Evapotranspiration in Korea estimated by application of a neural network to satellite images-Reference-Cited by-同舟云学术

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Evapotranspiration in Korea estimated by application of a neural network to satellite images

Published:2015-05 Issue:6 Volume:6 Page:429-438
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Yeom Jong-Min,Lee Chang-Suk,Park Soo-Jae,Ryu Jae-Hyun,Kim Jae-Jin,Kim Hyun-Cheol,Han Kyung-Soo

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

http://www.tandfonline.com/doi/pdf/10.1080/2150704X.2015.1041169

Reference33 articles.

1. Evapotranspiration information reporting: I. Factors governing measurement accuracy

2. Neural network modelling of variable hysteretic inductors

3. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach

4. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space

5. Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

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1. f-MOPSO/Div: an improved extreme-point-based multi-objective PSO algorithm applied to a socio-economic-environmental conjunctive water use problem;Environmental Monitoring and Assessment;2020-11-19

2. Retrieval of Daily Reference Evapotranspiration for Croplands in South Korea Using Machine Learning with Satellite Images and Numerical Weather Prediction Data;Remote Sensing;2020-11-06

3. Artificial neural networks model based on remote sensing to retrieve evapotranspiration over the Brazilian Pampa;Journal of Applied Remote Sensing;2020-09-15

4. Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea;GIScience & Remote Sensing;2018-07-06

5. Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements;International Journal of Remote Sensing;2017-09-13

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