Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea-Reference-Cited by-同舟云学术

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Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Published:2022-01 Issue: Volume:802 Page:149726
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Jeong Seungtaek,Ko Jonghan,Yeom Jong-Min

Funder

Korea Aerospace Research Institute

National Research Foundation of Korea

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference83 articles.

1. Real-time relative permeability prediction using deep learning;Arigbe;J. Petrol. Explor. Prod. Technol.,2019

2. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics;Bolton;Agric. Forest Meteorol.,2013

3. Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series;Bose;IEEE Trans. Geosci. Remote Sensing,2016

4. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches;Cai;Agric. Forest Meteorol.,2019

5. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches;Cao;Agric. Forest Meteorol.,2021

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