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

doi:10.1007/s11707-012-0346-7

Front Earth Sci

2013, Vol. 7

Issue (1) : 103-111 https://doi.org/10.1007/s11707-012-0346-7

RESEARCH ARTICLE

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

Zhuoqi CHEN¹, Runhe SHI²(email.png), Shupeng Zhang¹

1. 1. College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; 2. 2. Key Laboratory of Geographic Information Science, East China Normal University, Shanghai 200062, China

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Abstract

A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring 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 (R²) 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.

Keywords AmeriFlux artificial neural network (ANN) evapotranspiration (ET) remote sensing

Corresponding Author(s): SHI Runhe,Email:shirunhe@gmail.com

Issue Date: 05 March 2013

Cite this article:

Zhuoqi CHEN,Runhe SHI,Shupeng Zhang. An artificial neural network approach to estimate evapotranspiration from remote sensing and AmeriFlux data[J]. Front Earth Sci, 2013, 7(1): 103-111.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0346-7
https://academic.hep.com.cn/fesci/EN/Y2013/V7/I1/103

Tab.1 Description of AmeriFlux sites used in this study

Fig.1 The structure of the ANN model used in our study. ‘tansig’ represents tangent sigmoid transfer function; ‘purelin’ represents linear transfer function

Fig.2 Scatterplot of overall ANN-estimated ET and measured ET at five validation sites. The solid line represents a 1:1 relationship and the dashed line represents a linear fit

Fig.3 Scatterplot of overall residuals (measured ET minus estimated ET) at five validation sites

Tab.2 Validation results of the ANN model with inputs of , and

Fig.4 Time series of ANN-estimated ET and measured ET at each validation site. The solid line represents ANN-estimated ET and the dots represent the measured ET

Tab.3 Validation results of an ET estimation algorithm which based on a simplified P–M model and driven by MODIS data and Global Modeling and Assimilation Office (GAMO) meteorological reanalysis data (Mu et al., 2011)

Tab.4 Validation results of the ANN model with inputs of EVI, LST and R.

Tab.5 Validation results of the ANN model with inputs of NDVI, LST and

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