Reconstruction of cloud geometry from multi-view satellite images

Reflected solar radiances measured by the pushbroom cameras of the Multiangle Imaging SpectroRadiometer (MISR) on the Terra satellite at nine viewing angles are combined to give eight stereo pairs. These are analyzed with stereo-photogrammetric methods to measure the geometry of a convective cloud system. Both cloud-top heights and cloud sides are retrieved with a precision of about 200-300 m. Two case studies of deep, convective clouds over ocean are considered. The accuracy of the MISR retrieval is tested in the first case study by reference to coincident, higher resolution stereo data from ASTER, showing how the accuracy of the cloud-top height retrieval is improved using the oblique MISR views. In the second case study, the entire cross-section of the cloud aligned with the viewing azimuthal direction is measured, using all nine cameras. The methodology presented is an important step towards more routine retrievals of the 3D geometrical reconstruction of isolated, deep-convective clouds. Such reconstructions are a necessary prerequisite to the subsequent 3D radiative transfer modeling used to aid the remote sensing of the elusive microphysical properties of such clouds.     

Enhancement of the consistency of MODIS thin cirrus with cloud phase by adding 1.6 μm reflectance

Optically thin cirrus derived from the Moderate‐resolution Imaging Spectroradiometer (MODIS) instrument is examined in connection with its cloud phase (ice crystal or liquid droplet) for quasi‐single layer thin cirrus (either single‐layer thin cirrus or thin cirrus overlying low‐level thin clouds) over water surface conditions. Analysing 142 MODIS data samples during the period 1–16 March 2000, it is found that only about 20% of MODIS thin cirrus having a total‐column cloud optical depth less than 2.0 was detected as ice phase. In the present study, MODIS cloud phase datasets are newly reproduced by adding the 1.6 μm reflectance to an existing infrared trispectral algorithm of MODIS which used the 8.7, 11 and 12 mm bands. The percentage of ice phase in the thin cirrus is estimated at about 80% when new cloud phase is incorporated. The increase is found for all regions, in particular in mid‐latitudes.     

A three-channel algorithm for retrieving night-time land surface temperature from MODIS data under thin cirrus clouds

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K.     

Cloud climatology over the oceanic regions adjacent to the Indian Subcontinent: inter-comparison between passive and active sensors

Understanding the cloud vertical structure and its variation in space and time is important to reduce the uncertainty in climate forcing. Here, we present the cloud climatology over the oceanic regions (Arabian Sea, Bay of Bengal, and South Indian Ocean) adjacent to the Indian subcontinent using data from the Multiangle Imaging Spectroradiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), GCM-Oriented CALIPSO Cloud Product (GOCCP), and International Satellite Cloud Climatology Project (ISCCP). Fractional cloud cover (f_c) shows stronger seasonal variations over the Arabian Sea (mean annual f_c lies in the range 0.5–0.61) and Bay of Bengal (mean annual f_c lies in the range 0.69–0.75) relative to the South Indian Ocean (mean annual f_c lies in the range 0.64–0.71). Inter-comparison of statistics from passive (MISR, MODIS and ISCCP) and active (GOCCP) sensors reveals the challenges in interpreting satellite data for climate implications. While MISR detects more low clouds because of its stereo technique, MODIS and ISCCP detect more high clouds because of their radiometric techniques. Therefore, a combination of these two techniques in passive sensors may lead to more realistic understanding of the cloud vertical structure. GOCCP (active sensor) can detect multilayer cloud, but accuracy reduces if the high clouds are optically thick. A dominance of low and high clouds throughout the year is observed in these regions, where cumulus and cirrus dominate among low and high clouds, respectively.     

