A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods-Reference-Cited by-同舟云学术

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A normal form for synchronous land surface temperature and emissivity retrieval using deep learning coupled physical and statistical methods

Published:2024-03 Issue: Volume:127 Page:103704
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Wang Han^ORCID,Mao Kebiao^ORCID,Shi Jiancheng,Bateni Sayed M.,Altantuya Dorjsuren^ORCID,Sainbuyan Bayarsaikhan,Bao Yuhai

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Reference83 articles.

1. A split-window algorithm for estimating LST from Meteosat 9 data: Test and comparison with in situ data and MODIS LSTs;Atitar;IEEE Geosci. Remote Sens. Lett.,2009

2. Surface parameters from seviri observations through a kalman filter approach: Application and evaluation of the scheme to the southern Italy;Blasi;J. Weather Clim. Mediterr.,2016

3. Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing;Carter;Int. J. Appl. Earth Obs. Geoinf.,2019

4. Development of Himawari-8/Advanced Himawari Imager (AHI) land surface temperature retrieval algorithm;Choi;Remote Sens.,2018

5. Development of a land surface temperature retrieval algorithm from GK2A/AMI;Choi;Remote Sens.,2020

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