Name
Title | ||
|---|---|---|
Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine |
Abstract | ||
|---|---|---|
Application of remote sensing data to extrapolate evapotranspiration (ET) measured at eddy covariance flux towers is a potentially powerful method to estimate continental-scale ET. In support of this concept, we used meteorological and flux data from the AmeriFlux network and an inductive machine learning technique called support vector machine (SVM) to develop a predictive ET model. The model was then applied to the conterminous U.S. In this process, we first trained the SVM to predict 2000-2002 ET measurements from 25 AmeriFlux sites using three remotely sensed variables [land surface temperature, enhanced vegetation index (EVI), and land cover] and one ground-measured variable (surface shortwave radiation). Second, we evaluated the model performance by predicting ET for 19 flux sites in 2003. In this independent evaluation, the SVM predicted ET with a root-mean-square error (rmse) of 0.62 mm/day (approximately 23% of the mean observed values) and an R2 of 0.75. The rmse from SVM was significantly smaller than that from neural network and multiple-regression approaches in a cross-validation experiment. Among the explanatory variables, EVI was the most important factor. Indeed, removing this variable induced an rmse increase from 0.54 to 0.77 mm/day. Third, with forcings from remote sensing data alone, we used the SVM model to predict the spatial and temporal distributions of ET for the conterminous U.S. for 2004. The SVM model captured the spatial and temporal variations of ET at a continental scale |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/TGRS.2006.876297 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
ad 2004,eddy covariance flux towers,atmospheric humidity,remote sensing,transpiration,land cover,support vector machines (svms),ameriflux data,meteorological data,continental-scale evapotranspiration prediction,ad 2000 to 2002,ameriflux,moderate resolution imaging spectroradiometer (modis),remote sensing data,svm,hydrological techniques,extrapolation,evapotranspiration (et),evaporation,moderate resolution imaging spectroradiometer modis,index terms—ameriflux,modis data,conterminous united states,support vector machine,data combination,enhanced vegetation index,surface shortwave radiation,land surface temperature,support vector machines svms.,moderate resolution imaging spectroradiometer,inductive machine learning technique,vegetation,support vector machines,evapotranspiration et,atmospheric radiation,multiple regression,neural network,neural networks,prediction,eddy covariance,radiation,covariance,cross validation,power method,evapotranspiration,regression,root mean square error | Meteorology,Moderate-resolution imaging spectroradiometer,Remote sensing,Support vector machine,Normalized Difference Vegetation Index,Eddy covariance,Enhanced vegetation index,Evapotranspiration,Land cover,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
44 | 11 | 0196-2892 |
Citations | PageRank | References |
25 | 2.76 | 5 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feihua Yang | 1 | 25 | 2.76 |
| Michael A. White | 2 | 69 | 8.37 |
| Andrew R. Michaelis | 3 | 50 | 8.13 |
| Kazuhito Ichii | 4 | 41 | 5.53 |
| Hirofumi Hashimoto | 5 | 50 | 9.08 |
| Petr Votava | 6 | 66 | 10.03 |
| A-Xing Zhu | 7 | 228 | 37.22 |
| Ramakrishna R. Nemani | 8 | 455 | 91.96 |