1 / 14

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. Multiple Sensors, One target: estimating ground vegetation variability through time

kale
Télécharger la présentation

Molly E. Brown David J. Lary Hamse Mussa

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Neural Nets to Derive Sensor-Independent Climate Quality Vegetation Data:AVHRR and MODIS NDVI Datasets Molly E. Brown David J. Lary Hamse Mussa

  2. 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

  3. Global NDVI – A Key Data Input • Multiple satellites, multiple datasets

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

  5. 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.

  6. 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)

  7. GIMMS AVHRR VIg Input to Neural Networks MODIS NDVI GISS Soil Map Topo Map TOMS Reflectivity TOMS Ozone TOMS Aerosol

  8. Neural Networks 20 Nodes Input

  9. Results Neural Net Correction Removes high latitude differences, as well as those in the tropics. Difference Before NN Difference After NN

  10. 24 years of NDVI data

  11. Difference before correction Difference after correction Scatter plot of AVHRR-MODIS (x axis) vs Corrected AVHRR-MODIS (y axis)

  12. Time Series Time Series Of all three datasets

  13. Differences between AVHRR, MODISstill remain, but are less

  14. 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

More Related