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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #320896

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Deriving a global land surface albedo product from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach

Author
item Tao, He - University Of Maryland
item Liang, Shunlin - University Of Maryland
item Wang, Dongdong - University Of Maryland
item Cao, Yunfeng - University Of Maryland
item Gao, Feng
item Yu, Yunyue - National Oceanic & Atmospheric Administration (NOAA)
item Feng, M. - University Of Maryland

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/17/2017
Publication Date: 10/31/2017
Citation: Tao, H., Liang, S., Wang, D., Cao, Y., Gao, F.N., Yu, Y., Feng, M. 2017. Deriving a global land surface albedo product from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment. 24:181-196. https://doi.org/10.1016/j.rse.2017.10.031.
DOI: https://doi.org/10.1016/j.rse.2017.10.031

Interpretive Summary: Land surface albedo is a key parameter driving Earth’s climate. Albedo maps at high spatial resolution are needed for the field scale applications. Previous studies have been focused on mapping land surface albedo using Landsat imagery from recent years. This paper presents an approach that can be extended to earlier Landsat imagery. Comparing to ground measurements, the root mean square errors of Landsat albedo are 0.02 to 0.03 over snow-free and snow-covered surfaces. Long-term accurate albedo estimates from Landsat imagery is critical for the modeling carbon and water cycles at the field and regional scales required by the National Agricultural Statistics Service and Foreign Agricultural Service.

Technical Abstract: Surface albedo is widely used in climate and environment applications as an important parameter for controlling the surface energy budget. There is an increasing need for fine resolution (< 100 m) albedo data for use in small scale applications and for validating coarse-resolution datasets; however, such products with long-term global coverage are not available thus far. In this study, we propose a refined algorithm based on the well-established direct estimation approach, and apply it to Landsat data obtained by Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI). By using Landsat spectral response functions and a database of bidirectional reflectance distribution function (BRDF) into radiative transfer simulations, a unified algorithm is developed to estimate surface albedo directly from the Landsat data with few ancillary inputs. To overcome the saturation problems in TM and ETM+ data over very bright surfaces, a refined algorithm is employed by using only non-saturated bands. The validation results against globally distributed ground measurements show that our algorithm can achieve root-mean-square-errors (RMSEs) of 0.022 to 0.031 over both snow-free and snow-covered surfaces. In addition, we show the high potential of the earlier MSS data for producing consistent surface albedo estimations based on inter-comparison with TM-based results with an RMSE of 0.012 and R2 of 0.893. This long-term, fine resolution surface albedo product can date back to the early 1980s, which allows for improved understanding of long-term climate change effects. This product is particularly useful for small scale applications.