molly e. brown david j. lary hamse mussa
Post on 01-Jan-2016
30 Views
Preview:
DESCRIPTION
TRANSCRIPT
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)
Input to Neural Networks
TOMS Reflectivity TOMS Ozone TOMS Aerosol
GISS Soil Map
GIMMS AVHRR VIg
MODIS NDVI
Topo Map
Neural Networks
20 Nodes
Input
Results
Difference Before NN
Difference After NN
Neural Net CorrectionRemoves high latitude differences, as well as those in the tropics.
24 years of NDVI data
Difference before correction
Difference after correction
Scatter plot of AVHRR-MODIS (x axis) vsCorrected AVHRR-MODIS(y axis)
Time Series
Time SeriesOf all threedatasets
Differences between AVHRR, MODISstill remain, but are less
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
top related