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UW-Madison ECE 539 - Estimate Evapotranspiration from Remote Sensing Data

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1. Introduction2. Work Performed2.1 Data Collection2.2 Data Preprocessing2.3 ANN Design2.3.1 ANN Structure2.3.2 ANN Testing2.3.3 Matlab Program Description3. Results3.1 ANN Results3.2 Baseline Study4. DiscussionAppendix A Baseline Study MethodAppendix B Selective Matlab ProgramsEstimate Evapotranspiration from Remote Sensing Data-- An Artificial Neural Network ApproachCS539 Artificial Neural Network and Fuzzy LogicFinal ProjectDecember 15, 2003Feihua Yang 0TABLE OF CONTENTS1. INTRODUCTION....................................................................................................................................12. WORK PERFORMED..........................................................................................................................22.1 ...........................................................................................................DATA COLLECTION.......................................................................................................................................22.2 DATA PREPROCESSING....................................................................................................32.3 ANN DESIGN..................................................................................................................42.3.1 ANN Structure.......................................................................................................42.3.2 ANN Testing..........................................................................................................62.3.3 Matlab Program Description................................................................................63. RESULTS.....................................................................................................................................................63.1 ANN RESULTS..................................................................................................................63.2 BASELINE STUDY.............................................................................................................84. DISCUSSION.............................................................................................................................................8APPENDIX A BASELINE STUDY METHOD.................................................................................11APPENDIX B SELECTIVE MATLAB PROGRAMS..................................................................12Estimate Evapotranspiration from Remote Sensing Data-- An Artificial Neural Network Approach1. IntroductionEvapotranspiration (ET) is the combination of water that is evaporated and transpired by plants. Its energy equivalence is latent heat flux (LE) which is the energy required to transform water into vapor. ET is critical in understanding climate dynamic, watershed management, agriculture and wild fire assessment. Evapotranspiration is a complex process which is affected by environmental factors including land surface temperature, vapor pressure deficit, surface radiation, humidity, wind velocity, vegetation coverage. Several methods have been developed to estimate ET by integrating ground measurements with remote sensed data (Nemani 1989 and 1993, Nishida 2003). However, the results are far from promising when compared to ground truth.Artificial neural network (ANN) is a computing system motivated by the function of human brain. The basic element of an ANN is called neuron. Each neuron is linked to other neurons with varying coefficients of connectivity called weights. Learning is accomplished by adjusting weights to make the overall network output desired results. Once a network has gained its weights through learning, the weights can be used to produce output when an inputpattern is given. The learning process is called training in ANN.ANN is powerful in investigating the mechanism of a complex system from its past behaviors (Haykin 1999). In many ecosystem modeling and remote sensing, the mechanism behind ecosystem processes is often too complicated to be expressed explicitly by physical ormathematical equations. ANN provides an alternative way to explore the underlying relationships in those ecosystem processes.1Four environmental variables including land surface temperature (LST), saturated vapor pressure (SVP), surface radiation (RA), and vegetation index (EVI) are used in this study to examine the relationships between ET and its formative environmental conditions. These four variables are far from complete in terms of factors affecting ET. However, they are the main variables controlling evapotranspiration process and are remote sensing available.The objective of this study is to apply artificial neural network and remote sensing techniques to explore the relationships between ET and its formative environmental factors. This relationship can then be used to map the distribution of ET over continental to global scales and provide information for other ecosystem modeling and management activities. 2. Work Performed2.1 Data Collection The data used to explore the relationships between ET and its formative environmental conditions in this study were collected in year 2001. Ground truth of ET came from AmeriFlux tower measurements. Table 1 is a list of 12 AmeriFlux tower sites used in this study.Table 1 AmeriFlux Towers InformationABBR. AmeriFlux Tower Site Name Latitude Longitude Elevation(meter)CA_1 Blodgett Forest 38.8953 -120.6328 1300~1500IL_1 Bondville 40.0061 -88.2919 300MA_1 Harvard Forest 42.5378 -72.1715 180-490IN_1 Morgan Monre State Forest 39.3213 -86.4134 275MT_1 Fort Peck 48.3079 -105.1005 634CO_1 Niwot Ridge Forest 40.0329 -105.5464 3050CT_1 Great Mountain Forest, Norfolk 41.9667 -73.2333 380~480OR_1 Metolious Research Natural Area – young 44.4372 -121.5668 1188CA_2 Tonzi Ranch 38.4312 -120.9660 177CA_3 Vaira Rnach, Ione 38.4067 -120.9507 129WI_1 Lost Creek 46.0827 -89.9792 >480WI_2 Willow Creek 45.8059 -90.0799 >5202Hourly or daily latent heat flux (LE) which is the energy equivalence of ET is measured at each of the twelve AmeriFlux towers and is public available at AmeriFlux website. The hourly or daily LE is then aggregated into 8-day average LE using Perl programs. There are atotal of forty-five 8-day periods from Julian day 1 to 360.Daily surface radiation (RA) comes from GEOS satellite (Geostationary Operational Environmental Satellite) and is also public available. The daily surface radiation at each of the twelve AmeriFlux tower sites is extracted


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UW-Madison ECE 539 - Estimate Evapotranspiration from Remote Sensing Data

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