Performance of the MISR LAI and FPAR algorithm: a case study in Africa

The Multi-angle Imaging SpectroRadiometer (MISR) instrument is designed to provide global imagery at nine discrete viewing angles and four visible/near-infrared spectral bands. The MISR standard products include green leaf area index (LAI) of vegetation and fraction of photosynthetically active radiation absorbed by vegetation (FPAR). These parameters are being routinely processed from MISR data at the Langley Atmospheric Sciences Data Center (ASDC) since October 2002. This paper describes the research basis for transitioning the MISR LAI/FPAR product from beta to provisional status. The quality and spatial coverage of MISR land surface reflectances that are input to the algorithm determine the quality and spatial coverage of the LAI and FPAR products. Therefore, considerable efforts have been expended to analyze the performance of the algorithm as a function of uncertainties of MISR surface reflectances and to establish the convergence property of the MISR LAI/FPAR algorithm, namely, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. An additional objective of the MISR LAI/FPAR algorithm is classification of global vegetation into biome types—information that is usually an input to remote sensing algorithms that use single-angle observations. An upper limit of uncertainties of MISR surface reflectances that allows discrimination between biomes, minimizes the impact of biome misidentification on LAI retrievals, and maximizes the spatial coverage of retrievals was estimated. Algorithm performance evaluated on a limited set of MISR data from Africa suggests valid LAI retrievals and correct biome identification in about 20% of the pixels, on an average, given the current level of uncertainties in the MISR surface reflectance data. The other 80% of the LAI values are retrieved using incorrect information about the type of biome. However, the use of multi-angle data minimizes the impact of biome misidentification on LAI retrievals; that is, with a probability of about 70%, uncertainties in LAI retrievals due to biome misclassification do not exceed uncertainties in the observations. We also discuss in depth the parameters that characterize LAI/FPAR product quality—such as quality assessment (QA) that is available to the users along with the product. The analysis of the MISR LAI/FPAR product presented here demonstrates the physical basis of the radiative transfer algorithm used in the retrievals and, importantly, that the reliability and accuracy of the retrievals increase with increased input information content and accuracy. Further improvements in the quality of MISR surface reflectances are therefore expected to lead to LAI and FPAR retrievals of increasing quality.     

Synergetic use of POLDER and MODIS for multilayered cloud identification

The purpose of this study is to investigate the possibility of identifying overlapping clouds that contain thin cirrus overlying a lower-level water cloud by synergetic use of POLDER-3 (Polarization and Directionality of the Earth Reflectance) and MODIS (MODerate resolution Imaging Spectroradiometer) data. When thin cirrus clouds overlap the liquid cloud layer, the liquid information may be obtained by POLDER observations and the presence of the cirrus may be inferred from the MODIS CO₂-slicing technique. An initial comparison of the POLDER cloud phase and the MODIS cloud-top pressure for one scene over East Asia also shows that a large portion of clouds declared as liquid water clouds by POLDER-3 correspond to the lower cloud-top pressures derived from MODIS. As a result, an overlapped cloud identification method is proposed under the assumption that the multilayered cloud would be present if the POLDER cloud phase is liquid water and the MODIS cloud-top pressure is less than 500 hPa. For the studied scene, the comparison of the multilayered cloud identification results with CloudSat and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations illustrates that the proposed method could detect multilayered clouds when the upper cirrus has a visible optical thickness of less than 2.0. Then the identification results are compared with the MODIS Cloud_Multi_Layer_Flag. It is indicated that the consistency between the multilayered clouds from the proposed synergy and MODIS-operational algorithm increases gradually from over 40% to nearly 100% with the increase of the confidence level of the MODIS multilayered clouds from the lowest to the highest. Further analysis suggests that the majority of multilayered clouds falsely classified as single-layered clouds by the proposed method may correspond to relatively thick cirrus covering lower-level water clouds. Additionally, an index by using the multilayered cloud detection differences from the two methods is proposed to provide some information on the optical thickness of the cirrus covering lower-level water cloud. Finally, quantitative comparisons are extended to four other scenes at different locations by using active measurements. The results also show that the mean visible optical thickness of the high-level clouds of the multilayered clouds detected by both methods (1.57) is remarkably less than that by only MODIS-operational method (2.84), which means that the differences between the results from the two methods are mainly caused by the different sensitivities to the visible optical thickness of the high-level cloud and could be used to indicate the range of the visible optical thickness of the cirrus clouds covering the lower-level water clouds.     

Radiation characteristics of low and high clouds in different oceanic regions observed by CERES and MODIS

Radiative properties measured by the Clouds and the Earth's Radiant Energy System (CERES) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua spacecraft are evaluated for the same types of clouds in selected areas. Individual measurements are analysed statistically to take advantage of both gridded and individual cloud characteristics. The seasonal variations of radiative fluxes for the same types of clouds from different areas are remarkably similar. Although cloud liquid water paths (LWPs) or ice water paths (IWPs) vary considerably for the same types of clouds, their statistical distributions are very stable for different periods and areas, suggesting that the regional differences in dynamics and thermodynamics primarily cause changes in the cloud frequency or coverage and only secondarily in the cloud macrophysical characteristics such as IWPs or LWPs. These results establish a systematic approach of observations for testing modelled cloud statistics and for improving cloud model parameterizations.     

