**1. Introduction**

In many regions, strategic land use/land managemen<sup>t</sup> projects that enhance terrestrial carbon sinks or reduce terrestrial carbon emissions are viewed favorably and analogouslyto mitigating climate change. However, it is increasingly understood that it is important and necessary to include other climate regulating services on land in climate impact assessment studies [1,2]. This includes the surface albedo, which is a biogeophysical property that partly determines Earth's shortwave radiation balance [3]. To exclude the surface albedo in the assessments of land-based mitigation can result in the implementation of policies that are suboptimal or even counterproductive [4,5]. Indeed, recent research has consistently demonstrated the need to value the surface albedo alongside carbon in order to maximize mitigation benefits, particularly for forestry projects [6–10].

However, the credibility of such valuations largely rests on the underlying accuracy and spatial-temporal representativeness of the surface albedo data employed in the research. Although 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 [11–13], 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 particularly true for forests [15], in which the surface albedo is determined as much by vegetation structure [16–18] 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 managemen<sup>t</sup> practices [21–24].

Compared to global or regional land cover products, national mapping authorities often provide classifications of land cover and structure at a higher spatial resolution and accuracy. Such classifications often combine multiple information sources, including those obtained from optical satellite remote sensing, aerial LiDAR and photogrammetric remote sensing and local expert judgments. For instance, Wickham et al. [25] recently developed a land cover-dependent albedo dataset for the continental United States by combining the National Land Cover Database together with a MODIS climatology of surface albedo. Given that the mitigation policies of the land-based sectors are implemented and monitored nationally, the use of national land resource maps and national land cover classifications can serve to further improve the accuracy of land-cover dependent albedo estimates based on satellite remote sensing. Furthermore, the use of a national land classification makes pragmatic sense both from a managemen<sup>t</sup> and reporting perspective.

In the present study, we employ observation-based datasets of Norwegian land cover and structure, near surface air temperature, and MODIS-based snow cover (MOD10A1 v6) to spectrally unmix MODIS surface albedo (MCD43A3 v6) and to study spatial-temporal variations in surface albedo as a function of land cover, forest structure, and the environmental state. Our primary objective is to develop and present a set of simple land cover-dependent empirical models for Norway that facilitate high fidelity predictions of the surface albedo at a monthly resolution. This resolution is deemed appropriate as major intra-annual surface albedo dynamics play out over seasonal timescales. Furthermore, the monthly resolution makes the models amenable to inputs obtained from gridded historical climate observation products or from climate model scenario runs whose outputs are often provided at the monthly resolution. Unlike existing global [12] and national [25] land cover-dependent albedo datasets based on MODIS surface albedo products, our method does not require constraining the analysis to pixels that are homogeneous with respect to single land cover types, thus enabling a more efficient use of MODIS data. Given the relatively low nominal spatial resolution of the MODIS albedo product (i.e., 500 × 500 m), this is particularly important for regions, such as Norway, where the land cover and structure are relatively heterogeneous at small spatial scales. Furthermore, because spatial-temporal variations in the surface albedo not only depend on variations in land cover and structure but on local environmental conditions affecting the state of vegetation, soils, and snow, we include snow cover and near surface air temperature in our analysis since these factors are known to greatly affect the surface albedo either directlyor indirectly [26–29].

Given their conformity to national land cover products and classifications, such models will be useful in the studies seeking to quantify albedo-related impacts connected to national land use activities, or for constraining land cover-dependent albedo parameterizations in models employed in regional climate and hydrological research making use of the national land cover mapping and classification. In addition, such tools can be applied to create a seamless monthly surface albedo dataset that is land-cover dependent, thus providing a means to benchmark climate model predictions of surface albedo made at the scale of individual land cover or plant functional types—a task that is challenging at present.

We start by detailing our method and datasets in Section 2, which is followed by a presentation of results in Section 3 and a discussion of their merits and uncertainties in Sections 4 and 5.

#### **2. Materials and Methods**

The general workflow is divided into two parts: (i) model training and (ii) model validation. Both are limited to the southern portion of mainland Norway (Figure 1) in order to include a larger wintertime sample of good quality MODIS snow cover and surface albedo retrievals (described in Sections 2.5 and 2.6) since these have a low frequency at higher latitudes during winter. Furthermore, the study region contains the full range of land cover and climate variation found in Norway (Figure S6 of Supporting Information).
