Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm

•CNN-LSTM-ILE outperforms all the LE products used in integration method.•CNN-LSTM-ILE that combines information from LE products, EC and topography.•The spatial and temporal information of the forcing inputs were integrated. Accurate estimates of high-spatial-resolution global terrestrial latent he...

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Vydané v:	Agricultural and forest meteorology Ročník 349; s. 109962
Hlavní autori:	Guo, Xiaozheng, Yao, Yunjun, Tang, Qingxin, Liang, Shunlin, Shao, Changliang, Fisher, Joshua B., Chen, Jiquan, Jia, Kun, Zhang, Xiaotong, Shang, Ke, Yang, Junming, Yu, Ruiyang, Xie, Zijing, Liu, Lu, Ning, Jing, Zhang, Lilin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.04.2024
Predmet:	algorithms applied research CNN-LSTM eddy covariance forests High-spatial-resolution products hydrology Integration algorithm Landsat Latent heat flux memory meteorology regression analysis Integration algorithm High-spatial-resolution products CNN-LSTM Latent heat flux Landsat
ISSN:	0168-1923, 1873-2240
On-line prístup:	Získať plný text
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