Intercomparisons of cloud-top and cloud-base heights from ground-based Lidar,CloudSat and CALIPSO measurements

This study presents results of the intercomparison of cloud-top height (CTH) and cloud-bottom height (CBH) obtained from a space-borne active sensor Cloud Profiling Radar (CPR), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the space-borne passive sensor Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based Lidar measurements. Three selected cases (one daytime and two night-time cases) involving various cloud conditions such as semi-transparent thin cirrus, opaque thick tropospheric clouds and multi-layered clouds are studied, with special attention to CBH. The space-based CALIOP provides reliable heights of thin high-altitude cirrus clouds containing small ice particles, but the 94 GHz CPR has low sensitivity to these clouds. The CTHs retrieved from the CPR and CALIOP for thick tropospheric clouds are in good agreement with each other. Discrepancies between the CPR and the CALIOP values of the CBH for thick opaque clouds arise from strong Lidar signal attenuations. In cloud-overlap conditions (i.e. multi-layered clouds are present), the CALIOP has difficulties in determining the cloud vertical structure (CVS) for thick clouds underlying thin cirrus clouds due to signal attenuations, whereas the CPR detects the CTH and CBH of both the cloud layers. This fact is also confirmed by the comparison of seasonal variations of occurrences of CBH and CTH retrieved from 1 year measurements. The CBHs derived from the CPR and ground-based Lidar are generally in good agreement with each other. Especially, comparison of CBH between the ground-based Lidar and CPR retrieved from June 2006 to October 2008 shows an excellent linear relationship (coefficient of determination, R ² ～ 0.996).     

The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces

Passive optical multiangle observations make possible the retrieval of scene structural characteristics that cannot be obtained with, or require fewer underlying assumptions than, single-angle sensors. Retrievable quantities include aerosol amount over a wide variety of surfaces (including bright targets); aerosol microphysical properties such as particle shape; geometrically-derived cloud-top heights and 3-D cloud morphologies; distinctions between polar clouds and ice; and textural measures of sea ice, ice sheets, and vegetation. At the same time, multiangle data are necessary for accurate retrievals of radiative quantities such as surface and top-of-atmosphere albedos, whose magnitudes are governed by structural characteristics of the reflecting media and which involve angular integration over intrinsically anisotropic intensity fields. Measurements of directional radiation streams also provide independent checks on model assumptions conventionally used in satellite retrievals, such as the application of 1-D radiative transfer theory, and provide data required to constrain more sophisticated, 3-D approaches. In this paper, the value of multiangle remote sensing in establishing physical correspondence and self-consistency between scene structural and radiative characteristics is demonstrated using simultaneous observations from instruments aboard NASA's Terra satellite (MISR, CERES, ASTER, and MODIS). Illustrations pertaining to the remote sensing of clouds, aerosols, ice, and vegetation properties are presented.     

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m^?2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.     

Validation of cloud property retrievals from MTSAT-1R imagery using MODIS observations

A cloud property retrieval algorithm optimized for five channels (centred at 0.6, 3.7, 6.7, 10.8, and 12.0 μm) has been explored for application to onboard meteorological radiometers on geostationary satellites; however, its validity remains to be established. Here, we present validation results for the cloud properties retrieved by the developed algorithm from the full-disk imagery of the Multi-functional Transport Satellite (MTSAT-1R) for August 2006. The considered cloud properties include cloud phase (CP), cloud optical thickness (COT), effective radius (ER) and cloud top pressure (CTP). Their one-month averages, daily variations, and respective collocated values are compared with the Moderate Resolution Imaging Spectroradiometer cloud data. Our validation results show that an additional 6.7 μm brightness temperature test in CP retrieval identifies water and ice phases that may be overlooked in the 10.8- and 12.0-μm bands. Our method to extract cloud-reflected radiances at the 0.6- and 3.7-μm bands contributes to the accuracy of the COT for values between 5 and 60, and the ER for values less than 40 μm. Estimating high-cloud top pressure from the radiance ratio in the 6.7- and 10.8-μm bands remarkably reduces (by up to 70%) large uncertainties in the CTP, which may be found in the presence of high thin cirrus clouds.     

Difference between forward- and backward-looking bands of GOSAT-2 CAI-2 cloud discrimination used with Terra MISR data

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2017. GOSAT-2 will be equipped with two Earth-observing instruments: the thermal and near-infrared sensor for carbon observation Fourier transform spectrometer 2 (TANSO-FTS-2) and TANSO-cloud and aerosol imager 2 (CAI-2). The FTS-2 data will be used to determine atmospheric concentrations of greenhouse gases, such as CO₂ (carbon dioxide), CH₄ (methane) and CO (carbon monoxide). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands for observing the optical properties of aerosols and clouds, and for monitoring the status of urban air pollution and transboundary air pollution over oceans. An important role of CAI-2 is to perform cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA) will be used for cloud discrimination with CAI-2. The Multi-angle Imaging Spectroradiometer (MISR) aboard the Terra spacecraft provides radiometrically and geometrically calibrated images for spectral bands at nine widely spaced angles. In this study, we examined the difference between forward and backward cloud discrimination by using CLAUDIA with Terra MISR data. The results were as follows: (1) in land areas and polar regions, cloud discrimination results obtained with either band could be used; and (2) in sea areas, cloud discrimination results that include no-sun-glint regions should be used.     

