Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data-Reference-Cited by-同舟云学术

Retrieving fPAR of maize canopy using artificial neural networks with airborne LiDAR and hyperspectral data

Published:2020-09-24 Issue:11 Volume:11 Page:1002-1011
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Shi Juncheng¹²³,Wang Cheng⁴,Xi Xiaohuan⁴,Yang Xuebo⁴,Wang Jinliang¹²³,Ding Xue¹²³

Affiliation:

1. School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming, Yunnan, China

2. Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Yunnan Normal University, Kunming, Yunnan, China

3. Center for Geospatial Information Engineering and Technology of Yunnan Province, Yunnan Normal University, Kunming, Yunnan, China

4. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

the Erasmus+ Capacity Building in Higher Education of the Education, Audiovisual and Culture Executive Agency (EACEA) for the ‘Innovation on Remote Sensing Education and Learning’

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2020.1807647

Reference18 articles.

1. Mapping urban forest leaf area index with airborne lidar using penetration metrics and allometry

2. Direct retrieval of canopy gap probability using airborne waveform lidar

3. A lidar-based hierarchical approach for assessing MODIS fPAR

4. Measuring leaf area index of plant canopies with branch architecture

5. Groundwater level forecasting using artificial neural networks

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3. Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data;International Journal of Remote Sensing;2022-07-03