Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach-Reference-Cited by-同舟云学术

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Canopy Scale High-Resolution Forest Biophysical Parameter (LAI, fAPAR, and fCover) Retrieval Through Machine Learning and Cloud Computation Approach

Published:2023-01-27 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)
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Author:

Bandopadhyay Subhajit¹,Das Barnali²,Sánchez Alexander Cotrina³,Banerjee Sankar Prasad⁴,Banerjee Bikram P.⁵,Ghosh Subhasis⁶

Affiliation:

1. University of Southampton,Department of Geography and Environmental Sciences,Southampton,United Kingdom

2. Centre for Atmospheric Sciences,IITD,Delhi,India

3. Agri-Food and Forest Systems, Università degli Studi della,Department for Innovation in Biological,Tuscia,Italy

4. iMerit Technology Services Pvt. Ltd.,Ranchi,India

5. Agriculture Victoria,Victoria,Australia

6. Auburn University,Department of Geosciences,Auburn,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10064385/10064489/10064558.pdf?arnumber=10064558

Reference8 articles.

1. Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

2. Estimation of above-ground biomass in tropical afro-montane forest using Sentinel-2 derived indices

3. A 250 m resolution global leaf area index product derived from MODIS surface reflectance data

4. LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest;Tropical Ecology;2024-02-13

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