Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

The methodologies used by the Satellite Application Facility on Land Surface Analysis (Land SAF) for retrieving emissivity are presented here. In the first approach, i.e., the vegetation cover method (VCM), the land surface emissivity (EM) is computed for Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared channels and for the 3- to 14- range using information on the pixel fraction of vegetation cover (FVC). The VCM uses a lookup table, which takes into account the channel's spectral response function, and laboratory reflectance spectra for different materials. The accuracy of the VCM depends on the reliability of FVC and the land cover classification. The EM for SEVIRI split-window channels is primarily used as an internal product by Land SAF for land surface temperature (LST) estimations. However, sensitivity studies show that LST often fails to meet the required accuracy of 2 K over desert and semiarid regions, where the VCM is unable to model the EM spatial variability, which is mostly associated with soil composition. Moreover, it is also over such areas where the atmosphere is generally dry that the impact of EM uncertainties on LST is largest. A second approach to determine the EM for SEVIRI split-window channels is currently being tested. This methodology allows the simultaneous retrieval of LST and channel EMs with the assumption that the latter remain constant. The channel EMs are then averaged over a 22-day period to filter out the noise in the retrievals. A first analysis of the maps obtained for an area within Northern Africa shows spatial patterns with features also present in the surface albedo.     

Development of a technique to assess snow-cover mapping errors fromspace

Following the December 18, 1999, launch of the Earth Observing System (EOS) Terra satellite, daily snow-cover mapping is performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using moderate resolution imaging spectroradiometer (MODIS) data. This paper describes a technique for calculating global-scale snow mapping errors and provides estimates of Northern Hemisphere snow mapping errors based on prototype MODIS snow mapping algorithms. Field studies demonstrate that under cloud-free conditions, when snow cover is complete, snow mapping errors are small (<1%) in all land covers studied except forests, where errors are often greater and more variable. Thus, the accuracy of Northern Hemisphere snow-cover maps is largely determined by percent of forest cover north of the snowline. From the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the authors classify the Northern Hemisphere into seven land-cover classes and water. Estimated snow mapping errors in each of the land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. The resulting average monthly errors are expected to vary, ranging from about 5-10%, with the larger errors occurring during the months when snow covers the boreal forest in the Northern Hemisphere. As determined using prototype MODIS data, the annual average estimated error of the future Northern Hemisphere snow-cover maps is approximately 8% in the absence of cloud cover, assuming complete snow cover. Preliminary error estimates will be refined after MODIS data have been available for about one year     

Subpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization

This paper examines the subpixel analysis of Landsat ETM/sup +/ data to estimate the percent cover of impervious surface, lawn, and woody tree cover in typical urban/suburban landscapes. By combining Self-Organizing Map (SOM), Learning Vector Quantization (LVQ), and Gaussian Mixture Model (GMM) methods, the posterior probability of the various land cover components were estimated for each pixel as a means of subpixel analysis. The estimation of impervious surface and the differentiation of urban vegetation-grass versus woody tree cover-are the main objectives of this paper. Overall, the output estimates compared favorably with those obtained using higher spatial resolution aerial photograph and IKONOS satellite image and traditional hard classification techniques as independent reference. The SOM-LVQ-GMM model showed a moderate degree of similarity in the estimates of impervious surface [root mean-square errors (RMSEs) of 相似文献   

Spatially complete global spectral surface albedos: value-added datasets derived from Terra MODIS land products

Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo, and direct-beam directional hemispherical (black-sky) albedo from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the National Aeronautics and Space Administration's Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, climate models, and global change research projects. An ecosystem-dependent temporal interpolation technique is described that has been developed to fill missing or seasonally snow-covered data in the official MOD43B3 albedo product. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data. The resulting snow-free value-added products provide the scientific community with spatially and temporally complete global white- and black-sky surface albedo maps and statistics. These products are stored on 1-min and coarser resolution equal-angle grids and are computed for the first seven MODIS wavelengths, ranging from 0.47-2.1 /spl mu/m and for three broadband wavelengths 0.3-0.7, 0.3-5.0, and 0.7-5.0 /spl mu/m.     

