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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

AMS Moody1

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