Filling gaps of black-sky surface albedo of the Arctic sea ice using gradient boosting and brightness temperature data

• Published in: International Journal of Applied Earth Observation and Geoinformation
• Main Authors: Emmihenna Jääskeläinen, Terhikki Manninen, Janne Hakkarainen, Johanna Tamminen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2022
• Subjects: Surface albedo; Machine learning; Gradient boosting; Gap filling; geo; envir; Arctic; The Gib; albedo; Sea ice
• Online Access: https://doi.org/10.1016/j.jag.2022.102701; https://doaj.org/article/4b0682e951f348cba29589c1fb2573e7

Surface albedo is a necessary parameter for climate studies and modeling. There is a need for a full spatial coverage of albedo data, but clouds and high solar zenith angle cause missing values to the optical satellite products, especially around the polar areas. Therefore, our motivation is to develop gap filling models. For that purpose, we will apply monthly gradient boosting (GB) based models to the Arctic sea ice area of the 34 years long albedo time series of the Satellite Application Facility on Climate Monitoring (CM SAF) project. We demonstrate the ability of the GB models to accurately fill missing data using albedo monthly mean, brightness temperature, and sea ice concentration as model inputs. Monthly GB models produce the most unbiased, precise, and robust estimates when compared to alternative estimates presented, such as monthly mean albedo values or estimates from monthly linear regression (LR) models. The mean relative differences between GB based estimates and original pentad values vary from −20% to 20% with RMSE being 0.048, compared to relative differences varying from −20% to over 60% with RMSE varying from 0.054 to 0.074 between other estimates and original pentad values. Pixelwise mean differences and standard deviations (std) over the whole Arctic sea ice area show that GB based estimates are more accurate (mean differences from −0.02 to 0.02) and more precise (std from 0.02 to 0.08) than other estimates (mean differences varying between −0.05 to over 0.05, and std varying from around 0.03 to over 0.1). Also, albedo of the melting sea ice is predicted better by the GB model, with negligible mean differences, compared to the LR model. Based on these results, we show that GB method is a useful technique to fill missing data, and the brightness temperature and sea ice concentration are useful additional model input data sources.