2025/09/17 by Sixian Yin, Taixia Wu, Shudong Wang +3 · 1 voice
Earth and Planetary Sciences · Environmental Science · #Remote Sensing and Land Use #Remote Sensing in Agriculture #Soil and Land Suitability Analysis
paper · doi:10.1016/j.jia.2025.05.006
openalex publication_date 2025/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
• A novel flowering spectral index for the fine-scale extraction of angiosperms over large areas in complex multi-regional backgrounds was developed using rapeseed as an example. • Ground hyperspectral characteristics of rapeseed and other typical crops in rapeseed planting areas were analyzed and extended to Landsat-8 imagery to develop the Flowering Index of Rapeseed (FI-R). • FI-R shows low sensitivity to background complexity and rapeseed varieties, and it has good applicability to multiple multi-spectral sensor images. 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, 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.