A Temporal Filtering Algorithm to Reconstruct Daily Albedo Series Based on GLASS Albedo product

1. A Temporal Filtering Algorithm to Reconstruct Daily Albedo Series Based on GLASS Albedo product Nanfeng Liu1,2, Qiang Liu1,2, Lizhao Wang2, Jianguang Wen1 1IRSA, Chinese Academy of Sciences 2GCESS, 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 LAndSurface 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 Multi-day AB albedo products Multi-year global albedo products Global albedo a-priori statistics Build linear model Temporal filtering GLASS albedo

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

13. Temporal filtering algorithm- preliminary result

14. Temporal filtering algorithm- preliminary result Before filtering After filtering

15. Temporal filtering algorithm- conclusion 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?