Lai Estimation of Paddy Rice Using Sentinel-2 Vegetation Indices
Bildiriler > Tam Metin Bildiri
Yazarlar: Saygin Abdikan, Dessislava Ganeva, Omer Gokberk Narin, Petar Dimitrov, Aliihsan Sekertekin, Zlatomir Dimitrov, Caglar Bayik, Milen Chanev, Lachezar Filchev, Mustafa Ustuner, Mustafa Tolga ESETLİLİ, Fusun Balik Sanli, Yusuf KURUCU
2025 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), , , Uluslararası, Tam Metin Bildiri, ss 1-4
DOI: 10.1109/migars67156.2025.11231787
Özet
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 Türkiye, 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.