BRDF models to predict spectral reflectance and emissivity in thethermal infrared

This paper presents modifications to the linear kernel bidirectional reflectance distribution function (BRDF) models from Roujean et al. and from Wanner et al. that extend the spectral range into the thermal infrared (TIR). The present authors application is to synthesize the TIR optical properties of a scene pixel from laboratory component measurements. The angular reflectance and emissivity are needed to convert the radiance of a pixel as measured from space to land-surface temperature. The kernel models will be applied to develop a look-up table for the MODIS land-surface temperature algorithm to estimate the spectral, angular scene emissivity from land cover classification. A shrub scene and a dense canopy scene illustrate qualitative differences in angular emissivity that would not be evident without the kernel model modifications. They conclude that the modified models provide a simple and efficient way to estimate scene optical properties over a wide spectral range     

Evaluation of radiative transfer simulations over bright desert calibration sites

The Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Meteosat Second Generation main radiometer, measures the reflected solar radiation within three spectral bands centered at 0.6, 0.8, and 1.6 /spl mu/m, and within a broadband. This broadband is similar to the solar channel of the radiometer onboard the first generation of METEOSAT satellites. The operational absolute calibration of these channels relies on modeled radiances over bright desert sites, as no in-flight calibration device is available. These simulated radiances represent, therefore, the "reference" against which SEVIRI is calibrated. The present study describes the radiative properties of these targets and evaluates the uncertainties associated with the characterization of this "reference", i.e. the modeled radiances. To this end, top-of-atmosphere simulated radiances are compared with several thousands of calibrated observations acquired by the European Remote Sensing 2/Along-Track Scanning Radiometer 2 (ERS2/ATSR-2), SeaStar/Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Syste/spl grave/me Pour l'Observation de la Terre 4 (SPOT-4/VEGETATION), and the Environmental Research Satellite/Medium Resolution Imaging Spectrometer (ENVISAT/MERIS) instruments over the SEVIRI desert calibration sites. Results show that the mean relative bias between observation and simulation does not exceed 3% in the red and near-infrared spectral bands with respect to the first two instruments.     

A physics-based algorithm for retrieving land-surface emissivityand temperature from EOS/MODIS data

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NEΔT) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 μm IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K     

Effects of neglecting polarization on the MODIS aerosol retrieval over land

Reflectance measurements in the visible and infrared wavelengths, from the Moderate Resolution Imaging Spectroradiometer (MODIS), are used to derive aerosol optical thicknesses (AOTs) and aerosol properties over ocean and land surfaces, separately. Both algorithms employ radiative transfer (RT) code to create lookup tables, simulating the top-of-atmosphere (TOA) reflectance measured by the satellite. Whereas the algorithm over ocean uses a vector RT code that includes the effects of atmospheric polarization, the algorithm over land assumes scalar RT, thus neglecting polarization effects. In the red (0.66 /spl mu/m) and infrared (2.12 /spl mu/m) MODIS channels, scattering by molecules (Rayleigh scattering) is minimal. In these bands, the use of a scalar RT code is of sufficient accuracy to model TOA reflectance. However, in the blue (0.47 /spl mu/m), the presence of larger Rayleigh scattering (optical thickness approaching 0.2) results in nonnegligible polarization. The absolute difference between vector- and scalar-calculated TOA reflectance, even in the presence of depolarizing aerosols, is large enough to lead to substantial errors in retrieved AOT. Using RT code that allows for both vector and scalar calculations, we examine the reflectance differences at the TOA, assuming discrete loadings of continental-type aerosol. We find that the differences in blue channel TOA reflectance (vector-scalar) may be greater than 0.01 such that errors in derived AOT may be greater than 0.1. Errors may be positive or negative, depending on the specific geometry, and tend to cancel out when averages over a large enough sample of satellite geometry. Thus, the neglect of polarization introduces little error into global and long-term averages, yet can produce very large errors on smaller scales and individual retrievals. As a result of this study, a future version of aerosol retrieval from MODIS over land will include polarization within the atmosphere.     

Impact of horizontal and vertical heterogeneities on retrievals using multiangle microwave brightness temperature data

This paper investigates the impact of heterogeneity at the land surface on geophysical parameters retrieved from multiangle microwave brightness temperature data, such as would be obtained from the Soil Moisture and Ocean Salinity (SMOS) mission. Synthetic brightness temperature data were created using the Common Land (land surface) Model, coupled with a microwave emission model and set within the framework of the North American Land Data Assimilation System (NLDAS). Soil moisture, vegetation optical depth, and effective physical temperature were retrieved using a multiobjective calibration routine similar to the proposed SMOS retrieval algorithm for a typical on-axis range of look angles. The impact of heterogeneity both in the near-surface profiles of soil moisture and temperature and in the land cover on the accuracy of the retrievals was examined. There are significant errors in the retrieved parameters over regions with steep gradients in the near-surface soil moisture profile. These errors are approximately proportional to the difference in the soil water content between the top (at 0.7 cm) and second layer (at 2.7 cm) of the land surface model. The errors resulting from heterogeneity in the land cover are smaller and increase nonlinearly with increasing land-surface heterogeneity (represented by the standard deviation of the optical depth within the pixel). The most likely use of retrieved soil moisture is through assimilation into an LDAS for improved initiation of weather and climate models. Given that information on the soil moisture profile is already available within the LDAS, the error in the retrieved soil moisture as a result of the near-surface profile can be corrected for. The potential errors as a result of land-surface heterogeneity can also be assessed for use in the assimilation process.     

