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.

中文翻译：

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从挪威北方森林的遥感数据到估计地表反照率的快速灵活模型

人们越来越认识到，在评估林业和土地利用活动对气候变化的影响时，考虑地表反照率的变化并超越简单的碳核算的重要性。但是，气候模型中反照率变化的表示非常复杂且参数化很高，因此限制了其在气候影响研究中的应用。简单而可靠的反照率模型的可用性可以增强土地利用研究中反照率变化的考虑。我们提出了一套简化的模型，用于估算寒带森林中的地表反照率。我们处理并统一了挪威不同森林位置的遥感反照率估算，森林结构参数和气象记录的数据集。通过将线性分解与非线性规划相结合，我们同时以相同的土地覆盖数据集分辨率（16 m，显着高于卫星反演）生成反照率估算值，并使用多种灵活的反照率预测模型。我们测试功能形式，变量和约束的不同组合，包括特定于无雪条件的变体。我们发现模型捕获了地表反照率的季节性模式以及森林结构和气象参数的相互作用，其中许多模型显示出良好的统计得分。交叉验证的结果表明，从一个地区获得的模型在应用于挪威其他森林地区时，无论时间和空间尺度如何，其表现都相当不错。通过将森林结构和气候条件的变化纳入显式变量，