TY - JOUR
T1 - The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0
T2 - Algorithm development and preliminary validation
AU - Xie, Zijing
AU - Yao, Yunjun
AU - Zhang, Xiaotong
AU - Liang, Shunlin
AU - Fisher, Joshua B.
AU - Chen, Jiquan
AU - Jia, Kun
AU - Shang, Ke
AU - Yang, Junming
AU - Yu, Ruiyang
AU - Guo, Xiaozheng
AU - Liu, Lu
AU - Ning, Jing
AU - Zhang, Lilin
N1 - Funding Information:
This work was supported by the Natural Science Fund of China (No. 42171310 and No. 42192581). Acknowledgement for the data support from “National Earth System Science Data Center, National Science & Technology Infrastructure of China. ( http://www.geodata.cn) ”. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont,ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California – Berkeley and the University of Virginia. Other ground-measured data were obtained from the GAME AAN ( http://aan.suiri.tsukuba.ac.jp/ ), the Coordinated Enhanced Observation Project (CEOP) in arid and semi-arid regions of northern China ( http://observation.tea.ac.cn/ ), and the water experiments of Environmental and Ecological Science Data Center for West China (http://westdc.westgis.ac.cn/water).
Funding Information:
This work was supported by the Natural Science Fund of China (No. 42171310 and No. 42192581). Acknowledgement for the data support from “National Earth System Science Data Center, National Science & Technology Infrastructure of China. ( http://www.geodata.cn)”. This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont,ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Université Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California – Berkeley and the University of Virginia. Other ground-measured data were obtained from the GAME AAN ( http://aan.suiri.tsukuba.ac.jp/), the Coordinated Enhanced Observation Project (CEOP) in arid and semi-arid regions of northern China ( http://observation.tea.ac.cn/), and the water experiments of Environmental and Ecological Science Data Center for West China (http://westdc.westgis.ac.cn/water).
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/7
Y1 - 2022/7
N2 - 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. This global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.
AB - 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. This global terrestrial ET product provides a valuable dataset for monitoring regional and global water resources and environmental changes.
KW - Bayesian model averaging
KW - Deep neural networks
KW - Evapotranspiration
KW - GLASS
KW - Machine learning
KW - 2023 OA procedure
KW - ITC-ISI-JOURNAL-ARTICLE
U2 - 10.1016/j.jhydrol.2022.127990
DO - 10.1016/j.jhydrol.2022.127990
M3 - Article
AN - SCOPUS:85131130324
SN - 0022-1694
VL - 610
JO - Journal of hydrology
JF - Journal of hydrology
M1 - 127990
ER -