气候变化领域集成服务门户(CCPortal): Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing

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DOI	10.1016/j.jag.2019.01.020
	Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing
	Carter C.; Liang S.
发表日期	2019
ISSN	15698432
起始页码	86
结束页码	92
卷号	78
英文摘要	Remote sensing retrieval of evapotranspiration (ET), or surface latent heat exchange (LE), is of great utility for many applications. Machine learning (ML) methods have been extensively used in many disciplines, but so far little work has been performed systematically comparing ML methods for ET retrieval. This paper provides an evaluation of ten ML methods for estimating daily ET based on daily Global LAnd Surface Satellite (GLASS) radiation data and high-level Moderate-Resolution Imaging Spectroradiometer (MODIS) data products and ground measured ET data from 184 flux tower sites. Measurements of accuracy (RMSE, R 2 , and bias) and run time were made for each of ten ML methods with a smaller training data set (n = 7910 data points) and a larger training data set (n= 69,752 data points). Inclusion of more input variables improved algorithm performance but had little effect on run time. The best results were obtained with the larger training data set using the bootstrap aggregation (bagging) regression tree (validation RMSE = 19.91 W/m 2 ) and three hidden layer neural network (validation RMSE = 20.94 W/m 2 ), although the less computationally demanding random kernel (RKS) algorithm also produced good results (validation RMSE = 22.22 W/m 2 ). Comparison of results from sites with different ecosystem types showed the best results for evergreen, shrub, and grassland sites, and the weakest results for wetland sites. Generally, performance was not improved by training with data from only the same ecosystem type. © 2019
英文关键词	Albedo; Bootstrap aggregation tree; Computational efficiency; Evapotranspiration; Flux tower; FPAR; GLASS; Latent heat exchange; Leaf area index; Machine learning; MODIS; Nadir adjusted reflectance; Neural network; Random kernel; Regression tree; Regularized linear regression; Remote sensing; Surface energy balance; Surface radiation; Vegetation index
语种	英语
scopus关键词	bootstrapping; efficiency measurement; estimation method; evapotranspiration; latent heat flux; leaf area index; machine learning; MODIS; numerical model; remote sensing; satellite data; terrestrial environment
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156494
作者单位	Department of Geographical Sciences, University of Maryland, College Park, MD, United States
推荐引用方式 GB/T 7714	Carter C.,Liang S.. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing[J],2019,78.
APA	Carter C.,&Liang S..(2019).Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing.International Journal of Applied Earth Observation and Geoinformation,78.
MLA	Carter C.,et al."Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing".International Journal of Applied Earth Observation and Geoinformation 78(2019).