Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Negi, H. S.; Kokhanovsky, Alexander A.

doi:10.5194/tc-5-831-2011

Cited by 38 publications

(24 citation statements)

References 67 publications

(81 reference statements)

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“…In our case, δ is limited to 0.7 (i.e., a maximum tilt angle for the facet of 63 • ) because for higher values the probability of unphysical scattering events strongly increases, resulting in progressively larger loss of accuracy of the GO calculations of a given number of rays. Neshyba et al (2013) have shown that various other definitions and parameterizations of crystal surface roughness lead to similar results. Yang et al (2008) found that such approaches are efficient yet accurate treatments of microscale roughness.…”

Section: Methodssupporting

confidence: 58%

“…Using instead the 1594 nm channel, diameters typically 30 µm smaller are obtained for each scene, which can be explained by the different penetration depths of the two channels, indicating crystal size increasing with depth as expected Li et al, 2001;Aoki et al, 2000;Warren, 1982). These values, at the lower end of common retrieval ranges, are normally associated with fresh snow conditions as found in studies based on other remote sensors in alpine regions (Negi and Kokhanovsky, 2011;Painter et al, 2009;Dozier and Painter, 2004;Nolin and Dozier, 2000) and polar plateaus (Lyapustin et al, 2009;Hori et al, 2007). However, a direct comparison is challenged by differences in the definition of grain size, assumptions on grain sphericity and different penetration depths achieved by the use of different instrument channels.…”

Section: Resultsmentioning

confidence: 88%

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Photopolarimetric retrievals of snow properties

2015

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Abstract. Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

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Section: Methodssupporting

confidence: 58%

Section: Resultsmentioning

confidence: 88%

Photopolarimetric retrievals of snow properties

2015

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“…(1), the SSA is inversely proportional to the optical radius. Studies reporting on the retrieval of snow properties from satellite data often refer to the optical radius (Fily et al, 1999;Painter et al, 2009;Negi and Kokhanovsky, 2011b), while some others tend to refer to SSA . It is worth noting that the use of SSA is more appropriate when studying small grains with high albedo.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly to Nolin and Dozier (1993), for each pixel to be processed, the algorithm searches the LUT and selects the SSA whose computed reflectance spectrum resembles most that of the image according to the spectral distance D defined as…”

Section: Retrieval Methods Based On Look-up Tables Built Using Disortmentioning

confidence: 99%

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Mary¹,

Dumont

Dedieu

et al. 2013

The Cryosphere

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Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008-2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m 2 kg −1 and the mean difference was 0.1 m 2 kg −1 , based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m 2 kg −1 ). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

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“…In vast snow areas like the Himalyas, however, adequate field survey is almost impossible and in-situ measurements provide limited regional information (Negi and Kokhanovsky, 2011). Presently, with the use of remote sensing instruments, snow cover information and many other parameters can be determined on real-time, year-round over vast, rugged and remote areas.…”

Section: Snow Depth Distribution Estimation Methods For Mountain Regiomentioning

confidence: 97%

River management system development in Asia based on Data Integration and Analysis System (DIAS) under GEOSS

Koike

Koudelova

Jaranilla-Sanchez

et al. 2014

Sci. China Earth Sci.

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This paper introduces the process of development and practical use implementation of an advanced river management system for supporting integrated water resources management practices in Asian river basins under the framework of GEOSS Asia water cycle initiative (AWCI). The system is based on integration of data from earth observation satellites and in-situ networks with other types of data, including numerical weather prediction model outputs, climate model outputs, geographical information, and socio-economic data. The system builds on the water and energy budget distributed hydrological model (WEB-DHM) that was adapted for specific conditions of studied basins, in particular snow and glacier phenomena and equipped with other functions such as dam operation optimization scheme and a set of tools for climate change impact assessment to be able to generate relevant information for policy and decision makers. In situ data were archived for 18 selected basins at the Data Integration and Analysis System (DIAS) of Japan and demonstration projects were carried out showing potential of the new system. It included climate change impact assessment on hydrological regimes, which is presently a critical step for sound management decisions. Results of such three case studies in Pakistan, Philippines, and Vietnam are provided here. integrated water resources management tools, climate change impact assessment, Asian river basins, Asian Water Cycle Initiative Citation: Koike T, Koudelova P, Jaranilla-Sanchez P A, et al. 2015. River management system development in Asia based on Data Integration and Analysis System (DIAS) under GEOSS. Science China: Earth Sciences, 58: 76 -95,The global Earth observation system of systems (GEOSS) Asian water cycle initiative (AWCI) was established in 2007 as a response to the recognized needs for accurate, timely, and long-term water cycle information to implement integrated water resources management (IWRM) practices and with regards to the commonality in the water-related issues and socio-economic needs in the Asia-Pacific region. Implementing IWRM at the river basin level, while respecting the physical, social and political context, is an essential element to managing water resources in a more sustainable way, leading to long-term social, economic and environmental benefits (GWP, 2009). It requires a wide range of disparate data from multiple disciplines and various sources and appropriate tools for processing these data and integrating and translating them into relevant information for water resources practitioners and policy decision makers. A system for supporting IWRM practices thus must be able to simulate and predict a wide range of flows from droughts to floods and to be applicable for long-term, cli-

show abstract

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Cited by 38 publications

References 67 publications

Photopolarimetric retrievals of snow properties

Photopolarimetric retrievals of snow properties

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

River management system development in Asia based on Data Integration and Analysis System (DIAS) under GEOSS

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