From Remotely‐Sensed Data of Norwegian Boreal Forests to Fast and Flexible Models for Estimating Surface Albedo

摘要

The importance to consider changes in surface albedo and go beyond simple carbon accounting when assessing climate change impacts of forestry and land use activities is increasingly recognized. However, representation of albedo changes in climate models is complex and highly parameterized, thereby limiting their applications in climate impact studies. The availability of simple yet reliable albedo models can enhance consideration of albedo changes in land use studies. We propose a set of simplified models for estimating surface albedo in a boreal forest. We process and harmonize datasets of remotely‐sensed albedo estimates, forest structure parameters, and meteorological records for different forest locations in Norway. By combining linear unmixing with nonlinear programming, we simultaneously produce albedo estimates at the same resolution of the land cover dataset (16?m, notably higher than satellite retrievals) and a variety of flexible models for albedo predictions. We test different combinations of functional forms, variables, and constraints, including variants specific for snow‐free conditions. We find that models capture the seasonal pattern of surface albedo and the interactive effect of forest structures and meteorological parameters, and many of them show good statistical scores. The cross‐validation exercise shows that the models derived from one area perform reasonably well when applied to other forested areas in Norway, regardless of the temporal and spatial scales. By incorporating changes in forest structure and climate conditions as explicit variables, these models are simple to be used in different applications aiming at estimating albedo changes from forest management and climate change. Plain Language Summary Surface albedo is the fraction of solar radiation reflected back into the atmosphere by a surface, and it determines how much energy is re‐distributed in the biosphere. It is one of the most important physical properties of land cover and a key mechanism for climate control. By combining satellite retrievals of Norwegian boreal forest with high resolution land cover data and meteorological records, our study produces a range of relatively simple models to estimate surface albedo using the forest structure parameters and climate data. The models require simple variables of forest structure information (age and/or volume) and temperature and/or snow water equivalents. These models are relatively easy and fast to be used for quantifying effects of forest management and climate change on surface albedo.