Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea-Reference-Cited by-同舟云学术

Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea

Published:2018-07-06 Issue:1 Volume:56 Page:43-67
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Lee Chang suk¹^ORCID,Sohn Eunha¹,Park Jun Dong¹,Jang Jae-Dong¹^ORCID

Affiliation:

1. National Meteorological Satellite Center, Korea Meteorological Administration, 61-18 Guam-gil, Gwanghyewon-myeon, Jincheon-gun, Chungcheongbuk-do 27803, Korea

Funder

National Meteorological Satellite Center

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2018.1489943

Reference50 articles.

1. Estimating soil moisture using remote sensing data: A machine learning approach

2. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

3. Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau

4. Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

5. Estimation of Soil Physical Properties Using Remote Sensing and Artificial Neural Network

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