## 4 Results
### Regional Timing Albedo Estimation and Verification  
We validated the proposed assimilation algorithm on five types of land surface areas, each area containing a ground-based station. The five stations, as mentioned above, were US-Bar, AU-Wac, IT-Sro, US-Arm, and AU-Stp (Table 1). The results are shown in Figure 7. The daily estimation result was difficult to display and the beginning date for each site was different, so we used nine days-worth of estimation results from each site.  
Figure 6: Estimated albedos and ground measurements at AmeriFlux and Flxunet sites: (**a**) DBF; (**b**) EBF; (**c**) ENF; (**d**) CRO; (**e**) GRA. Different colors represent different sites.  
The EnKF assimilation method uses TM albedo as observation data. The estimation is strongly dependent on the quantity of TM albedo values. To obtain time-continuous, high spatial resolution albedo data, the EnKF algorithm updates the background field from the first cloudless TM image; therefore, assimilation results will be improved as more TM images become available. Figure 7 shows the estimated time series albedos for five different land cover types. Again, the starting point of each time series was not exactly the same as was determined by the availability of high-quality TM data. Starting with the first cloudless TM image, the algorithm can obtain an albedo dataset with a temporal resolution of one day and a spatial resolution of 30 m (shown here as 25-day or 35-day intervals), thereby reflecting albedo fluctuations within a one-year period.  
As verification, we compared the albedo generated by the EnKF algorithm and the TM albedo directly estimated on the same date. Figure 8 shows that the EnKF and TM albedo results are consistent. The scatter plot has RMSE values between 0.0031 and 0.0112, and R\({}^{2}\) values between 0.8584 and 0.9494. One exception was the farmland area (US-Arm site), where regional estimation and TM albedo verification results had R\({}^{2}\) lower than the other four surface types, and RMSE higher than other landforms. This may be due to the fact that farmland surface vegetation is more affected by anthropogenic factors than other land surface types; the jump in surface albedo makes the deviation of farmland albedo estimations larger than others. In this case, inducing more high-resolution images into the assimilation process could effectively improve the albedo estimation accuracy.  
Figure 7: Shortwave albedo maps generated by EnKF for five different land surface types. Day of year (DOY) indicated for each albedo map. Estimations have one-day temporal resolution starting with the first cloud-free TM image (only a portion of the results are shown).  
Figure 8: Shortwave albedo map generated by EnKF method with directly estimated TM albedo for five different land surface types: (**a**) DBF at US-Bar site (2009); (**b**) EBF at AU-Wac site (2007); (**c**) ENF at IT-Sro site (2009); (**d**) CRO at US-Arm site (2010); (**e**) GRA at AU-Stp site (2009). Right-hand scatter plots show consistency between the two sets of results, as number of dots gradually decreases from red to blue.  
The DBF, EBF, and CRO sites selected in this study are heterogeneous research areas and have RMSE values less than 0.01 and R\({}^{2}\) values greater than 0.9, which indicate that the algorithm is well-suited to albedo estimation in heterogeneous areas.