@article{Yin2026, 
author = {Sixian Yin and Taixia Wu and Shudong Wang and Ran Chen and Yingying Yang and Hongzhao Tang},
title = {Development of the FI-R model, a novel remote sensing method for fine-scale extraction of vegetation, using rapeseed as an example},
year = {2026},
journal = {Journal of Integrative Agriculture (JIA)},
volume = {25},
number = {3},
pages = {1223-1242},
keywords = {flowering rapeseed, multispectral imagery, precise classification, spectral indices, FI-R},
url = {https://www.sciopen.com/article/10.1016/j.jia.2025.05.006},
doi = {10.1016/j.jia.2025.05.006},
abstract = {The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses. Accurately extracting the distributions of different crop species is of critical importance for improving agricultural production efficiency and ensuring food security. Traditional fine-scale vegetation extraction methods often face significant challenges due to the presence of spectrally similar features and the substantial influence of background interference, which limit their applicability across large areas. As a key phenological stage of angiosperms, flowering is characterized by distinctive flowering times, floral morphology, and canopy spectral signatures, so it is an effective pathway for fine-scale vegetation extraction using remote sensing. Using rapeseed as an example, this study developed a spectral index model for precise flowering vegetation extraction (FI-R) based on Landsat OLI imagery. The model integrates a yellowness index (Blue, Green) and a peak index (Red, Nir and SWIR1) while leveraging the NDVI to mitigate background interference from spectrally similar objects. This approach successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions. The proposed method was tested in five rapeseed cultivation regions worldwide with diverse backgrounds. Validation datasets were generated using GF imagery and the U.S. CDL dataset. The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation, and achieved overall accuracies exceeding 94% in all study areas. Furthermore, FI-R is compatible with other multispectral sensors that have similar band configurations, so it is applicable to rapeseed extraction in broader contexts. The method also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation.}
}