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.     

基于ART模型的MODIS积雪反照率反演研究

积雪反照率是研究局地或全球的能量收支平衡和气候变化中的重要参数,遥感反演为积雪反照率的获取提供了便利的手段。积雪反照率大小主要取决于积雪的自身物理属性(雪粒径、形状和污染物等因子)以及天气状况,遥感反演反照率大多基于双向反射模型(BRDF),积雪BRDF模型常使用积雪辐射传输模型获得。采用考虑了雪粒径、粒子形状以及污染物影响的渐进辐射传输理论(ART)模型,建立了MODIS积雪反照率反演算法,得到了MODIS 8d合成积雪反照率产品。将此算法应用于具有均一积雪地表的格陵兰岛地区,并使用GC-Net实测数据进行了验证,反演的总均方根误差(RMSE)为0.018,相关系数(r)为0.83,结果表明考虑了积雪特性的ART模型能够较好地反演积雪反照率,而且反演需要的参数较少。     

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.     

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.     

Validation of MISR land surface broadband albedo

Land surface broadband albedo is a critical variable for many scientific applications. Due to the scarcity of spectral albedo measurements of the Earth's surface environments, it is useful to construct broadband albedo from spectral albedo data obtained by multi‐angle satellite observations. The Multi‐angle Imaging SpectroRadiometer (MISR) onboard NASA's Earth Observing System (EOS) Terra satellite provides land surface albedo products from multi‐angular observations; however, the products have not been comprehensively validated. We convert MISR spectral albedos to total shortwave albedos and validate them using ground measurements at different validation sites. For most surface types, a published narrowband to broadband conversion formula was used, but a new conversion formula for snow and ice covered sites is developed in this study where the spectral range of the instrument is different. Several comparisons are made: (1) between MISR directional‐hemispherical reflectance (DHR) or albedo and MODIS (Moderate Resolution Imaging Spectroradiometer) DHR; and (2) between MISR spectral DHR and bi‐hemispherical reflectance (BHR). The results show that: (1) both the value and the temporal trends of the MISR shortwave albedo and the ground measured shortwave albedo are in good agreement, with the exception of the snow and ice sites; (2) the MISR DHR conforms well to MODIS DHR; and (3) the values of MISR DHR and BHR are nearly identical.     

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.     

The effect of anisotropic reflectance on imaging spectroscopy of snow properties

How does snow's anisotropic directional reflectance affect the mapping of snow properties from imaging spectrometer data? This sensitivity study applies two spectroscopy models to synthetic images of the spectral hemispherical-directional reflectance factor (HDRF) with prescribed snow-covered area and snow grain size. The MEMSCAG model determines both sub-pixel snow-covered area and the grain size of the fractional snow cover. The Nolin/Dozier model analyzes the ice absorption feature that spans wavelength λ≅1.03 μm to estimate snow grain radius when the pixel is fully snow-covered. Retrievals of subpixel snow-covered area with MEMSCAG are progressively more sensitive to the HDRF as grain size decreases, solar zenith angle increases, and fractional snow cover increases. The model overestimates snow cover in the forward reflectance angles by up to +20% and underestimates it in the backward reflectance angles by as much as −15%. Grain size retrievals from both MEMSCAG and Nolin/Dozier are more sensitive to anisotropy as grain size and solar zenith angle increase. MEMSCAG retrievals of grain size are insensitive to snow-covered area. The largest inferred grain sizes occur around a peak in the backward reflectance angles and the smallest generally occur at the largest view angles in the forward direction. Retrievals of albedo from MEMSCAG and Nolin/Dozier are similarly sensitive to anisotropy, with albedo errors up to 5% for a 30° solar zenith angle and up to 10% at 60°. The albedo differences between the two models are less than 0.015 for all grain sizes and solar zenith angles.     

Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

We describe and validate an automated model that retrieves subpixel snow-covered area and effective grain size from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The model analyzes multiple endmember spectral mixtures with a spectral library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model; spectra for vegetation, rock, and soil were collected in the field and laboratory. For three AVIRIS images of Mammoth Mountain, California that span common snow conditions for winter through spring, we validate the estimates of snow-covered area with fine-resolution aerial photographs and validate the estimates of grain size with stereological analysis of snow samples collected within 2 h of the AVIRIS overpasses. The RMS error for snow-covered area retrieved from AVIRIS for the combined set of three images was 4%. The RMS error for snow grain size retrieved from a 3×3 window of AVIRIS data for the combined set of three images is 48 μm, and the RMS error for reflectance integrated over the solar spectrum and over all hemispherical reflectance angles is 0.018.     

Analysis of broadband surface BRDFs derived from TOA SW CERES measurements for surfaces classified by the IGBP land cover

Most studies on the reflectance properties of the Earth's surface are addressed estimating the bidirectional reflectance distribution function (BRDF) of high spatial resolution and high spectral resolution satellite measurements. This article assesses the development of broadband (BB) BRDFs from radiances corresponding to large footprints classified according to the International Geosphere-Biosphere Programme (IGBP) land-cover classification. Top-of-atmosphere (TOA) shortwave (SW) CERES (Clouds and the Earth's Radiant Energy System) measurements are employed to invert the bidirectional reflectance factor (BRF) Rahman–Pinty–Verstraete (RPV) model for regions identified with the same IGBP type. The inversion of this non-linear parametric model is optimized to improve the computation efficiency and merged into a radiative transfer model to correct the surface radiances for the atmospheric effect. Analysis of the nature of the reflectance field simulated for several regions selected for every IGBP type determines whether the creation of general BRF models for surfaces defined by the same IGBP land cover is feasible. According to the results gathered in this study, the BB BRDFs for regions classified by the IGBP classification show values for the coefficient of variation (CV) between 3.5% and 44.1%. Consequently, the high differences achieved in the reflectance fields discourage the creation of BRDFs based on the IGBP land types.     

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.     

Arctic tundra albedo and its estimation from spectral hemispheric reflectance

We present the results of a field experiment in which the nearly complete bidirectional reflectance distribution function of Alaskan arctic tundra sites early in the growing season is measured by the PARABOLA instrument. The spectral hemispheric reflectances were computed by angular integration of these measurements for three wavebands: red (650-670nm), near-infrared (810-840nm) and shortwave infrared (1620-1690 nm). Total albedo was then estimated by weighting the spectral hemispheric reflectances by the fraction of total solar irradiance in three broadband spectral regions (300-700, 700-1300 and 1300-4000nm) and representing each spectral region by the narrowband PARABOLA measurements. These calculations resulted in albedo estimates with a mean relative error of 15.7 per cent as compared to pyranometer measured albedo. Since vegetation reflectance varies significantly over each of the three broadband regions, additional reflectance weighting factors were computed from a combination of high spectral resolution canopy reflectance data and corresponding computed spectral solar irradiance. This additional reflectance weighting resulted in a reduction in the mean relative error to 7.5 per cent relative to pyranometer measured albedo. It is noted that the three spectral bands of the PARABOLA instrument data reported here are similar to those of the spectral wavebands planned for future Advanced Very High Resolution Radiometer (AVHRR) sensors on National Oceanic and Atmospheric Administration (NOAA) satellites. Therefore the results and techniques presented here may be useful for future global albedo estimation utilizing AVHRR sensors. The analysis presented here may also be applied to albedo estimation from satellite sensors with higher spectral resolution and more complete spectral coverage, such as the future orbiting MODIS sensor, in which the errors of spectral reflectance weighting will be reduced considerably due to a more complete sampling of the reflected spectrum.     

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain,China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.     

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.     

