Contrasting snow and ice albedos derived from MODIS, Landsat ETM+ and airborne data from Langjökull, Iceland

• Main Authors: Pope, EL, Willis, IC, Pope, A, Miles, ES, Arnold, NS, Rees, WG
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier BV 2016
• Subjects: Albedo measurement; Landsat; MODIS; Snow; Ice; Glacier; Ice cap; Spatial scales; FLAASH; 6S; Arctic; Langjökull; albedo; glacier; Iceland
• Online Access: https://www.repository.cam.ac.uk/handle/1810/254819

Surface albedo is a key parameter in the energy balance of glaciers and ice sheets because it controls the shortwave radiation budget, which is often the dominant term of a glacier's surface energy balance. Monitoring surface albedo is a key application of remote sensing and achieving consistency between instruments is crucial to accurate assessment of changing albedo. Here we take advantage of a high resolution (5m) airborne multispectral dataset that was collected over Langjökull, Iceland in 2007, and compare it with near contemporaneous ETM+ and MODIS imagery. All three radiance datasets are converted to reflectance by applying commonly used atmospheric correction schemes: 6S and FLAASH. These are used to derive broadband albedos. We first assess the similarity of albedo values produced by different atmospheric correction schemes for the same instrument, then contrast results from different instruments. In this way we are able to evaluate the consistency of the available atmospheric correction algorithms and to consider the impacts of different spatial resolutions. We observe that FLAASH leads to the derivation of surface albedos greater than when 6S is used. Albedo is shown to be highly variable at small spatial scales. This leads to consistent differences associated with specific facies types between different resolution instruments, in part attributable to different surface bi-directional reflectance distribution functions. Uncertainties, however, still exist in this analysis as no correction for variable bi-directional reflectance distribution functions could be implemented for the ETM+ and airborne datasets. 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, ...