@inproceedings{6981b5dc909b484aaf01028a2614813d,
title = "Lai Estimation of Paddy Rice Using Sentinel-2 Vegetation Indices",
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 T{\"u}rkiye, 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 (R2 = 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.",
keywords = "LAI, Sentinel-2, paddy rice, random forest",
author = "Saygin Abdikan and Dessislava Ganeva and Narin, \{Omer Gokberk\} and Petar Dimitrov and Aliihsan Sekertekin and Zlatomir Dimitrov and Caglar Bayik and Milen Chanev and Lachezar Filchev and Mustafa Ustuner and Esetlili, \{Mustafa Tolga\} and \{Balik Sanli\}, Fusun and Yusuf Kurucu",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 3rd International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2025 ; Conference date: 02-09-2025 Through 04-09-2025",
year = "2025",
doi = "10.1109/MIGARS67156.2025.11231787",
language = "English",
series = "2025 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2025",
address = "United States",
}