A comparison of cloud droplet radii measured from space

Cloud droplet effective radius (CDR) can be estimated from the spectral signature of cloud reflectance. The technique has been applied to measurements of the Advanced Very High Resolution Radiometer instrument and more recently to the Moderate Resolution Imaging Spectroradiometer (MODIS). Another technique relies on the directional signature of the polarized reflectance and has been applied to observations from Polarization and Directionality of the Earth's Reflectances (POLDER) onboard Advanced Earth Observation Satellite (ADEOS). Although the latter technique requires very specific conditions, we argue that, when applicable, it is very accurate. A large fraction of successful POLDER estimates are derived from measurements over stratocumulus cloud fields. During portions of 2003, POLDER and MODIS acquired near coincident observations. The data can then be used for an evaluation of the two CDR products. The two datasets are highly correlated over the oceans albeit with a MODIS high bias of about 2 /spl mu/m. The correlation breaks down when POLDER retrieves small droplets (less than 7 /spl mu/m), which occurs over most land surfaces as well as polluted oceanic areas. We discuss the possible causes for biases and errors. Although differences in the two CDR estimates are expected because of the differences in the spatial scale and vertical weighting function, we did not find a fully satisfactory explanation for the bias and lack of correlation over land surfaces. It seems, however, that the spatial variability as seen by MODIS is larger than that deduced from POLDER measurements, in particular over land surfaces.     

Remote sensing of suspended sediments and shallow coastal waters

Ocean color sensors were designed mainly for remote sensing of chlorophyll concentrations over the clear open oceanic areas (Case 1 water) using channels between 0.4-0.86 /spl mu/m. The Moderate Resolution Imaging Spectroradiometer (MODIS) launched on the National Aeronautics and Space Administration Terra and Aqua spacecrafts is equipped with narrow channels located within a wider wavelength range between 0.4-2.5 /spl mu/m for a variety of remote sensing applications. The wide spectral range can provide improved capabilities for remote sensing of the more complex and turbid coastal waters (Case 2 water) and for improved atmospheric corrections for ocean scenes. We describe an empirical algorithm that uses this wide spectral range to identify areas with suspended sediments in turbid waters and shallow waters with bottom reflections. The algorithm takes advantage of the strong water absorption at wavelengths longer than 1 /spl mu/m that does not allow illumination of sediments in the water or a shallow ocean floor. MODIS data acquired over the east coast of China, west coast of Africa, Arabian Sea, Mississippi Delta, and west coast of Florida are used.     

An algorithm using visible and 1.38-/spl mu/m channels to retrieve cirrus cloud reflectances from aircraft and satellite data

The Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the Terra spacecraft has a channel near 1.38 /spl mu/m for remote sensing of high clouds from space. The implementation of this channel on MODIS was primarily based on previous analysis of hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We describe an algorithm to retrieve cirrus bidirectional reflectance using channels near 0.66 and 1.38 /spl mu/m. It is shown that the apparent reflectance of the 1.38-/spl mu/m channel is essentially the bidirectional reflectance of cirrus clouds attenuated by the absorption of water vapor above cirrus clouds. A practical algorithm based on the scatterplot of 1.38-/spl mu/m channel apparent reflectance versus 0.66-/spl mu/m channel apparent reflectance has been developed to scale the effect of water vapor absorption so that the true cirrus reflectance in the visible spectral region can be obtained. To illustrate the applicability of the present algorithm, results for cirrus reflectance retrievals from AVIRIS and MODIS data are shown. The derived cirrus reflectance in the spectral region of 0.4-1 /spl mu/m can be used to remove cirrus contamination in a satellite image obtained at a visible channel. An example of such an application is shown. The spatially averaged cirrus reflectances derived from MODIS data can be used to establish global cirrus climatology, as is demonstrated by a sample global cirrus reflectance image.     

