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Spatially Complete Global Surface Albedos Derivedfrom Terra/MODIS Data
Michael D. King1, Eric G. Moody1,2, Steven Platnick 1, and Crystal Schaaf 3 1NASA Goddard Space Flight Center
2L3-Communications GSI3Boston University
Operational MODIS surface albedo data product (MOD43B3/MCD43B3) 0.47, 0.56, 0.67, 0.86, 1.24, 1.64, 2.1 m , 0.3-5.0, 0.7-5.0, 0.3-0.7 m 1 km spatial resolution (sinusoidal projection stored as 10 x 10 tiles) 16-day periodicity (001, 017, , 353)
Limitations Spatial and temporal gaps due to cloud cover and seasonal snowMotivation
Ancillary input for ground-based (AERONET), airborne, and satellite remote sensing Land surface and climate modeling Global change research projects
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IGBP Ecosystem Classification (MOD12Q1)
0 Water
5 Mixed Forests
6 Closed Shrublands
11 Permanent Wetlands
12 Croplands
1 Evergreen Needleleaf Forest
2 Evergreen Broadleaf Forest
3 Deciduous Needleleaf Forest
4 Deciduous Broadleaf Forest
7 Open Shrublands
8 Woody Savannas
9 Savannas
10 Grasslands
13 Urban and Built-Up
14 Cropland/Natural Veg. Mosaic
15 Snow and Ice
16 Barren or Sparsely Vegetated
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Compute regional ecosystem statistics 0.5, 1-5, 10, 10x20, 10x30 box sizes
Obtain pixel-level and regional ecosystem statistical phenological trends Curves will have different magnitudes Shapes will be consistent
Impose shape of curves onto pixel level data Only 10-15% of pixels have adequate coverage to obtain phenology Maintains pixel level detail with appropriate phenology
Select curve that has best representation Representation of existing pixel data
Completeness of trend Bias towards smaller statistical box sizes
Fill missing values with selected curve
General Methodology
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Prepare MOD43B3 data Mosaic SIN data onto 1-minute climate modeling grid Apply MOD43B3 QA
Remove lesser quality pixelsRemove seasonal snow
Prepare MOD12Q1 data Mosaic SIN data onto 1-minute climate modeling grid
Compute regional ecosystem statistics CMG grids at 0.5, 1-5, 10, 10x20, 10x30 A priori computation
Processing Preparation
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Phenological Curves for Deciduous Broadleaf ForestVermont, USA
Phenological Curves
Phenological Curveswith Offset Applied
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Continental United StatesJuly 12-27, 2002
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Cloud and snow cover obscure full decay stateOver hemisphere average of high latitudes
Unique ecosystem and wavelength extrema percent change Compute % change from pixels with adequate representation
For each pixel/statistical curve Pin winter endpoints with value computed from
Percent changeSummer extrema
Then apply General Methodology
Seasonal Snow Methodology
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Phenological Curve for Mixed ForestNorthern Russia
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Europe and Western AsiaDecember 3-18, 2002
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Clouds obscure trends over large regions Usually full growth stage is obscured Even 10x30 boxes may not observe complete temporal trend
Compute 1 statistical curve per ecosystem class 5-15 Latitude belts
Centered around pixels latitude Yearly phenological behavior curves
Instead of 2 half year curvesImpose shape of curve onto existing pixel data
Persistent Cloud Methodology
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Missing SeasonAsian Monsoon
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Indian Subcontinent during MonsoonJune 10-26, 2002
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Limited Yearly DataTropical Cloudiness
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South America during Rainy SeasonJune 10-26, 2002
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Africa in the Presence of Persistent CloudsDecember 3-18, 2002
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Spatially Complete Albedo Mapsl = 0.858 m
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Spectral Variability by Ecosystem ClassJune 26 - July 11, 2001
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Temporal Variability by Ecosystem ClassJanuary - July 2001
Cropland Ecosystem
Urban Ecosystem
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2001 (v3) and 2002 (v4) spatially complete albedo maps Maps and QA stored in 1-minute rectangular coordinates First seven MODIS wavelengths and 3 broadband White- and black-sky albedo values
Currently processing 2000-2004 (v4)2001 (v3) and 2002 (v4) statistics of filled albedo maps
0.5, 1, 2, 3, 4, 5, 10 box sizes Statistics computed in boxes and by ecosystem class in boxes
Statistics of snow albedos Hemispheric averages of MOD43B3 validated data Separated by ecosystem class and NISE wet/dry classification
IGBP ecosystem classification map Maps and QA stored in 1-minute rectangular coordinates
Available Products
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Used near real-time ice and snow extent (NISE) dataset Distinguishes land snow and sea ice (away from coastal regions) Identifies wet vs dry snow
Projected onto an equal- area 1 angle grid
Aggregate snow albedo from MOD43B3 product Surface albedo flagged as snow
Aggregate only snow pixels whose composite NISE snow type is >90% is flaggedas either wet or dry snow in any 16-day period
Hemispherical multiyear statisticsSeparate spectral albedo by ecosystem (MOD12Q1)
Results represent average snow conditions Additional sources of variability include snow depth, snow age, grain size,
contamination (soot), and, in the case of black-sky albedo, solar zenith angle
Spectral Albedo of Snow
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Snow Albedo by Forest EcosystemsNorthern Hemisphere Multiyear Average (2000-2004)
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Snow Albedo for Sparse Vegetation EcosystemsNorthern Hemisphere Multiyear Average (2000-2004)
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Spatially Complete White-Sky AlbedoJanuary 1-16, 2002
0.6
0.8
0.0
0.2 S u r
f a c e
A l b e
d o
( 0 . 8
6 m
)
0.4
Snow-free
Snow-covered
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Spatially complete surface albedo datasets have been generated Uses high-quality operational MODIS surface albedo dataset Imposes phenological curve and ecosystem-dependent variability White- and black-sky albedos produced for 7 spectral bands and 3 broadbands
See modis-atmos.gsfc.nasa.gov for data access and further descriptions, and Moody, E. G., M. D. King, S., Platnick, C. B. Schaaf, and F. Gao, 2005: Spatially
complete global spectral surface albedos: Value-added datasets derived fromTerra MODIS land products. IEEE Trans. Geosci. Remote Sens ., 43 , 144-158.
Spectral albedo of snow Hemispheric averages of MOD43B3 validated data Separated by ecosystem class and NISE wet/dry classification Additional variability due to snow depth, age, grain size, and contamination not
accessible from MODIS data alone, and hence not incorporated
Summary and Conclusions