Evaluating FY3C-VIRR reconstructed land surface temperature in cloudy regions

Autor: Ye Qinyu, Gao Yanghua, Yang Shiqi, Zhu Hao, Wang Yongqian, Zhang Dejun, Shihao Tang, Sun Liang

Publikováno v: European Journal of Remote Sensing, Vol 54, Iss 1, Pp 266-280 (2021)

Missing values in land surface temperature (LST) data are often observed in the cloud-sheltered area, thereby seriously limiting the spatiotemporal continuity of LST. In this work, Remotely Sensed Daily Land Surface Temperature Reconstruction model(R

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00c0aa8cc6ea6ae333d39ed5d8f841f3
https://doaj.org/article/5bde343f82a6430eb4e1cda0d7091de5

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition

Autor: Zhiping Nie, Zifan Gui, Qiuwen Zhang, Huafei Que, Gui Zhang, Xike Zhang

Publikováno v: International Journal of Environmental Research and Public Health
International Journal of Environmental Research and Public Health; Volume 15; Issue 5; Pages: 1032
International Journal of Environmental Research and Public Health, Vol 15, Iss 5, p 1032 (2018)

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d972c15dd9c848de9c8ae8b26071836
https://pubmed.ncbi.nlm.nih.gov/29883381

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