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Estimating Evapotranspiration from Remote Sensing Data using ANN Approach

Explore the use of Artificial Neural Networks to estimate Evapotranspiration from Remote Sensing data, analyzing factors like Land Surface Temperature, Solar Radiation, and Vegetation Index. The study collected data on Latent Heat Flux, LST, Saturated Vapor Pressure, Solar Radiation, and Vegetation Index, normalized it, and designed an ANN with parameters like 1 hidden layer and 3 neurons. The ANN testing showed improved results compared to baseline studies, with an R-squared value of 0.7. The discussion suggests integrating existing knowledge of ET mechanisms into ANN models for enhanced performance and further testing diverse ANN structures like SVM and RBF.

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Estimating Evapotranspiration from Remote Sensing Data using ANN Approach

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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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