Field-based spectral reflectance measurements of seasonal snow cover in the Indian Himalaya

In the present study, spectroradiometer (350–2500 nm) experiments are carried out in the field to understand the influence of snow grain size, contamination, moisture, ageing, snow depth, slope / aspect on spectral reflectance and to determine the sensitive wavelengths for mapping of snow and estimation of snow characteristics using satellite data. The observations suggest that, due to ageing and grain-size variation, the maximum variations in reflectance are observed in the near-infrared region, i.e. around 1040–1050 nm. For varying contamination and snow depth, the maximum variations are observed in the visible region, i.e. around 470 and 590 nm, respectively. For the moisture changes, the maximum variations are observed around 980 and 1160 nm. Based on the spectral signatures of seasonal snow, the normalized difference snow index (NDSI) is studied, and snow indexes, such as grain and contamination indexes, are proposed. The study also suggests that the NDSI increases with ageing, grain size and moisture content. The NDSI values remain constant with variations in slope and aspect. Attempts are made to estimate seasonal snow characteristics using multispectral Advanced Wide Field Sensor (AWiFS) Indian Remote Sensing (IRS-P6) and Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite data and validated with snow-meteorological observatory data of the study area.     

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。     

Spatial relationships between snow contaminant content, grain size, and surface temperature from multispectral images of Mt. Rainier, Washington (USA)

We use multispectral MODIS/ASTER Airborne Simulator (MASTER) data collected at Mt. Rainier, Washington (USA) to map spatial covariance between snowpack properties and to evaluate techniques for quantitative estimation of reflectance, grain size, and temperature. The late-August MASTER images reveal a distinct pattern of snow contaminant content, grain size, and temperature related to a recent snowfall and late-summer melting. Spatial correlation between grain size and temperature patterns suggests that rapid destructive metamorphism of the fresh snow occurred when temperatures were near 0 °C. We use 10 specific locations to evaluate hemispherical-directional reflectance factor (HDRF), grain size, and temperature retrievals. We map relative snow contaminant content using visible (0.4-0.8 μm) HDRF spectra. Atmospheric correction and topographic modeling limit the accuracy of HDRF estimates. We use MASTER-derived spectra near 1.8 and 2.2 μm to estimate optical grain size (by comparison to modeled layers of ice spheres) and physical grain size (by comparison to measured spectra with known physical grain size and by correlation to ground measurements). Estimated physical grain sizes were less than estimated optical grain sizes. Differing definitions of optical and physical grain sizes could contribute to this discrepancy. Limitations at 1.8 and 2.2 μm, including reduced discrimination between larger grain radii (>∼500 μm physical, >∼200 μm optical) and low signal-to-noise ration with atmospheric effects and decreasing solar irradiance, suggest that grain size retrieval may be improved at other wavelengths (e.g., 1.1 μm). Accounting for uncertainty in emissivity, atmospheric correction, and detector noise, we estimate systematic errors in our radiant temperatures at <1.8 °C. This study shows both strengths and limitations for coregistered visible, short-wave infrared, and thermal infrared images to estimate snowpack properties and reveal their spatial coherence.     

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。     

Sizing snow grains using backscattered solar light

In this article, we describe a technique to determine dry snow grain size from optical observations. The method is based on analysis of the snow reflectance in the near-infrared region, in particular, the Medium Resolution Imaging Spectrometer (MERIS) band at 865 nm, which is common to many spaceborne optical sensors, is used. In addition, the algorithm is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1240 nm band. It is found that bands located at 1020 and 1240 nm are the most suitable for snow grain size remote-sensing applications. The developed method is validated using MODIS observations over flat snow deposited on a lake ice in Hokkaido, Japan.     

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。     

基于中国通量网的MODIS短波反照率验证与分析

遥感地表反照率产品的验证与分析是将其应用于环境研究的基础。采用中国通量网的地表实测短波反照率数据对MODIS反照率产品进行对比和分析,针对选取的8个地面站点,提取了2004年的MODIS反照率产品并进行验证。这些站点的植被覆盖情况涵盖了草地、森林和农业用地。结果显示MODIS在多数情况下能提供准确的地表反照率产品。针对各个站点的误差、均方根误差、相关系数分析都显示了这个结果,总体反演误差在0.002左右。较大的误差出现在有冰雪影响的时候,排除受积雪影响的数据,总体均方根误差可达0.028。分析了引起误差的原因并提出了改进意见。