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Estimate Evapotranspiration from Remote Sensing Data -- An ANN Approach Feihua Yang ECE539 Final Project Fall 2003. What’s included?. Introduction Statement of Purpose Work Perfomed Data Collection Data Pre-processing ANN Design ANN Testing Results Discussion. Introduction.

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What’s included?

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  1. Estimate Evapotranspiration from Remote Sensing Data-- An ANN ApproachFeihua YangECE539 Final ProjectFall 2003

  2. What’s included? • Introduction • Statement of Purpose • Work Perfomed • Data Collection • Data Pre-processing • ANN Design • ANN Testing • Results • Discussion

  3. Introduction • Evapotranspiration (ET): • The combination of water evaporated and transpired by plants. Its energy equivalent is latent heat flux (LE). • Critical in understanding climate dynamic and in watershed management, agriculture and wild fire assessment • Can be estimated from land surface by using satellite remote sensing and validated by ground truth measured at flux towers • Existing approach: • No widely accepted methods to estimate ET from RS on continental to global scales • Why ANN? • ANN is powerful in investigatingthe mechanism of a complex system from its past behavior. It gives an alternative way to estimate ET from RS.

  4. Statement of Purposes • Explore the dynamic relationships between ET (LE) and its affecting factors through back propagation • Output: Latent heat flux (LE) • Feature: Land surface temperature (LST) Saturated vapor pressure (SVP) Solar radiation (RA) Enhanced vegetation index (EVI)

  5. Work Performed

  6. Work Performed (continued) • Data Collection • Latent heat flux (AmeriFlux) • land surface temperature (MOD11) • saturated vapor pressure (derived from land surface temperature) • solar radiation (GOES) • vegetation index (MOD13) • Data Pre-processing • N/A data • Normalize • Data partition • ANN Design • ANN Testing

  7. Work Performed (continued) • Data Collection • Data Pre-processing • ANN Design • Parameters selection (number of hidden layer = 1)

  8. Work Performed (continued) • Data Collection • Data Pre-processing • ANN Design • Parameters selection (number of hidden layer = 1) • Output of the best configuration for each of a 3-way cross validation with 3 trials

  9. Work Performed (continued) • Data Collection • Data Pre-processing • ANN Design • Parameters selection (number of hidden layer = 1) • Output of the best configuration for each of a 3-way cross validation with 3 trials • Parameters selected for the result in this study: • Number of hidden layer: 1 • Neurons in the hidden layer: 3 • Learning rate: 0.3 • Momentum: 0.8 • Epoch size: 64 • Maximum number of epochs to run:1000

  10. Work Performed (continued) • Data Collection • Data Pre-processing • ANN Design • ANN Testing • Using 3-way cross validation

  11. Results • The results from ANN is compared to a baseline study. The R-squared value based on ANN is 0.7, which is improved compared to the baseline study. • The slope between ground truth LE and approximated LE is 0.84, which is closer to 1 than 0.62 from the baseline study.

  12. Discussion • ANN provides an alternative way to predict ET from RS. • This study does not take existing knowledge between ET and its formative environmental variables into account. • Integrate existing knowledge of ET mechanism in ANN probably will improve the performance of ANN more. • Other ANN structure such as SVM, RBF and mixture expert system could be tested to find a best ANN solution for ET.

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