Spatial validation of the collection 4 MODIS LAI product in eastern Amazonia

This paper reports on the validation of the Collection 4 MODIS leaf area index (LAI) product over the Tapajo/spl acute/s region, eastern Amazonia. The validation site is enclosed in tile h12v09 of the MODIS LAI product. The methodology to assess MODIS LAI accuracy included two main steps: (1) a multiple regression analysis for the generation of LAI surfaces, based on the relationships between field data and remote sensing information from the Enhanced Thematic Mapper Plus sensor, and between field data and topographic information from a digital elevation model; (2) the direct comparison of these LAI surfaces with the MODIS LAI surfaces. The analysis indicated that MODIS LAI is significantly overestimated for the Tapajo/spl acute/s region by a factor of 1.18. No relationships between MODIS LAI and the validation surfaces were found. These results are indicative of a predominance of LAI retrievals by the backup algorithm, which is overcompensating LAI values at the saturation domain. The overgeneralization of the land cover layer (MOD12Q1) can be a source of uncertainties for the lookup table parameterization. Further validation efforts must be carried out over Amazonia for a quantitative quality assessment of the MODIS LAI surfaces in order to improve its accuracy.     

基于流形学习的混合光谱解混分析

光谱解混分析的重要研究内容是计算分析各地物类别成分在混合像素内所占的比例技术。文中以实测高光谱数据为研究对象,针对高光谱数据具有高维度数、严重的光谱混合等特点,基于流形学习中局部线性嵌入（LLE）算法的思想,提出了一种约束最小乘方局部线性加权回归（CLS-LLWR）建模方法。通过4种典型地物的光谱吸收特征差异分析,从它们不同比例组合下的实测混合光谱中选取了不同波段范围,分别对该模型预测覆盖度信息能力进行了验证分析。最后,将CLS-LLWR模型与主成分回归（PCR）和偏最小二乘回归（PLSR）模型,通过计算预测标准误差（SE）进行了对比分析。结果表明,CLS-LLWR模型有较好的预测能力。这为流形学习在高光谱遥感图像信息提取方面进行了有意的探索。     

Microwave land emissivity calculations using AMSU measurements

Atmospheric parameter retrievals over land from Advanced Microwave Sounding Unit (AMSU) measurements, such as atmospheric temperature and moisture profiles, could be possible using a reliable estimate of the land emissivity. The land surface emissivities have been calculated using six months of data, for 30 beam positions (observation zenith angles from -58/spl deg/ to +58/spl deg/) and the 23.8-, 31.4-, 50.3-, 89-, and 150-GHz channels. The emissivity calculation covers a large area including Africa, Eurasia, and Eastern South America. The day-to-day variability of the emissivity is less than 2% in these channels. The angular and spectral dependence of the emissivity is studied. The obtained AMSU emissivities are in good agreement with the previously derived SSMI ones. The scan asymmetry problem has been evidenced for AMSU-A channels. And possible extrapolation of the emissivity from window channels to sounding ones has been successfully tested.     

Cloud cover comparisons of the MODIS daytime cloud mask with surface instruments at the north slope of Alaska ARM site

This paper compares daytime cloud fraction derived from the Moderate Resolution Imaging Spectrometer (MODIS), an imager on the National Aeronautics and Space Administration's Earth Observing System Aqua and Terra platforms, to observations from a suite of surface-based instrumentation located at the Department of Energy's atmospheric radiation measurement (ARM) program North Slope of Alaska (NSA) Clouds and Radiation Testbed site. In this systematic comparison of satellite-to-surface measurements, 3650 cases are analyzed from February through September 2001. The surface instruments used in these comparisons include the Vaisala Ceilometer (VCEIL), the Micropulse Lidar (MPL), the Active Remote Sensing of Clouds (ARSCL) composite laser-derived data product, the Whole-Sky Imager (WSI), and the Normal Incidence Pyrheliometer (NIP). In terms of the active sensors, VCEIL cloud cover results compare to within /spl plusmn/20% of MODIS results 77% of the time. As expected, VCEIL is found to be insensitive to optically thin high-level clouds. MPL results are consistent with MODIS in 83% of the cases; however, the MPL preliminary.cbh variable reports spurious clouds in clear-sky conditions. The ARSCL composite laser-derived data product agrees with MODIS in 81% of the cases, improving upon high cloud detection of the VCEIL, while eliminating the spurious clear-sky cloud detections in the MPL preliminary.cbh variable. For the passive WSI, cloud cover agrees with the MODIS cloud fraction in 74% of the cases, with the difference primarily caused by the insensitivity of the WSI to thin clouds. Detailed analysis of individual cases shows that the MODIS cloud mask generally detects more thin cirrus than the surface-based instruments, but it sometimes fails to detect low-level cumulus and fog over the ARM NSA site.     

