Abstract
Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC).
Highlights
In many regions, strategic land use/land management projects that enhance terrestrial carbon sinks or reduce terrestrial carbon emissions are viewed favorably and analogouslyto mitigating climate change
We presented a coherent empirical modeling framework based on linear spectral unmixing of optical satellite retrievals of surface albedo
The models can facilitate comparisons to land-cover dependent albedo predictions made by land surface models, which are difficult to evaluate by direct comparisons to satellite retrievals
Summary
Strategic land use/land management projects that enhance terrestrial carbon sinks or reduce terrestrial carbon emissions are viewed favorably and analogouslyto mitigating climate change. Satellite remote sensing analyses of surface albedo have been incredibly useful for constraining the surface albedo by land cover type at a regional or global scale [11,12,13], the land cover classifications underlying such constraints are still insufficiently broad for subregional applications, as evidenced by the large albedo variations observed across both time and space within individual land cover types [11,12,14] This is true for forests [15], in which the surface albedo is determined as much by vegetation structure [16,17,18] and functioning [19,20] as it is by local environmental factors such as snow. Large spatial variations in the surface albedo exist for other land cover types, such as croplands and grasslands, which are heavily influenced by local land management practices [21,22,23,24]
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