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Article: DNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information

TitleDNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information
Authors
KeywordsAncillary information
Deep neural networks
DNN-MET
Evapotranspiration
Merging method
Issue Date2021
Citation
Agricultural and Forest Meteorology, 2021, v. 308-309, article no. 108582 How to Cite?
AbstractAccurate estimates of the spatiotemporal distribution of evapotranspiration (ET) are essential for understanding terrestrial energy, carbon and water cycles. Station-based observations are limited for their spatial coverage whereas satellite-derived ET products exhibit large discrepancies and uncertainties. Here we presented a Deep Neural Networks based Merging ET (DNN-MET) framework that combines information from satellite-derived ET products, eddy covariance (EC) observations and ancillary surface properties to improve the representation of the spatiotemporal distribution of ET, especially in data-sparse regions. DNN-MET was implemented over the Heihe River Basin (HRB) from 2008 to 2015, and the performance of DNN-MET and eight input state-of-the-art satellite-derived ET products (i.e., MOD16, ET-SEMI, ET-JPL, ET-MS, ET-HF, GLEAM, ETMonitor and EB-ET) was evaluated against observations from 19 EC flux tower sites. The results showed that DNN-MET improved ET estimates over HRB, and decreased the RMSE by 0.13 to 1.02 mm/day (14%-56%) when compared with eight products. DNN-MET also yielded superior performance compared to the products derived by other merging methods (i.e., Random Forest, Bayesian model averaging and a simple averaging method). When DNN-MET was validated for data-scarce regions, its performance remained better even when the training samples were decreased to 20% of the available EC sites. An innovation of our approach is by building a multivariate merging model with ancillary surface properties, DNN-MET incorporated geographical proximity effects and spatial autocorrelations into merging procedure, which can be used as a “spatial knowledge engine” to improve ET predictions. The approach can be readily and effectively applied elsewhere to improve the spatiotemporal representation of various hydrometeorological variables.
Persistent Identifierhttp://hdl.handle.net/10722/323132
ISSN
2023 Impact Factor: 5.6
2023 SCImago Journal Rankings: 1.677
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShang, Ke-
dc.contributor.authorYao, Yunjun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorZhang, Yuhu-
dc.contributor.authorFisher, Joshua B.-
dc.contributor.authorChen, Jiquan-
dc.contributor.authorLiu, Shaomin-
dc.contributor.authorXu, Ziwei-
dc.contributor.authorZhang, Yuan-
dc.contributor.authorJia, Kun-
dc.contributor.authorZhang, Xiaotong-
dc.contributor.authorYang, Junming-
dc.contributor.authorBei, Xiangyi-
dc.contributor.authorGuo, Xiaozheng-
dc.contributor.authorYu, Ruiyang-
dc.contributor.authorXie, Zijing-
dc.contributor.authorZhang, Lilin-
dc.date.accessioned2022-11-18T11:54:57Z-
dc.date.available2022-11-18T11:54:57Z-
dc.date.issued2021-
dc.identifier.citationAgricultural and Forest Meteorology, 2021, v. 308-309, article no. 108582-
dc.identifier.issn0168-1923-
dc.identifier.urihttp://hdl.handle.net/10722/323132-
dc.description.abstractAccurate estimates of the spatiotemporal distribution of evapotranspiration (ET) are essential for understanding terrestrial energy, carbon and water cycles. Station-based observations are limited for their spatial coverage whereas satellite-derived ET products exhibit large discrepancies and uncertainties. Here we presented a Deep Neural Networks based Merging ET (DNN-MET) framework that combines information from satellite-derived ET products, eddy covariance (EC) observations and ancillary surface properties to improve the representation of the spatiotemporal distribution of ET, especially in data-sparse regions. DNN-MET was implemented over the Heihe River Basin (HRB) from 2008 to 2015, and the performance of DNN-MET and eight input state-of-the-art satellite-derived ET products (i.e., MOD16, ET-SEMI, ET-JPL, ET-MS, ET-HF, GLEAM, ETMonitor and EB-ET) was evaluated against observations from 19 EC flux tower sites. The results showed that DNN-MET improved ET estimates over HRB, and decreased the RMSE by 0.13 to 1.02 mm/day (14%-56%) when compared with eight products. DNN-MET also yielded superior performance compared to the products derived by other merging methods (i.e., Random Forest, Bayesian model averaging and a simple averaging method). When DNN-MET was validated for data-scarce regions, its performance remained better even when the training samples were decreased to 20% of the available EC sites. An innovation of our approach is by building a multivariate merging model with ancillary surface properties, DNN-MET incorporated geographical proximity effects and spatial autocorrelations into merging procedure, which can be used as a “spatial knowledge engine” to improve ET predictions. The approach can be readily and effectively applied elsewhere to improve the spatiotemporal representation of various hydrometeorological variables.-
dc.languageeng-
dc.relation.ispartofAgricultural and Forest Meteorology-
dc.subjectAncillary information-
dc.subjectDeep neural networks-
dc.subjectDNN-MET-
dc.subjectEvapotranspiration-
dc.subjectMerging method-
dc.titleDNN-MET: A deep neural networks method to integrate satellite-derived evapotranspiration products, eddy covariance observations and ancillary information-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.agrformet.2021.108582-
dc.identifier.scopuseid_2-s2.0-85112303893-
dc.identifier.volume308-309-
dc.identifier.spagearticle no. 108582-
dc.identifier.epagearticle no. 108582-
dc.identifier.isiWOS:000692679900043-

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