Using Neural Nets to Derive Sensor-Independent Climate Quality Vegetation Data:
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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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Molly e brown david j lary hamse mussa

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

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

Global NDVI – A Key Data Input

  • Multiple satellites, multiple datasets


Differences between sensors

Differences between Sensors

  • Spectral Characteristics means variable sensitivity to atmospheric interference such as clouds, ozone, scattering, etc.


Source of differences con t

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

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

GIMMS AVHRR VIg

Input to Neural Networks

MODIS NDVI

GISS Soil Map

Topo Map

TOMS Reflectivity

TOMS Ozone

TOMS Aerosol


Neural networks

Neural Networks

20 Nodes

Input


Results

Results

Neural Net Correction

Removes high latitude differences, as well as those in the tropics.

Difference Before NN

Difference After NN


Molly e brown david j lary hamse mussa

24 years of NDVI data


Molly e brown david j lary hamse mussa

Difference before

correction

Difference after

correction

Scatter plot of

AVHRR-MODIS (x axis) vs

Corrected AVHRR-MODIS

(y axis)


Time series

Time Series

Time Series

Of all three

datasets


Differences between avhrr modis still remain but are less

Differences between AVHRR, MODISstill remain, but are less


Correcting gimms ndvig with toms sza and soils data

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


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