Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin-Reference-Cited by-同舟云学术

Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin

Published:2023-11 Issue: Volume:155 Page:111088
ISSN:1470-160X
Container-title:Ecological Indicators
language:en
Short-container-title:Ecological Indicators

Author:

Wang Shidong,Cui Dunyue,Wang Lu,Peng JinYan

Funder

U.S. Geological Survey

National Natural Science Foundation of China

National Aeronautics and Space Administration

Publisher

Elsevier BV

Subject

Ecology,Ecology, Evolution, Behavior and Systematics,General Decision Sciences

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4. Multi-source remotely sensed data fusion for improving land cover classification;Chen;ISPRS J. Photogramm. Remote Sens.,2017

5. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015;Chu;Sci. Total Environ.,2019

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