An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data  

作　　者：Zhuoqi CHEN Runhe SHI Shupeng Zhang 

机构地区：[1]College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China [2]Key Laboratory of Geographic Information Science, East China Normal University, Shanghai 200062, China

出　　处：《Frontiers of Earth Science》2013年第1期103-111,共9页地球科学前沿（英文版）

摘　　要：A simple and accurate method to estimate evapotranspiration （ET） is essential for dynamic monitor- ing of the Earth system at a large scale. In this paper, we developed an artificial neural network （ANN） model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data （normalized difference vegetation index, land surface temperature and surface net radiation） for the period 2002-2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination （RE） value for comparison between estimated and measured ET was 0.77, the root-mean- square error was 0.62 mm/d, and the mean residual was - 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.A simple and accurate method to estimate evapotranspiration （ET） is essential for dynamic monitor- ing of the Earth system at a large scale. In this paper, we developed an artificial neural network （ANN） model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data （normalized difference vegetation index, land surface temperature and surface net radiation） for the period 2002-2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination （RE） value for comparison between estimated and measured ET was 0.77, the root-mean- square error was 0.62 mm/d, and the mean residual was - 0.28. The simple model developed in this paper captured the seasonal and interannual variation features of ET on the whole. However, the accuracy of estimated ET depended on the vegetation types, among which estimated ET showed the best result for deciduous broadleaf forest compared to the other four vegetation types.

关 键 词：AMERIFLUX artificial neural network （ANN） evapotranspiration （ET） remote sensing 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP79[自动化与计算机技术—控制科学与工程]