A technique for detecting burn scars using MODIS data

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the National Aeronautics and Space Administration Terra and Aqua spacecrafts have several visible and near-infrared (NIR) channels with resolutions of 250, 500, and 1 km for remote sensing of land surfaces and atmosphere. The MODIS data directly broadcasted to ground receiving stations can have many practical applications, including the rapid assessment of fires and burned areas. In this paper, we describe an empirical technique for remote sensing of burn scars using a single dataset of MODIS NIR channels centered near 1.24 and 2.13 /spl mu/m. These channels are sensitive to changes in the surface properties induced by the fire and are not obscured by smoke. Therefore, they allow remote sensing of burn scars in the presence of smoke. Detection of burn scars from single MODIS images, without the need of data from previous days, is very useful for near real-time burn scar recognition in operational direct broadcasting systems. The technique is applied to MODIS data acquired over the western U.S. during the summer fire season, the southeastern part of Canada during the summer and spring seasons, and the southeastern part of Australia. The burnt areas estimated from MODIS data are consistent with those estimated from the high spatial resolution Landsat 7 imaging data.     

Terra MODIS on-orbit spectral characterization and performance

The Moderate Resolution Imaging Spectroradiometer (MODIS) protoflight model onboard the National Aeronautics and Space Administration's Earth Observing System Terra spacecraft has been in operation for over five years since its launch in December 1999. It makes measurements using 36 spectral bands with wavelengths from 0.41 to 14.5 /spl mu/m. Bands 1-19 and 26 with wavelengths below 2.2 /spl mu/m, the reflective solar bands (RSBs), collect daytime reflected solar radiance at three nadir spatial resolutions: 0.25 km (bands 1-2), 0.5 km (bands 3-7), and 1 km (bands 8-19 and 26). Bands 20-25 and 27-36, the thermal emissive bands, collect both daytime and nighttime thermal emissions, at 1-km nadir spatial resolution. The MODIS spectral characterization was performed prelaunch at the system level. One of the MODIS onboard calibrators, the Spectroradiometric Calibration Assembly (SRCA), was designed to perform on-orbit spectral characterization of the MODIS RSB. This paper provides a brief overview of MODIS prelaunch spectral characterization, but focuses primarily on the algorithms and results of using the SRCA for on-orbit spectral characterization. Discussions are provided on the RSB center wavelength measurements and their relative spectral response retrievals, comparisons of on-orbit results with those from prelaunch measurements, and the dependence of center wavelength shifts on instrument temperature. For Terra MODIS, the center wavelength shifts over the past five years are less than 0.5 nm for most RSBs, indicating excellent stability of the instrument's spectral characteristics. Similar spectral performance has also been obtained from the Aqua MODIS (launched in May 2002) SRCA measurements.     

Virtual sensors: using data mining techniques to efficiently estimate remote sensing spectra

Various instruments are used to create images of the earth and other objects in the universe in a diverse set of wavelength bands with the aim of understanding natural phenomena. Sometimes these instruments are built in a phased approach, with additional measurement capabilities added in later phases. In other cases, technology may mature to the point that the instrument offers new measurement capabilities that were not planned in the original design of the instrument. In still other cases, high-resolution spectral measurements may be too costly to perform on a large sample, and therefore, lower resolution spectral instruments are used to take the majority of measurements. Many applied science questions that are relevant to the earth science remote sensing community require analysis of enormous amounts of data that were generated by instruments with disparate measurement capabilities. This work addresses this problem using virtual sensors: a method that uses models trained on spectrally rich (high spectral resolution) data to "fill in" unmeasured spectral channels in spectrally poor (low spectral resolution) data. The models we use Are multilayer perceptrons, support vector machines (SVMs) with radial basis function kernels, and SVMs with mixture density Mercer kernels. We demonstrate this method by using models trained on the high spectral resolution Terra Moderate Resolution Imaging Spectrometer (MODIS) instrument to estimate what the equivalent of the MODIS 1.6-/spl mu/m channel would be for the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR/2) instrument. The scientific motivation for the simulation of the 1.6-/spl mu/m channel is to improve the ability of the AVHRR/2 sensor to detect clouds over snow and ice.