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

Negi, H. S.; Singh, Sushil Kumar; Kulkarni, Anil V.; Semwal, B. S.

doi:10.1080/01431160903002417

Cited by 58 publications

(66 citation statements)

References 27 publications

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“…With knowledge of ARFs for the sample surface and reflectance panel, these measurements can be used in conjunction with an angular integration model to approximate albedo (Grenfell, 2011;Casey et al, 2016). This measurement also requires correction for instrument shading (Nicolaus et al, 2010;Grenfell, 2011). Finally, for reflectance-factor measurements the white reference panel must be clean and flat (Schaepman-Strub et al, 2006;McCoy et al, 2005;Milton et al, 2009) and a white reference measurement should be taken before and after each new measurement so that changes in solar angle, cloud cover or atmospheric effects can be accounted for.…”

Section: Standardising Measurement and Instrument Configurationsmentioning

confidence: 99%

“…Other options include microcomputed tomography (Micro-CT: Dadic et al, 2013), although this requires cores to remain completely frozen through extraction, transport and analysis. Grain sizes could be determined using a polarising hand lens (Hubbard and Glasser, 2005;Negi et al, 2010) -although the error can be high (Painter et al, 2007) -or semi-automated analysis of photographs of thin/thick sections (Dadic et al, 2013) or the sample surface. Non-spherical grain shapes can influence the surface albedo (Dang et al, 2016) and can be simulated in the radiative transfer scheme TARTES .…”

Section: Characterising Snow or Ice Optical Characteristicsmentioning

confidence: 99%

“…To avoid error resulting from variable cloud conditions, downwards irradiance measurements can be made before and after upwards irradiance measurements and interpolated to the precise measurement time to calculate the albedo (S. Warren, personal communication, 2017). Depending on the frame used to support the sensor, a shadowing correction should be applied (Nicolaus et al, 2010; for reference, Figure 6. Field measured albedo (cosine collector) and nadir-view HCRF (8 • collimating lens) for one clean ice and one algal ice surface.…”

Section: Standardising Measurement and Instrument Configurationsmentioning

confidence: 99%

“…This was discussed by Yallop et al (2012) who linked algal pigmentation to ice albedo in south-western Greenland but did not explicitly identify the pigments responsible. Ice algae produce a range of pigments to facilitate both light harvesting across the visible spectrum and energy dissipation under conditions of excessive irradiance (Remias et al, 2009) via, for example, the interconversion of xanthophyll pigments to quench excess excitation energy as heat (Niyogi et al, 1997;Goss and Jakob, 2010). In addition, ice algae inhabiting surface habitats produce specialist UV-absorbing pigments (purpurogallins) that are distinct from the typical UV-absorbing pigments (mycosporine-like amino acids) identified in other algal lineages (Remias et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

et al. 2017

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Abstract. The darkening effects of biological impurities on ice and snow have been recognised as a control on the surface energy balance of terrestrial snow, sea ice, glaciers and ice sheets. With a heightened interest in understanding the impacts of a changing climate on snow and ice processes, quantifying the impact of biological impurities on ice and snow albedo ("bioalbedo") and its evolution through time is a rapidly growing field of research. However, rigorous quantification of bioalbedo has remained elusive because of difficulties in isolating the biological contribution to ice albedo from that of inorganic impurities and the variable optical properties of the ice itself. For this reason, isolation of the biological signature in reflectance data obtained from aerial/orbital platforms has not been achieved, even when ground-based biological measurements have been available. This paper provides the cell-specific optical properties that are required to model the spectral signatures and broadband darkening of ice. Applying radiative transfer theory, these properties provide the physical basis needed to link biological and glaciological ground measurements with remotely sensed reflectance data. Using these new capabilities we confirm that biological impurities can influence ice albedo, then we identify 10 challenges to the measurement of bioalbedo in the field with the aim of improving future experimental designs to better quantify bioalbedo feedbacks. These challenges are (1) ambiguity in terminology, (2) characterising snow or ice optical properties, (3) characterising solar irradiance, (4) determining optical properties of cells, (5) measuring biomass, (6) characterising vertical distribution of cells, (7) characterising abiotic impurities, (8) surface anisotropy, (9) measuring indirect albedo feedbacks, and (10) measurement and instrument configurations. This paper aims to provide a broad audience of glaciologists and biologists with an overview of radiative transfer and albedo that could support future experimental design.

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Section: Standardising Measurement and Instrument Configurationsmentioning

confidence: 99%

Section: Characterising Snow or Ice Optical Characteristicsmentioning

confidence: 99%

Section: Standardising Measurement and Instrument Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

et al. 2017

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“…Such unequal variation in reflectance between Bands 4 and 6 are interpreted as representing the spectral reflectance of snow being higher at green bands than at red bands, which has been corroborated by field-based experiments showing that different wavelengths produced different reflectances at different SZA values [29]. The Algorithm Theoretical Basis Document for the MODIS Snow and Sea Ice-Mapping of a non-Lambertian reflector states that snow's anisotropy might result in some errors when mapped at large view angles [30].…”

Section: Analysis Of the Mechanism Responsible For The Sza Effectmentioning

confidence: 73%

Impact of Sensor Zenith Angle on MOD10A1 Data Reliability and Modification of Snow Cover Data for the Tarim River Basin

Xiao

2016

Remote Sensing

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Snow in the mountainous watersheds of the Tarim River Basin is the primary source of water for western China. The Snow Cover Daily L3 Global 500-m Grid (MOD10A1) remote sensing dataset has proven extremely valuable for monitoring the changing snow cover patterns over large spatial areas; however, inherent uncertainty associated with large sensor zenith angles (SZAs) has called its reliability into question. Comparative analysis that utilized a paired-date difference method for parameters such as snow cover frequency, snow cover percentage, and normalized difference snow index (NDSI) has shown that overestimation of snow cover in the Tarim River Basin correlates with high values of SZA. Hence, such overestimation was associated with an increase in the NDSI, attributable to the change in reflectance between Band 4 and Band 6 imagery. A maximum threshold value of SZA of 22.37 • was used alongside a multiday refilling method to modify the MOD10A1 dataset to produce a new daily snow cover map of the Tarim River Basin, spanning a 10-year period. A comparison of benchmark results of snow cover classification produced by the HJ-1A/B satellite revealed an increase in the overall accuracy of up to 4%, confirming the usefulness of our modified MOD10A1 data.

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Remote Sensing for Snowpack Monitoring and Its Implications

Varade

Manickam

Singh

2021

Geographic Information Science for Land Resource Management

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

Cited by 58 publications

References 27 publications

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo

Impact of Sensor Zenith Angle on MOD10A1 Data Reliability and Modification of Snow Cover Data for the Tarim River Basin

Remote Sensing for Snowpack Monitoring and Its Implications

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