Comparative analysis of multi-source data for machine learning-based LAI estimation in Argania spinosa-Reference-Cited by-同舟云学术

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Comparative analysis of multi-source data for machine learning-based LAI estimation in Argania spinosa

Published:2024-05 Issue:10 Volume:73 Page:4976-4987
ISSN:0273-1177
Container-title:Advances in Space Research
language:en
Short-container-title:Advances in Space Research

Author:

Mouafik Mohamed^ORCID,Fouad Mounir,Audet Felix Antoine,El Aboudi Ahmed

Publisher

Elsevier BV

Reference65 articles.

1. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1;Asrar;Agron. J.,1984

2. Potentials and limits of vegetation indices for LAI and APAR assessment;Baret;Remote Sens. Environ.,1991

3. Validation of the LAI biophysical product derived from Sentinel-2 and Proba-V images for winter wheat in western Poland;Bochenek;Geoinformation Issues,2017

4. Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring;Bsaibes;Remote Sens. Environ.,2009

Cited by 3 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. Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review;Geographies;2024-07-26

2. Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing;Agriculture;2024-07-18

3. Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data;AgriEngineering;2024-07-17

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