Discriminating sea ice from low-level water clouds in split-window,mid-wavelength IR imagery

A technique is demonstrated to enhance the contrast between sea ice and low-level water clouds. The approach uses the brightness temperature difference (BTD) feature from data collected in the split-window, mid-wavelength infrared (IR) region (i.e. two bands at 3.7 μm and 4.0 μm). These spectral data are available with Visible Infrared Imager Radiometer Suite (VIIRS) moderate-resolution bands M12 and M13, respectively. Under daytime conditions, the data collected in these bands contain energy that originates from both the sun and the Earth–atmosphere system. Due to the small wavelength difference between these, the terrestrial energy component in the bands is typically quite similar as are the surface reflectances for sea ice and ocean surfaces. Thus, the enhanced contrast between sea ice and water clouds, evident in a M12–M13 BTD image, results from differences in the solar energy, which decreases rapidly across this atmospheric window. Observed BTD values for water clouds can exceed 30K, while those for snow-ice fields are typically much smaller (e.g. 0–5K). Thus, water clouds appear bright in the image while sea ice, oceans, and most land surfaces are very dark. The enhanced contrast in the split-window, mid-wave IR BTD image makes it valuable for both image analysis and use in cloud algorithms. In addition, these images support the creation of manually generated cloud masks that have been shown useful for quantitatively evaluating the performance of automated cloud analysis algorithms and cloud forecast models. In this article, the value of 3.7 μm minus 4.0 μm BTD imagery for distinguishing between sea ice and low-level water clouds is shown using VIIRS data collected over the Beaufort Sea on 31 May 2012. Manually generated cloud masks, derived in part from these data, are then used to quantitatively evaluate the effectiveness of various cloud tests, including those used in the VIIRS cloud mask algorithm and the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm. The results strongly suggest that split-window, mid-wavelength IR imagery provides valuable information to help differentiate between clouds and sea ice. It is concluded that collecting data in these mid-wavelength IR bands should be considered part of any future satellite sensor designed for environmental monitoring, especially over the polar regions.     

View angle effects on the discrimination of selected Amazonian land cover types from a principal‐component analysis of MISR spectra

Multi‐angle Imaging Spectroradiometer (MISR) data, collected in four bands and at nine view angles in the Brazilian Amazon region, were used to describe view‐angle effects on the spectral response and discrimination of three forest types; close and open lowland forests, open submontane forest and green/emerging pastures. A principal‐component analysis (PCA) was applied over 450 bidirectional reflectance factor (BRF) MISR spectra (10 pixels, five land covers and nine view angles) to characterize the spectral‐angular variability in the dataset and to identify the best view direction to enhance land cover discrimination. The analysis was extended into the images of the different cameras, which were classified for the presence of the forest covers using the minimum distance of the pixels to the average PC1 and PC2 scores of each forest class calculated from spectra analysis. Results showed an increase in the mean reflectance over the spectral bands (brightness) of the land covers from nadir to extreme viewing, as indicated by the first principal component, especially in the backward direction due to the predominance of sunlit view vegetation components. The transition from the backward (sunlit view surface components) to the forward (shaded view surface components) scattering directions was also characterized by changes in the shape of the BRF spectra, as indicated by decreasing PC2 score or near‐infrared/blue ratio values. The variations in the MISR BRF followed the regularities expected from theory. PCA results also indicated that the best viewing to discriminate the forest types was the backward scattering direction (?26.1° view angle), whereas the less favourable viewing was the forward scattering direction under the view shading condition (e.g. +45.6° view angle). The overall classification accuracy for the three forest types increased from 52.4% at +45.6° view angle to 78.7% at nadir, and to 95.0% at a ?26.1° view angle. From nadir to extreme view angles, directional effects produced a NDVI decrease for the forest types and an NDVI increase for the green and especially emerging pastures. Results demonstrated that data acquisition in off‐nadir viewing may improve the discrimination and mapping of the Amazonian land cover types.     

Aerosol characteristics over Delhi national capital region: a satellite view

Multi-sensor aerosol data sets are analysed to examine the aerosol characteristics over the Delhi national capital region. Both the Multiple-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) capture the seasonal cycle of aerosol optical depth (AOD) as observed by ground-based measurements. However, AOD from MISR shows a low bias relative to AOD from MODIS, which increases linearly at high AOD conditions. A large difference (by >25 W m^–2 per unit AOD) in the top-of-atmosphere direct radiative forcing efficiency derived from MODIS and MISR-retrieved AOD is observed during the winter and pre-monsoon seasons relative to the other seasons. The ubiquitous presence of dust (as indicated by non-spherical particle fraction to AOD and linear depolarization ratio values) is observed throughout the year. The aerosol layer is mostly confined to within 2 km of surface in the winter and post-monsoon seasons, while it expands beyond 6 km in the pre-monsoon and monsoon seasons. Columnar AOD is found to be highly sensitive to aerosol vertical distribution. The applicability of multi-sensor data sets and climatic implications are discussed.     

A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images

Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VENµS (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors.Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively.MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VENµS level 2 processing and will be proposed for SENTINEL-2 level 2 processing.     

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi‐arid region with MISR imagery

In this study we show that multiangle remote sensing is useful for increasing the accuracy of vegetation community type mapping in desert regions. Using images from the National Aeronautics and Space Administration (NASA) Multiangle Imaging Spectroradiometer (MISR), we compared roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with those played by topographic parameters in improving vegetation community type classifications for the Jornada Experimental Range and the Sevilleta National Wildlife Refuge in New Mexico, USA. The BRDF models used were the Rahman–Pinty–Verstraete (RPV) model and the RossThin‐LiSparseReciprocal (RTnLS) model. MISR nadir multispectral reflectance was considered as baseline because nadir observation is the most basic remote sensing observation. The BRDF model parameters and the topographic parameters were considered as additional data. The BRDF model parameters were obtained by inversion of the RPV model and the RTnLS model against the MISR multiangle reflectance data. The results of 32 classification experiments show that the BRDF model parameters are useful for vegetation mapping; they can be used to raise classification accuracies by providing information that is not available in the spectral‐nadir domain, or from ancillary topographic parameters. This study suggests that the Moderate Resolution Imaging Spectroradiometer (MODIS) and MISR BRDF model parameter data products have great potential to be used as additional information for vegetation mapping.     

毫米波/亚毫米波冰云探测辐射计模型仿真

冰云对全球气候的显著影响,使得利用毫米波/亚毫米波辐射计探测冰云分布持续受到关注。为从辐射计亮温测量值中反演出冰云粒子参数,完善的辐射传输正演模型起到非常关键的作用。针对即将发射的冰云成像仪毫米波/亚毫米波星载辐射计,建立起了包含粒子散射在内的完整的辐射传输模型。传输模型读取大气廓线及云层粒子微观参数,仿真计算得到辐射计各通道的辐射亮温。给出了辐射传输模型的详细配置,并分析了不同通道对云层粒子的敏感性。结果表明：冰云成像仪各通道对冰云粒子、雪粒子均有很强的敏感性,但只有低频率通道能探测到水云粒子、雨粒子的变化。辐射计通道中,不同的中心频率对冰晶粒子敏感度不同,从而可使辐射计探测不同高度、不同特性的粒子。对于同一中心频率,不同频偏对应不同的大气透明度,使得辐射计可进一步探测到不同高度的云层信息。仿真工作将会为后续的毫米波/亚毫米波辐射计设计、反演算法研究等打下良好的基础。     

Cloud reflectance variations in channel-3

Abstract

Using photographic terminology for channel 3 pictures in sunshine, one notes that most ice clouds appear black and that cloud shadows are equally dark, but water droplet clouds appear in all shades. These shades also vary greatly with the direction of sunshine relative to the line of sight because scatter is almost entirely by diffraction. Droplets and ice crystals larger than about 10 fan absorb the incident radiation almost completely and it does not penetrate through clouds unless there exist plenty of unobstructed ray paths through the clouds. The reflection from a water surface is almost metallic in intensity so that glint completely saturates the radiometer. There is no evidence of comparable reflection from ice. All snow-covered surfaces, including sea ice, appear black. Stratus cloud shows large variations in reflectance depending on the state of the convection in it which brings very small droplets to the surface. Small particle size causes some contrails and orographic cirrus to appear white although most appear black; old cumulonimbus tops develop pale areas when gravitational settling leaves predominantly very small crystals at the top while still active areas remain black.     

Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) is addressed in this article. We define the goal of scaling as the process by which it is established that LAI values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI retrievals is investigated with 1-km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice versa. A physically based scaling with explicit spatial resolution-dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated. These principles underlie our approach to the production and validation of LAI product from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectroradiometer (MISR) aboard the TERRA platform.