1. Estimate Evapotranspiration from Remote Sensing Data -- An ANN Approach Feihua Yang ECE539 Final Project Fall 2003

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

3. 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 investigating the mechanism of a complex system from its past behavior. It gives an alternative way to estimate ET from RS. Introduction

4. 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) Statement of Purposes

5. Work Performed

6. 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 Work Performed (continued)

7. Data Collection Data Pre-processing ANN Design Parameters selection (number of hidden layer = 1) Trials Variables123456Default number of neurons 35122030505 learning rate 0.010.10.30.60.81.00.1 momentum 0.10.30.40.50.81.00.5 epoch size 102030648012030 Max. number of epochs to run 200300500100020005000300

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 Parameter Trials Number of neuronsLearning rate Momentu m Epoch size Max. number of epochs to run CV130.30.8801000 CV2500.3 645000 CV3300.10.5645000 CV130.30.5805000 CV21200.3 642000 CV330.30.8801000 CV1200.60.8302000 CV2500.30.5642000 CV3300.1 802000

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.

13. 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.