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 Quality Vegetation Data:

  • 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 Quality Vegetation Data:

  • Multiple satellites, multiple datasets


Differences between sensors
Differences between Sensors Quality Vegetation Data:

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


Source of differences con t
Source of Differences, con’t Quality Vegetation Data:

  • 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 Quality Vegetation Data:

  • 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 Quality Vegetation Data:

Input to Neural Networks

MODIS NDVI

GISS Soil Map

Topo Map

TOMS Reflectivity

TOMS Ozone

TOMS Aerosol


Neural networks
Neural Networks Quality Vegetation Data:

20 Nodes

Input


Results
Results Quality Vegetation Data:

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 Quality Vegetation Data:


Molly e brown david j lary hamse mussa

Difference before Quality Vegetation Data:

correction

Difference after

correction

Scatter plot of

AVHRR-MODIS (x axis) vs

Corrected AVHRR-MODIS

(y axis)


Time series
Time Series Quality Vegetation Data:

Time Series

Of all three

datasets


Differences between avhrr modis still remain but are less
Differences between AVHRR, MODIS Quality Vegetation Data:still remain, but are less


Correcting gimms ndvig with toms sza and soils data
Correcting GIMMS NDVIg with TOMS, SZA and Soils data Quality Vegetation 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