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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. Outline. Multiple Sensors, One target: estimating ground vegetation variability through time
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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
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
Global NDVI – A Key Data Input • Multiple satellites, multiple datasets
Differences between Sensors • Spectral Characteristics means variable sensitivity to atmospheric interference such as clouds, ozone, scattering, etc.
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.
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)
GIMMS AVHRR VIg Input to Neural Networks MODIS NDVI GISS Soil Map Topo Map TOMS Reflectivity TOMS Ozone TOMS Aerosol
Neural Networks 20 Nodes Input
Results Neural Net Correction Removes high latitude differences, as well as those in the tropics. Difference Before NN Difference After NN
Difference before correction Difference after correction Scatter plot of AVHRR-MODIS (x axis) vs Corrected AVHRR-MODIS (y axis)
Time Series Time Series Of all three datasets
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