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