Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network-Reference-Cited by-同舟云学术

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Mapping fine-spatial-resolution vegetation spring phenology from individual Landsat images using a convolutional neural network

Published:2023-05-03 Issue:9 Volume:44 Page:3059-3081
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Kun Xiao¹,Wei Wu¹^ORCID,Sun Ying¹,Wang Yidan¹,Xin Qinchuan¹

Affiliation:

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou, China

Funder

Natural Science Foundation of Guangdong Province

Western Talents

National Key R&D Program of China

Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2216846

Reference51 articles.

1. Circumpolar vegetation dynamics product for global change study

2. Identifying Leaf Phenology of Deciduous Broadleaf Forests from PhenoCam Images Using a Convolutional Neural Network Regression Method

3. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter

4. A Simple Method for Detecting Phenological Change From Time Series of Vegetation Index

5. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests

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