1. A Temporal Filtering Algorithm to Reconstruct Daily Albedo Series Based on GLASS Albedo product Nanfeng Liu 1,2, Qiang Liu 1,2, Lizhao Wang 2, Jianguang Wen 1 1 IRSA, Chinese Academy of Sciences 2 GCESS, Beijing Normal University Outline:  Motivation  Description of GLASS preliminary albedo product  Temporal filtering algorithm  Basic idea  Temporal filtering formula  Global albedo a-priori statistics  Preliminary result  Conclusion

2. Motivation Current albedo products: MODIS, POLDER, MERIS, MSG Temporal resolution: 8-day ~ 1 month Spatial resolution: 0.5km ~ 20km Drawback: Low temporal resolution Large number of gaps Objective of GLASS albedo products: To provide daily spatially complete land surface albedo products

3. Description of GLASS albedo preliminary product GLASS (Global LAnd Surface Satellite)project: To provide land surface parameter datasets with high resolution (sponsored by Chinese “863” programme) Parameters including: Albedo Emissivity(8-day, 1km) LAI(8-day, 1km) PAR(3-hour, 5km) GLASS preliminary albedo data set characteristics: Algorithm: AB (Angular Bin) algorithm (Liang et al, 2005; Qu et al, 2011) Resolution: 1km, 1-day Projection: Sinusoidal Data format: HDF-EOS

4. Description of GLASS albedo preliminary product GLASS albedo preliminary product deficiencies: Frequent data gaps caused by: Cloud coverage Seasonal snow Sharp fluctuations in time series caused by: Data noise Uncertainty of AB inversion algorithm Temporal filtering algorithm objective: To fill in data gaps To smooth the albedo time series

5. Albedo map (h11v04, 2005) Grey and black colors represent the data gaps

7. Temporal filtering algorithm - Basic idea Basic idea: Firstly, based on the temporal correlation of albedo measurements between neighboring days, it is reasonable to assume that the albedo values between neighboring days are linearly related. Then based on the Bayesian theory, it is possible to predict the true albedo with the neighboring days’ AB albedo retrievals.

8. Temporal filtering algorithm - Basic idea Global albedo a-priori statistics Multi-year global albedo products Multi-day AB albedo products Temporal filtering GLASS albedo Build linear model

9. Temporal filtering algorithm - Temporal filtering formula Temporal filtering algorithm is a weighted average of neighboring days’ albedo! Derived from global a-priori statistics

10. Temporal filtering algorithm - Global albedo a-priori statistics Data Set used: MODIS albedo products(MCD43B3, 2000-2009) The same inputs as AB algorithm (MOD09) Stability Statistics include: Multi-year average and variance Correlation coefficients of albedo between two neighboring days Resolution: 5km, 8-days

11. Temporal filtering algorithm - Global albedo a-priori statistics Calculate regression coefficients with background filed Albedo a-priori statistics

12. Temporal filtering algorithm - preliminary result

14. Before filteringAfter filtering

15. Temporal filtering algorithm - conclusion SiteTimeAAD1AAD2AAD3 Forts Peck Winter0.02010.04850.0473 Summer0.00940.0110.0126 Whole year0.01190.02200.0231 Flagstaff Wildfire Winter0.01490.09990.1014 Summer0.01020.01620.0199 Whole year0.01480.04530.0454 Table1: Validation results of temporal filtering algorithm AAD: Average Absolute Deviation; AAD1: AAD between GLASS albedo and temporal algorithm results; AAD2: AAD between ground measured albedo and temporal algorithm results; AAD3: AAD between ground measured albedo and GLASS albedo and temporal algorithm results;

16. Temporal filtering algorithm - conclusion The temporal correlation of neighboring day’s albedo is considered in the TF method; Temporal filtering algorithm is an weighted average of neighboring days’ albedo values; TF method can fill in data gaps and smooth albedo series; TF method sometimes will smooth the albedo series overly; Further validations are required;

17. Thank you for your attention! Any Questions?