Accurate mapping of rapeseed fields in the initial flowering stage using Sentinel-2 satellite images and convolutional neural networks-Reference-Cited by-同舟云学术

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Accurate mapping of rapeseed fields in the initial flowering stage using Sentinel-2 satellite images and convolutional neural networks

Published:2024-05 Issue: Volume:162 Page:112027
ISSN:1470-160X
Container-title:Ecological Indicators
language:en
Short-container-title:Ecological Indicators

Author:

Sun Yifei,Hao Zhenbang^ORCID,Chang Hongcai,Yang Jialin,Ding Guiling,Guo Zhanbao,He Xi,Huang Jiaxing

Funder

Chinese Academy of Agricultural Sciences

Ministry of Agriculture and Rural Affairs of the People's Republic of China

National Key Research and Development Program of China

Agriculture Research System of China

Agricultural Science and Technology Innovation Program

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Reference74 articles.

1. Mapping of the invasive species hakea sericea using Unmanned Aerial Vehicle (UAV) and WorldView-2 imagery and an object-oriented approach [J];Alvarez-Taboada;Remote Sens. (Basel),2017

2. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series [J];Andrimont;Remote Sens. Environ.,2020

3. A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data [J];Arata;Econ. Model.,2020

4. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis [J];Belgiu;Remote Sens. Environ.,2018

5. Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images [J];Cao;Ecol. Ind.,2023

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