Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations-Reference-Cited by-同舟云学术

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Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations

Published:2024-08 Issue: Volume:132 Page:104029
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:

Jang Eunna^ORCID,Han Daehyeon^ORCID,Im Jungho^ORCID,Sung Taejun^ORCID,Jun Kim Young

Funder

Ministry of Oceans and Fisheries

Korea Institute of Marine Science and Technology Promotion

Publisher

Elsevier BV

Reference52 articles.

1. Classification of polarimetric SAR images using compact convolutional neural networks;Ahishali;Gisci. Remote Sens.,2021

2. Bay of Bengal Sea surface salinity variability using a decade of improved SMOS re-processing;Akhil;Remote Sens. Environ.,2020

3. Analysis of SMOS sea surface salinity data using DINEOF;Alvera-Azcárate;Remote Sens. Environ.,2016

4. DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations;Barth;Geosci. Model Dev.,2020

5. DINCAE 2.0: Multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations;Barth;Geosci. Model Dev.,2022

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