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 Sciences2GCESS, Beijing Normal University
Outline:MotivationDescription of GLASS preliminary albedo productTemporal filtering algorithm
Basic idea Temporal filtering formula Global albedo a-priori statistics Preliminary result
Conclusion
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
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
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
Albedo map (h11v04, 2005)Grey and black colors represent the data gaps
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.
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Temporal filtering algorithm- Basic idea
Global albedo a-priori statistics
Multi-year global albedo products
Multi-day AB albedo products
Temporal filtering
GLASS albedo
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Temporal filtering algorithm- Temporal filtering formula
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Temporal filtering algorithm is a weighted average of neighboring days’ albedo!
Derived from global a-priori
statistics
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
Temporal filtering algorithm- Global albedo a-priori statistics
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Albedo a-priori statistics
Temporal filtering algorithm- preliminary result
Temporal filtering algorithm- preliminary result
Temporal filtering algorithm- preliminary result
Before filtering After filtering
Temporal filtering algorithm- conclusion
Site Time AAD1 AAD2 AAD3
Forts PeckWinter 0.0201 0.0485 0.0473
Summer 0.0094 0.011 0.0126Whole year 0.0119 0.0220 0.0231
Flagstaff Wildfire
Winter 0.0149 0.0999 0.1014Summer 0.0102 0.0162 0.0199
Whole year 0.0148 0.0453 0.0454
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;
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;
Thank you for your attention!Any Questions?
