Molly E. Brown David J. Lary Hamse Mussa
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Transcript of Molly E. Brown David J. Lary Hamse Mussa
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Using Neural Nets to Derive Sensor-Independent Climate
Quality Vegetation Data:AVHRR and MODIS NDVI Datasets
Molly E. Brown
David J. Lary
Hamse Mussa
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Outline
• Multiple Sensors, One target: estimating ground vegetation variability through time
• Inputs and Procedure for Neural Network training and correction
• Results of Correction: – Relationship to MODIS, Rainfall– Time Series at EOS sites
• Future Work
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Global NDVI – A Key Data Input• Multiple satellites, multiple datasets
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Differences between Sensors• Spectral Characteristics means variable
sensitivity to atmospheric interference such as clouds, ozone, scattering, etc.
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Source of Differences, con’t
• Compositing Methods
• Spatial and Temporal Sampling
• Differences in atmospheric correction
• Diurnal cycle of surface-atmosphere properties affecting the sampling of land surface
• Others…This paper tries to address those differences caused by Atmospheric Interference of signal.
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Neural Networks: Procedure
• Train Data on 80% of points, randomly sampled, on MODIS-AVHRR overlap period (Jan ‘00-Dec ‘03)– Root Mean Error of training tested on 10%, not
included in training– Fewer the inputs the better – inputs were chosen as
atmospheric constituents most likely to affect AVHRR sensor more than MODIS
• Apply Weighting Functions to input through time to correct the entire AVHRR archive using historical TOMS data (Jan ’82 – Dec ’03)
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Input to Neural Networks
TOMS Reflectivity TOMS Ozone TOMS Aerosol
GISS Soil Map
GIMMS AVHRR VIg
MODIS NDVI
Topo Map
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Neural Networks
20 Nodes
Input
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Results
Difference Before NN
Difference After NN
Neural Net CorrectionRemoves high latitude differences, as well as those in the tropics.
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24 years of NDVI data
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Difference before correction
Difference after correction
Scatter plot of AVHRR-MODIS (x axis) vsCorrected AVHRR-MODIS(y axis)
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Time Series
Time SeriesOf all threedatasets
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Differences between AVHRR, MODISstill remain, but are less
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Correcting GIMMS NDVIg with TOMS, SZA and Soils data
• Method has promise:– Is very flexible, can be used to fit AVHRR to SeaWiFS,
SPOT or MODIS datasets– Dataset correction improves the relationship between
AVHRR and MODIS in the tropics and northern latitudes
– Does not seem to remove interannual variability of AVHRR
– Uses observed conditions to correct differences due to aerosols and other atmospheric contaminants.
• Can be used to project NDVI as well – These results show the ‘zero month’ projection, but we can also do ‘one, two and three month’ projections