Combining MODIS and National Land Resource Products to Model Land Cover-Dependent Surface Albedo for Norway

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).

Evaluating the terrestrial carbon dioxide removal potential of improved forest management and accelerated forest conversion in Norway

“…We are also confident in our albedo results, since the albedo predictions are based on empirical models calibrated within Norway for the same forested ecosystems, which have normalized prediction biases of <10% (see e.g., figure S9 of Bright and Astrup, 2019).…”

“…We employ the empirical models of Bright and Astrup (2019)—developed in Norway—which are based on satellite optical remote sensing and which predict the monthly surface albedo as a function of land cover type, forest structure (if forest), monthly near surface air temperature, and monthly snow cover. These models provide estimates of the monthly mean “black‐sky” and “white‐sky” surface albedos (entire shortwave broadband) at local solar noon.…”

“…The albedo models of Bright and Astrup (2019) require inputs of snow cover percentage based on the normalized difference snow index (NDSI) as provided by the version 6 MOD10 Snow Cover product (Hall & Riggs, 2016). Since these inputs are not available for our forward‐looking simulations, we develop an empirical function through ordinary least squares regressions that give NDSI snow cover percentage as a function of land cover type and monthly mean SWE (see Section S3 for details).…”

“…For forests, the models of Bright and Astrup (2019) require inputs of either stand volume density (m 3 /ha) or aboveground biomass (t/ha) as a structural predictor; here, the parameters for the forest models based on volume density as a structural predictor are applied. In each scenario, models for all three tree species groups (i.e., spruce‐dominant, pine‐dominant, deciduous broadleaf‐dominant) are applied, with results weighted by the relative volume share of each tree species simulated at each NFI plot (Figure S4).…”

Editorial for Special Issue: “Remotely Sensed Albedo”

“…Forecasting surface albedo on a monthly basis is possible from the forest structure, snow cover, and near surface air temperature. New insights are offered between the impact of a changing climate on albedo and anthropogenic land use/land cover change (LULCC) [6].…”