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Open AccessArticle

Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm

1
State Key Laboratory of Remote Sensing Science, Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, BNU, Beijing 100875, China
2
School of Surveying & Land Information Engineering, Henan Polytechnic University, Henan 454000, China
3
Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 753; https://doi.org/10.3390/rs11070753
Received: 6 March 2019 / Revised: 22 March 2019 / Accepted: 26 March 2019 / Published: 28 March 2019
(This article belongs to the Special Issue Remotely Sensed Albedo)
Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an “amalgamation albedo” approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085–0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution. View Full-Text
Keywords: land surface albedo; time series; high spatio-temporal resolution; EnKF land surface albedo; time series; high spatio-temporal resolution; EnKF
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MDPI and ACS Style

Zhang, G.; Zhou, H.; Wang, C.; Xue, H.; Wang, J.; Wan, H. Time Series High-Resolution Land Surface Albedo Estimation Based on the Ensemble Kalman Filter Algorithm. Remote Sens. 2019, 11, 753.

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