A Method for Retrieving Maize Fractional Vegetation Cover by Combining 3-D Radiative Transfer Model and Transfer Learning-Reference-Cited by-同舟云学术

A Method for Retrieving Maize Fractional Vegetation Cover by Combining 3-D Radiative Transfer Model and Transfer Learning

Published:2024 Issue: Volume:17 Page:15671-15684
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wu Zhuo¹^ORCID,Zheng Xingming²^ORCID,Ding Yanling¹^ORCID,Tao Zui³^ORCID,Sun Yuan³,Li Bingze²,Chen Xinmeng¹,Zhao Jianing¹,Liu Yirui¹,Chen Xinyu²,Li Xinbiao²

Affiliation:

1. School of Geographical Sciences, Northeast Normal University, Changchun, China

2. State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China

3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jilin Province

National Civil Space Infrastructure Land Observation Satellites

Fundamental Research Funds for the Central Universities

Strategic Priority Research Program of the Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10648810.pdf?arnumber=10648810

Reference74 articles.

1. Using NDVI for the assessment of canopy cover in agricultural crops within modelling research

2. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States

3. Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

4. Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images

5. A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles