Contrasting snow and ice albedos derived from MODIS, Landsat ETM+ and airborne data from Langjökull, Iceland
Introduction
A key concern associated with rising high northern latitude temperatures is the melting of terrestrial ice bodies leading to the rise of global sea levels (Dowdeswell et al., 1997, Hagen et al., 2003, Meier et al., 2007, Radić and Hock, 2011, Wolken et al., 2009). Arctic and sub-Arctic ice masses are particularly sensitive to climate change as temperatures there are rising at about twice the global average (Graversen, Mauritsen, Tjernström, Källén, & Svensson, 2008). Predicting the response of terrestrial ice bodies to high northern latitude climate change requires accurate calculation of surface melt rates and thus precise assessment of the ice surface energy-balance (Aas et al., 2015). In many systems, energy balance studies have shown that net shortwave radiation is often the dominant contributor of available energy for melting glacier snow and ice (Arendt, 1999). The amount of energy available to glacier surfaces from shortwave fluxes is controlled by the surface albedo, i.e. its reflectivity. Accurate measurement and parameterisation of surface albedo is therefore a key component in calibrating/validating energy balance models designed to estimate past, current and future glacier melt. This is particularly important in Arctic and sub-Arctic regions because a key reason why high northern latitude temperatures are rising so rapidly is due to albedo feedbacks (Serreze, Holland, & Stroeve, 2007).
Glacier surface reflectance can be measured using either in situ ground based methods or remote sensing techniques (Cutler and Munro, 1996, Reijmer et al., 1999). Satellite and airborne remote sensing allows both large spatial coverage and regular temporal sampling, and limits the cost and risk associated with repeat field measurements (Aniya et al., 1996, Box et al., 2012, Boyd, 2009, Paul et al., 2004). The various instruments on board different remote sensing platforms use a range of spectral bands which require geometric and atmospheric correction to convert radiance to reflectance, and Narrow-To-Broadband (NTB) transformation to produce average surface reflectance. The suitability of individual sensors for energy balance studies depends, therefore, on the spectral and spatial resolution of individual sensors, the reliability of geometric/atmospheric correction techniques, and the precision of the NTB transformation (Chander et al., 2009, Greuell and Oerlemans, 2004, Rees, 2006, Vermote et al., 2002).
The albedo of glacier snow and ice is highly spatially and temporally variable. It depends on a range of factors including solar incidence angle, cloud cover, surface topography, snow grain size and geometry, impurities in the snow and ice, and water content (Arnold et al., 2006, Dumont et al., 2012, Warren and Wiscombe, 1980, Wiscombe and Warren, 1980). Albedo varies over spatial scales on the order of metres (Arnold & Rees, 2003) and it evolves temporally as snow metamorphoses and melts or as new snow falls (Brock, Willis, Sharp, & Arnold, 2000). Surface albedo and its association with different snow and ice facies has been monitored throughout the year in a range of climatic settings (Box et al., 2012, Klok and Oerlemans, 2002, Tedesco et al., 2011). Fresh snow can have an albedo of over 0.9 whereas ice can range between ~ 0.4 and ~ 0.1 depending on the debris content (Cuffey & Paterson, 2010).
Many studies have attempted to compare satellite derived albedos with ground measurements and to improve the algorithms used to derive surface albedo from satellite data (Greuell et al., 2002, Hall et al., 1989, Hendriks and Pellikka, 2004, Knap and Reijmer, 1998, Liang et al., 2005, Reijmer et al., 1999, Stroeve et al., 2005). Such studies use mainly ground point measurements and are therefore limited by their lack of spatial and temporal resolution. Regarding spatial resolution, point measurements have sometimes been used to validate measurements for pixels with areas up to 1 km2. Despite their frequent use, point measurement validations rely on relatively homogeneous surface reflectance characteristics in the surrounding areas. This is frequently not the case, especially for valley glaciers and the outlet glaciers from ice caps and ice sheets.
Greuell et al. (2002) sought to improve on ground point measurements using helicopter-based albedo readings. However, the spatial coverage of this study was still rather limited, equating to a few thousand data points. By contrast, in the present study, we use 1.32 × 107 measurements derived from a high resolution Airborne Thematic Mapper (ATM) dataset to validate both Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and MODerate Resolution Imaging Spectroradiometer (MODIS) albedo data. The primary aims of our study are to assess the spatial pattern of surface albedo across an ice mass and to evaluate the consistency of different scientific products derived from various remote sensing instruments. The study is applied to Langjökull, a typical Icelandic ice mass, which exhibits a large range of albedos both spatially across the ice cap and temporally through the year.
Section snippets
Study site
Langjökull (64.7°N, 20.4°W) is Iceland's second largest ice cap, with an area of ~ 925 km2 (Fig. 1). The ice cap elevation ranges from 450 to 1450 m above sea level with an average height of 900 m (Pope, Willis, Rees, Arnold, & Palsson, 2013). Langjökull is surrounded by basalt lava fields, sandur plains and proglacial lakes and while two major rivers drain some meltwater from the ice cap, a significant proportion drains directly into the substrate to feed groundwater aquifers (Guðmundsson,
Results
Results are divided into two parts. First, for both the ATM and ETM+ datasets, the effects of the different processing techniques (i.e. FLAASH and 6S) are analysed. Second, differences between the albedo estimates generated by the different instruments (ATM, ETM+ and MODIS) are compared. The spatial extent of all the datasets is the same, as each dataset has been masked to the full extent of available albedo values across all datasets.
Discussion
The discussion is divided into three parts. First, we outline the possible reasons for the differences between the ATM6S, ETM6S and MCD43 datasets. Second, we evaluate the MCD43 product against the ATM6S and ETM6S datasets. Last, we assess the implications of the differences between the datasets for energy balance modelling and melt estimates.
Conclusions
This study has explored the ability of different resolution instruments and different retrieval methods to measure the surface albedo across Langjökull. Different retrieval methods for the same instrument have been shown to produce inconsistent surface albedo measurements. These differences are the result of contrasts between different atmospheric correction models which were applied. Correction of both ATM and ETM+ datasets using FLAASH produced mean albedos greater than those generated by 6S.
Acknowledgements
This work was supported by the UK NERC ARSF — Project IPY07-08. E. Pope was supported by the NERC Arctic Research Programme under project NE/K00008Xs/1. A. Pope was supported by the National Science Foundation Graduate Research Fellowship Programme under Grant No. DGE-1038596 and by Trinity College, Cambridge. E. Miles was supported by a Gates Cambridge Scholarship and by Trinity College, Cambridge. Fieldwork associated with the ATM flights was supported by grants from the University of
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