LAI ESTIMATION OF PADDY RICE USING SENTINEL-2 VEGETATION INDICES

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Abdikan, S Ganeva, D Narin, OG Dimitrov, P Sekertekin, A Dimitrov, Z Bayik, C Chaney, M Filchev, L Ustuner, M Esetlili, MT Sanli, FB Kurucu, Y

2025 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS , Volume/Issue: ,Pages: 65 - 68 ,2025

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Abstract

The growing demand for rice products highlights the importance of maximizing yield and closely monitoring crop development. In this context, spectral vegetation indices (VIs) play a key role in characterizing plant growth. This study evaluated the performance of 20 vegetation indices derived from multi-temporal Sentinel-2 imagery for estimating the Leaf Area Index (LAI) in rice crops. In-situ LAI samples were collected at various phenological stages from selected regions in Bulgaria and Turkiye, in coordination with field campaigns conducted in parallel with Sentinel-2 image acquisition. Among the indices, GOSAVI showed the highest Pearson correlation with LAI (r = 0.74). The Random Forest algorithm was employed to estimate LAI from each index, with SAVI yielding the highest accuracy (R-2 = 0.59, RMSE = 1.4). The findings indicate that it can effectively support practical and efficient LAI estimation with Sentinel-2 data for data collected from different regions and help improve rice crop monitoring studies.

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    Uluslararası

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    Hakemli

  • Index info

    WOS.ISTP

  • Language

    English

  • Article Type

    None

  • Keywords

    paddy rice Sentinel-2 LAI random forest