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Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations
J. M. Krijger, P. Tol, L. G. Istomina, C. Schlundt, H. Schrijver,I. Aben
Atmospheric Measurement Techniques (AMT) & Discussions (AMTD) , 2011,
Abstract: In the ultra-violet, visible and near infra-red wavelength range the presence of clouds can strongly affect the satellite-based passive remote sensing observation of constituents in the troposphere, because clouds effectively shield the lower part of the atmosphere. Therefore, cloud detection algorithms are of crucial importance in satellite remote sensing. However, the detection of clouds over snow/ice surfaces is particularly difficult in the visible wavelengths as both clouds an snow/ice are both white and highly reflective. The SCIAMACHY Polarisation Measurement Devices (PMD) Identification of Clouds and Ice/snow method (SPICI) uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 μm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the on-board SCIAMACHY PMDs are used because they provide higher spatial resolution compared to the main spectrometer measurements. In this paper we expand on the original SPICI algorithm (Krijger et al., 2005a) to also adequately detect clouds over snow-covered forests which is inherently difficult because of the similar spectral characteristics. Furthermore the SCIAMACHY measurements suffer from degradation with time. This must be corrected for adequate performance of SPICI over the full SCIAMACHY time range. Such a correction is described here. Finally the performance of the new SPICI algorithm is compared with various other datasets, such as from FRESCO, MICROS and AATSR, focusing on the algorithm improvements.
Geochemical characteristics and zones of surface snow on east Antarctic Ice Sheet
Jiancheng Kang,Leibao Liu,Dahe Qin,Dali Wang,Jiahong Wen,Dejun Tan,Zhongqin Li,Jun Li,Xiaowei Zhang
Chinese Science Bulletin , 2004, DOI: 10.1007/BF03185789
Abstract: The surface-snow geochemical characteristics are discussed on the East Antarctic Ice Sheet, depending on the stable isotopes ratios of oxygen and hydrogen, concentration of impurities (soluble-ions and insoluble micro-particle) in surface snow collected on the ice sheet. The purpose is to study geochemical zones on the East Antarctic Ice Sheet and to research sources and transportation route of the water vapor and the impurities in surface snow. It has been found that the ratio coefficients, as S1, d1 in the equation δD =S 1δ18O +d 1, are changed near the elevation 2000 m on the ice sheet. The weight ratio of C1 /Na+ at the area below the elevation of 2000 m is close to the ratio in the sea salt; but it is about 2 times that of the sea salt, at the inland area up to the elevation of 2000 m. The concentrations of non-sea-salt Ca2+ ion (nssCa2+) and fine-particle increase at the interior up to the elevation 2000 m. At the region below the elevation of 2000 m, the impurity concentration is decreasing with the elevation increasing. Near coastal region, the surface snow has a high concentration of impurity, where the elevation is below 800 m. Combining the translating processes of water-vapor and impurities, it suggests that the region up to the elevation 2000 m is affected by large-scale circulation with longitude-direction, and that water-vapor and impurities in surface snow come from long sources. The region below the elevation 2000 m is affected by some strong cyclones acting at peripheral region of the ice sheet, and the sources of water and impurities could be at high latitude sea and coast. The area below elevation 800 m is affected by local coastal cyclones.
Improved cloud and snow screening in MAIAC aerosol retrievals using spectral and spatial analysis
A. Lyapustin, Y. Wang, I. Laszlo,S. Korkin
Atmospheric Measurement Techniques (AMT) & Discussions (AMTD) , 2012,
Abstract: An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques.
The detection of cloud free snow covered areas using AATSR measurements  [PDF]
L. G. Istomina,W. von Hoyningen-Huene,A. A. Kokhanovsky,J. P. Burrows
Atmospheric Measurement Techniques Discussions , 2010, DOI: 10.5194/amtd-3-1099-2010
Abstract: A new method to detect cloud free snow covered areas is developed using the measurements by the Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite in order to discriminate clear snow fields for the retrieval of aerosol optical thickness or snow properties. The algorithm uses seven AATSR channels from VIS to TIR and analyzes the spectral behavior of each pixel in order to recognize the spectral signature of snow. The algorithm includes a set of relative thresholds and combines all seven channels into one flexible criterion, which allows us to filter out all the pixels with spectral behavior similar to that of snow. The algorithm does not use any kind of morphological criteria and does not require the studied surface to have any special structure. The snow spectral shape criterion was determined by a comprehensive theoretical study, which included radiative transfer simulations for various atmospheric conditions as well as studying existing models and measurements of snow optical and physical properties in different spectral bands. The method has been optimized to detect cloud free snow covered areas, and does not produce cloud/land/ocean/snow mask. However, the algorithm can be extended and be able to discriminate various kinds of surfaces. The presented method has been validated against Micro Pulse Lidar data and compared to MODIS cloud mask over snow covered areas, showing quite good correspondence to each other.
Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations
J. M. Krijger,P. Tol,L. G. Istomina,C. Schlundt
Atmospheric Measurement Techniques Discussions , 2011, DOI: 10.5194/amtd-4-1113-2011
Abstract: An improved version is presented of the SCIAMACHY PMD Identification of Clouds and Ice/snow method (SPICI). SPICI uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 μm to make a distinction between clouds and ice/snow covered surfaces, specifically developed to identify cloud-free SCIAMACHY observations. For this purpose the SCIAMACHY Polarisation Measurement Devices (PMDs) are used because they provide higher spatial resolution compared to the main spectrometer measurements. The improvements (compared to Krijger et al., 2005) include a snow over vegetation detection and correction for SCIAMACHY PMD degradation.
Effective UV surface albedo of seasonally snow-covered lands
A. Tanskanen ,T. Manninen
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2007,
Abstract: At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.
Effective UV surface albedo of seasonally snow-covered lands  [PDF]
A. Tanskanen,T. Manninen
Atmospheric Chemistry and Physics Discussions , 2007,
Abstract: At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.
Effective UV surface albedo of seasonally snow-covered lands  [PDF]
A. Tanskanen,T. Manninen
Atmospheric Chemistry and Physics (ACP) & Discussions (ACPD) , 2007,
Abstract: At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.
Inferring Snow Water Equivalent for a Snow-Covered Ground Reflector Using GPS Multipath Signals  [PDF]
Mark D. Jacobson
Remote Sensing , 2010, DOI: 10.3390/rs2102426
Abstract: A nonlinear least squares fitting algorithm is used to estimate both snow depth and snow density for a snow-layer above a flat ground reflector. The product of these two quantities, snow depth and density, provides an estimate of the snow water equivalent. The input to this algorithm is a simple ray model that includes a speculary reflected signal along with a direct signal. These signals are transmitted from the global positioning system satellites at 1.57542 GHz with right-hand circularly polarization. The elevation angles of interest at the GPS receiving antenna are between 5° and 30°. The results from this nonlinear algorithm show potential for inferring snow water equivalent using GPS multipath signals.
Modelling the spatial distribution of snow water equivalent at the catchment scale taking into account changes in snow covered area
T. Skaugen,F. Randen
Hydrology and Earth System Sciences Discussions , 2011, DOI: 10.5194/hessd-8-11485-2011
Abstract: A successful modelling of the snow reservoir is necessary for water resources assessments and the mitigation of spring flood hazards. A good estimate of the spatial probability density function (PDF) of snow water equivalent (SWE) is important for obtaining estimates of the snow reservoir, but also for modelling the changes in snow covered area (SCA), which is crucial for the runoff dynamics in spring. In a previous paper the PDF of SWE was modelled as a sum of temporally correlated gamma distributed variables. This methodology was constrained to estimate the PDF of SWE for snow covered areas only. In order to model the PDF of SWE for a catchment, we need to take into account the change in snow coverage and provide the spatial moments of SWE for both snow covered areas and for the catchment as a whole. The spatial PDF of accumulated SWE is, also in this study, modelled as a sum of correlated gamma distributed variables. After accumulation and melting events the changes in the spatial moments are weighted by changes in SCA. The spatial variance of accumulated SWE is, after both accumulation- and melting events, evaluated by use of the covariance matrix. For accumulation events there are only positive elements in the covariance matrix, whereas for melting events, there are both positive and negative elements. The negative elements dictate that the correlation between melt and SWE is negative. The negative contributions become dominant only after some time into the melting season so at the onset of the melting season, the spatial variance thus continues to increase, for later to decrease. This behaviour is consistent with observations and called the "hysteretic" effect by some authors. The parameters for the snow distribution model can be estimated from observed historical precipitation data which reduces by one the number of parameters to be calibrated in a hydrological model. Results from the model are in good agreement with observed spatial moments of SWE and SCA and found to provide better estimates of the spatial variability than the current model for snow distribution used in the HBV model, the hydrological model used for flood forecasting in Norway. When implemented in the HBV model, simulations show that the precision in predicting runoff is maintained although there is one parameter less to calibrate.
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