DISAGGRREGATION OF REMOTELY SENSED SOIL MOISTURE USING NEURAL NETWORKS

1. DISAGGRREGATION OF REMOTELY SENSED SOIL MOISTURE USING NEURAL NETWORKS Marius P. Schamschula, William L. Crosson, Charles Laymon, Ramarao Inguva, and Adrian Steward

2. Background • Hydrological modeling • Models need to be initialized • Point wise data is not sufficient to initialize models • Want to use remotely sensed data • Resolution of sensors is insufficient for hydrological models

3. Disaggregation • We need some method of integrating the low resolution remotely sensed data into the hydrological model • This requires upsampling or disaggregating the low resolution data to high resolution

4. Disaggregation: Issues • We cannot create information • We must supply additional information • Due to discontinuities, the nature of the data is not suitable to smoothing or spline based interpolation

5. Disaggregation: Approaches • Bayesian statistics • Requires priors (high-resolution) • Linear Regression • Artificial Neural Networks (ANNs) • Require additional high-resolution information

6. The Data: Little Washita River Watershed • The dataset is based on the Southern Great Plains ‘97 (SGP ‘97) field experiment • 16 over-flights with the Electronically Scanned Thinned Array Radiometer (ESTAR) instrument • One month of meteorological/hydrological data

7. Additional Data Needed for Training • The 16 over-flights are insufficient for training an ANN • Additional data was derived from the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS)hydrological model and a Radiative Transfer Model (RTM) • We will use synthetically generated datasets

8. Neural Networks: Inputs • High resolution • Precipitation (1, 2-3, 4-6, 7-12, 13-24, 25-48, 49-96, 97-192 hours before current time) • Soil type: sand and clay content • Vegetation water content • Upstream contributing area • Low resolution • Microwave emissivity

9. Neural Networks: Output • High Resolution • Top layer soil moisture

10. Linear Neural Network

11. Linear Neural Network: “Design” • A linear ANN is not trained. The weights can be directly determined by matrix inversion • Design is fast: for about 1000 points, 350 time steps the process takes less than 10 seconds

12. Linear Neural Network: A “Little” Problem • The valid range of soil moisture is between 0 and 1 • The linear ANN in very wet conditions will overshoot the maximum value • We can manually clip the maximum at 1… • Need to do better

13. Clipping Linear Neural Network

14. Clipping Linear Neural Network: Training • Requires more computational resources: • 400 MB RAM • About a minute and a half CPU time(G4 533 MHz)

15. Differences between Linear and Clipping Linear NNs • Given that the ANN architectures differ only in their transfer functions, we find • Weights are similar, except: • Precipitation from 2 to 3 hours before current time • Clipping Linear ANN requires no post processing for overshoot

16. 1 0 1.6 km “Truth” 12.8 km Clipping Linear Neural Network: Dry Conditions

17. 1 “Truth” 1.6 km 0 12.8 km Clipping Linear Neural Network: Wet Conditions

18. Clipping Linear Neural Network: High Resolution Movie

19. Clipping Linear Neural Network: Low Resolution Movie

20. Root Mean Square Error

21. Future Work • Refine the transfer function: asymptotic exponential • An additional layer with transfer functions appropriate for the various inputs • Neighborhood interconnectivity

22. Conclusion • We can use an ANN to perform data disaggregation of low resolution remotely sensed microwave signals by fusing in other data such as precipitation, soil type, etc. • More information @http://www.caos.aamu.edu/HSCaRS/