Highlights Synergistic use of drone, satellite, and machine learning technologies improves LAI estimation. Robust validation and advanced performance metrics confirm model accuracy and precision. Utilizing Vegetation Indices to enhance the precision of LAI quantification.

    Abstract In this study, we conducted a comprehensive assessment of Leaf Area Index (LAI) estimation using three distinct sources of satellite data: Sentinel-2 imagery, drone imagery (UAVs), and Mohammed VI satellite data. The main objective was to identify the most reliable and precise dataset for predicting LAI, with a focus on evaluating the performance of Random Forest models. For Sentinel-2 imagery, our Random Forest model achieves a robust R-squared (R2) value of 0.89, signifying a strong alignment between predicted and measured LAI values. The associated root-mean-square error (RMSE) is 0.4, indicating high predictive accuracy. In the context of UAVs, our Random Forest model excels, exhibiting an impressive R2 value of 0.93, highlighting a substantial correlation between predicted and measured LAI. The RMSE for drone imagery stands at 0.37, showcasing exceptional predictive accuracy. Finally, the Random Forest model trained on Mohammed VI satellite data yields an R2 value of 0.92, underlining its strong fit with measured LAI values. The RMSE for Mohammed VI imagery is 0.39, further underscoring the model's exceptional predictive accuracy. This comparative analysis underscores the importance of selecting the most suitable satellite data source for LAI estimation in Argania spinosa. UAV imagery emerges as the most accurate choice, closely followed by Mohammed VI Satellite and Sentinel-2 imagery. These findings offer valuable insights for effective monitoring of Argania spinosa and advancing sustainable land management practices in rural ecosystems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:

    Erschienen in:

    Advances in Space Research ; 73 , 10 ; 4976-4987


    Erscheinungsdatum :

    2024-02-17


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Flow Estimation of Freeway Section Based on Multi-Source Data

    Huang, Shuai / Sun, Dihua / Zhao, Min et al. | TIBKAT | 2021


    Flow Estimation of Freeway Section Based on Multi-Source Data

    Huang, Shuai / Sun, Dihua / Zhao, Min et al. | ASCE | 2021


    Multi-models machine learning methods for traffic flow estimation from Floating Car Data

    Li, Jinjian / Boonaert, Jacques / Doniec, Arnaud et al. | Elsevier | 2021


    Urban traffic speed estimation method based on multi-source crowd sensing data

    HE SHIBO / ZHANG YINGQIAN / LI CHAO et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation

    Li, S. / Fu, Q. / Gu, L. et al. | British Library Conference Proceedings | 2001