectance distribution function (BRDF) algorithm, the MODIS albedo RMSE is ±0.04, slightly larger than the in situ measurement uncertainty. There is general agreement between MODIS and in situ observations for albedo <0.7, while near the upper limit, a −0.05 MODIS albedo bias is evident from the scatter of the 16-site composite.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

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.     

Snow optical properties for different particle shapes with application to snow grain size retrieval and MODIS/CERES radiance comparison over Antarctica

We investigated the single scattering optical properties of snow for different ice particle shapes and degrees of microscopic scale roughness. These optical properties were implemented and tested in a coupled atmosphere-snow radiative transfer model. The modeled surface spectral albedo and radiance distribution were compared with surface measurements. The results show that the reflected radiance and irradiance over snow are sensitive to the snow grain size and its vertical profile. When inhomogeneity of the particle size distribution in the vertical is taken into account, the measured spectral albedo can be matched, regardless of the particle shapes. But this is not true for the modeled radiance distribution, which depends a lot on the particle shape. The usual “equivalent spheres” assumption significantly overestimates forward reflected radiances, and underestimates backscattering radiances, around the principal plane. On average, the aggregate shape assumption has the best agreement with the measured radiances to a mean bias within 2%.The snow optical properties with the aggregate assumption were applied to the retrieval of snow grain size over the Antarctic plateau. The retrieved grain sizes of the top layer showed similar and large seasonal variation in all years, but only small year to year variation. Using the retrieved snow grain sizes, the modeled spectral and broadband radiances showed good agreements with MODIS and CERES measurements over the Antarctic plateau. Except for the MODIS 2.13 μm channel, the mean relative model-observation differences are within few percent. The modeled MODIS radiances using measured surface reflectance at Dome C also showed good agreement in visible channels, where radiation is not sensitive to snow grain size and the measured surface bidirectional reflectance is applicable over the Antarctic plateau. But modeled radiances using local, surface-measured reflectance in the near infrared yielded large errors because of the high sensitivity to the snow grain size, which varies spatially and temporally. The CERES broadband shortwave radiance is moderately sensitive to the snow grain size, comparable to the MODIS 0.86 μm channel. The variation of broadband snow reflectance due to the seasonal variation in snow grain size is about 5% in a year over the Antarctic plateau. CERES broadband radiances simulated with grain sizes retrieved using MODIS are about 2% larger than those observed.     

Retrieval of subpixel snow covered area, grain size, and albedo from MODIS

We describe and validate a model that retrieves fractional snow-covered area and the grain size and albedo of that snow from surface reflectance data (product MOD09GA) acquired by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The model analyzes the MODIS visible, near infrared, and shortwave infrared bands with multiple endmember spectral mixtures from a library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model specific to a scene's illumination geometry; spectra for vegetation, rock, and soil were collected in the field and laboratory. We validate the model with fractional snow cover estimates from Landsat Thematic Mapper data, at 30 m resolution, for the Sierra Nevada, Rocky Mountains, high plains of Colorado, and Himalaya. Grain size measurements are validated with field measurements during the Cold Land Processes Experiment, and albedo retrievals are validated with in situ measurements in the San Juan Mountains of Colorado. The pixel-weighted average RMS error for snow-covered area across 31 scenes is 5%, ranging from 1% to 13%. The mean absolute error for grain size was 51 µm and the mean absolute error for albedo was 4.2%. Fractional snow cover errors are relatively insensitive to solar zenith angle. Because MODSCAG is a physically based algorithm that accounts for the spatial and temporal variation in surface reflectances of snow and other surfaces, it is capable of global snow cover mapping in its more computationally efficient, operational mode.     

MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland

In situ measurements of snow albedo at five stations along a north-south transect in the dry-snow facies of the interior of Greenland follow the theoretically expected dependence of snow albedo with solar zenith angle (SZA). Greenland Climate Network (GC-Net) measurements from 1997 through 2007 exhibit the trend of modest surface brightening with increasing SZA on both diurnal and seasonal timescales. SZA explains up to 50% of seasonal albedo variability. The two other environmental factors considered, temperature and cloudiness, play much less significant roles in seasonal albedo variability at the five stations studied. Compared to the 10-year record of these GC-Net measurements, the five-year record of MODIS satellite-retrieved snow albedo shows a systematic negative bias for SZA larger than about 55°. Larger bias of MODIS snow albedo exists at more northerly stations. MODIS albedos successfully capture the snow albedo dependence on SZA and have a relatively good agreement with GC-Net measurements for SZA < 55°. The discrepancy of MODIS albedo with in situ albedo and with theory is determined mainly by two related factors, SZA and retrieval quality. While the spatiotemporal structure, especially zonal features, of the MODIS-retrieved albedo may be correct for large SZA, the accuracy deteriorates for SZA > 55° and often becomes physically unrealistic for SZA > 65°. This unphysical behavior biases parameterizations of surface albedo and restricts the range of usefulness of the MODIS albedo products. Seasonal-to-interannual trends in surface brightness in Greenland, and in polar (i.e., large SZA) regions in general, and model simulations of these trends, should be evaluated in light of these limitations.     

MODIS、MISR与POLDER 3种全球地表反照率卫星反演产品的比较与分析

对MODIS、MISR和POLDER 3种由多角度卫星观测反演得到的全球地表反照率数据(无冰雪覆盖区域)短波波段(SW,0.3~5 μm)与可见光波段(VIS,0.3~0.7 μm)的黑空地表反照率(DHR)进行了相互比较。3种产品6年平均的全球均值存在显著差异,其值从大到小依次为POLDER\,MISR和MODIS。3种产品的纬向平均在35°N以北区域表现出较大的差异。3种产品彼此之间相关性比较高,其中MODIS与MISR产品的相关性最强,MISR与POLDER产品的相关性最低,短波波段的相关系数(r) 分别为0.939与0.911。3种产品在可见光波段的相关性大于短波波段。在不同地表类型上,3种产品表现出了大致相似的差异,表明其对地表类型并不敏感。对气溶胶的分析表明:MODIS与MISR的550 nm气溶胶光学厚度(AOD)较为相似,其差异不足以解释DHR的差别;但是POLDER的865 nm AOD明显小于MISR,因此可以认为是由于POLDER的AOD估算偏低,导致了POLDER的DHR值大于MODIS与MISR。     

A novel technique to detect turbid water and mask clouds in Greenland fjords

The existing National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD35_L2 cloud mask performance was assessed using imagery of Kangerlussuaq Fjord, Greenland. It was found to perform suboptimally, especially near glacially fed river mouths, due to sediment-laden water being highly reflective in near-infrared wavelengths. In situ observations of suspended sediment concentration (SSC) were compared against MODIS band 1 and 2 reflectance and show a reflectance saturation effect past which increasing SSC values fail to increase the reflectance of water. A new mask optimized for turbid waters uses the reflectance saturation effect observed in high-SSC water. This new mask and a custom adaption of individual tests within the existing MOD35_L2 cloud mask were tested. The new mask outperforms the standard MOD35_L2 mask and the combination of new and custom masks was shown to screen out clouds very well in Greenland fjords. It is thought that with local measurements of MODIS band 1 and 2 reflectance values of turbid water to serve as training data, this mask should perform equally well in other turbid coastal waters.     

Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months

The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary satellite Kalpana‐1 Very High Resolution Radiometer (K1VHRR) sensor in the visible (VIS), water vapour (WV) and thermal infrared (TIR) bands from noontime (0900 GMT) observations were processed to retrieve these quantities in clear skies for five winter months. Cloud detection was carried out using bispectral threshold tests (in both VIS and TIR bands) in a dekadal time series. Surface albedo was retrieved using a simple atmospheric transmission model. K1VHRR albedo was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA noontime albedo over different land targets (agriculture, forest, desert, scrub and snow) that showed minimum differences over agriculture and forest. The comparison of spatial albedo over different landscapes yielded a root mean square deviation (RMSD) of 0.021 in VHRR albedo (9% of MODIS albedo). A mono‐window algorithm was implemented with a single TIR band to retrieve the LST. Its accuracy was also verified over different land targets by comparison with aggregated MODIS AQUA LST. The maximum RMSD was obtained over agriculture. Spatial comparison of VHRR and AQUA LSTs over homogeneous and heterogeneous landscape cutouts revealed an overall RMSD of 2.3 K. An improvement in the retrieval accuracy is expected to be achieved with atmospheric products from the sounder and split thermal bands in the imager of future INSAT 3D missions.