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Title: The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation

Abstract

An accurate estimation of spatially and temporally continuous global terrestrial evapotranspiration (ET) is essential in the assessment of surface energy, water and carbon cycles. The Global LAnd Surface Satellite (GLASS) ET product Version 4.0 (v4.0) based on the Bayesian model averaging (BMA) method was generated to estimate global terrestrial ET. However, certain uncertainty for the GLASS ET product v4.0 limits its application. In this study, we introduced the deep neural networks (DNN) merging framework to improve terrestrial ET estimation for GLASS ET product Version 5.0 (v5.0) generation by integrating five satellite-derived ET products [Moderate Resolution Imaging Spectroradiometer (MODIS) ET product (MOD16), Shuttleworth–Wallace dual-source ET product (SW), Priestley–Taylor-based ET product (PT-JPL), modified satellite-based Priestley–Taylor ET product (MS-PT) and simple hybrid ET product (SIM)]. We compared the performance of DNN method against other merging methods, including GLASS ET algorithm v4.0 (BMA), the gradient boosting regression tree (GBRT) method and the random forest (RF) method, based on 195 global eddy covariance (EC) flux towers covering observations from 2000 through 2015. Validations indicated that the DNN had the highest accuracy among four merging methods across different land cover types, yielding the highest average determination coefficients (R2, 0.62), root-mean-squared-error (RMSE, 24.1 W/m2) and Kling–Gupta efficiencymore » (KGE, 0.77) with a of 99% confidence interval. Compared with GLASS ET algorithm v4.0, the DNN improved on the R2 by approximately 7% (p < 0.01) and the KGE by 10%. Based on the DNN, we then generated 8-day GLASS ET product v5.0 globally with a 1 km spatial resolution from 2001 to 2015 driven by GLASS vegetation and surface net radiation (Rn) datasets and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) datasets. Finally, this global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.« less

Authors:
 [1];  [1];  [1];  [2];  [3];  [4];  [1];  [1];  [1];  [1];  [1];  [1];  [1];  [5]
+ Show Author Affiliations
  1. Beijing Normal University, Beijing (China)
  2. Univ. of Maryland, College Park, MD (United States)
  3. Chapman Univ., Orange, CA (United States)
  4. Michigan State Univ., East Lansing, MI (United States)
  5. Univ. of Twente, Enschede (Netherlands)
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1977307
Grant/Contract Number:  
FG02-04ER63911; FG02-04ER63917
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Hydrology
Additional Journal Information:
Journal Volume: 610; Journal Issue: C; Journal ID: ISSN 0022-1694
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Engineering; Geology; Water resources; GLASS; Evapotranspiration; Deep neural networks; Machine learning; Bayesian model averaging

Citation Formats

Xie, Zijing, Yao, Yunjun, Zhang, Xiaotong, Liang, Shunlin, Fisher, Joshua B., Chen, Jiquan, Jia, Kun, Shang, Ke, Yang, Junming, Yu, Ruiyang, Guo, Xiaozheng, Liu, Lu, Ning, Jing, and Zhang, Lilin. The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation. United States: N. p., 2022. Web. doi:10.1016/j.jhydrol.2022.127990.
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Xie, Zijing, Yao, Yunjun, Zhang, Xiaotong, Liang, Shunlin, Fisher, Joshua B., Chen, Jiquan, Jia, Kun, Shang, Ke, Yang, Junming, Yu, Ruiyang, Guo, Xiaozheng, Liu, Lu, Ning, Jing, & Zhang, Lilin. The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation. United States. https://doi.org/10.1016/j.jhydrol.2022.127990
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Xie, Zijing, Yao, Yunjun, Zhang, Xiaotong, Liang, Shunlin, Fisher, Joshua B., Chen, Jiquan, Jia, Kun, Shang, Ke, Yang, Junming, Yu, Ruiyang, Guo, Xiaozheng, Liu, Lu, Ning, Jing, and Zhang, Lilin. Sat . "The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation". United States. https://doi.org/10.1016/j.jhydrol.2022.127990. https://www.osti.gov/servlets/purl/1977307.
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@article{osti_1977307,
title = {The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation},
author = {Xie, Zijing and Yao, Yunjun and Zhang, Xiaotong and Liang, Shunlin and Fisher, Joshua B. and Chen, Jiquan and Jia, Kun and Shang, Ke and Yang, Junming and Yu, Ruiyang and Guo, Xiaozheng and Liu, Lu and Ning, Jing and Zhang, Lilin},
abstractNote = {An accurate estimation of spatially and temporally continuous global terrestrial evapotranspiration (ET) is essential in the assessment of surface energy, water and carbon cycles. The Global LAnd Surface Satellite (GLASS) ET product Version 4.0 (v4.0) based on the Bayesian model averaging (BMA) method was generated to estimate global terrestrial ET. However, certain uncertainty for the GLASS ET product v4.0 limits its application. In this study, we introduced the deep neural networks (DNN) merging framework to improve terrestrial ET estimation for GLASS ET product Version 5.0 (v5.0) generation by integrating five satellite-derived ET products [Moderate Resolution Imaging Spectroradiometer (MODIS) ET product (MOD16), Shuttleworth–Wallace dual-source ET product (SW), Priestley–Taylor-based ET product (PT-JPL), modified satellite-based Priestley–Taylor ET product (MS-PT) and simple hybrid ET product (SIM)]. We compared the performance of DNN method against other merging methods, including GLASS ET algorithm v4.0 (BMA), the gradient boosting regression tree (GBRT) method and the random forest (RF) method, based on 195 global eddy covariance (EC) flux towers covering observations from 2000 through 2015. Validations indicated that the DNN had the highest accuracy among four merging methods across different land cover types, yielding the highest average determination coefficients (R2, 0.62), root-mean-squared-error (RMSE, 24.1 W/m2) and Kling–Gupta efficiency (KGE, 0.77) with a of 99% confidence interval. Compared with GLASS ET algorithm v4.0, the DNN improved on the R2 by approximately 7% (p < 0.01) and the KGE by 10%. Based on the DNN, we then generated 8-day GLASS ET product v5.0 globally with a 1 km spatial resolution from 2001 to 2015 driven by GLASS vegetation and surface net radiation (Rn) datasets and Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) datasets. Finally, this global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.},
doi = {10.1016/j.jhydrol.2022.127990},
journal = {Journal of Hydrology},
number = C,
volume = 610,
place = {United States},
year = {Sat May 28 00:00:00 EDT 2022},
month = {Sat May 28 00:00:00 EDT 2022}
}
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