Fusing Optical Remote Sensing and SAR Garlic Planting Structure Extraction Method

Abstract

Obtaining crop planting structure information is an important basis for agricultural production management and policy formulation. As an important economic crop in China, high-precision identification of garlic planting distribution is of great significance for production management, optimization of planting structure, and yield assessment. Addressing the problems of mixed pixels and spectral confusion caused by the overlapping growth periods of garlic and winter wheat, and the small and scattered planting plots, this study takes Kaifeng City in Henan Province as the research area, integrating Sentinel-2 optical images and Sentinel-1 radar data, and conducts garlic planting distribution identification based on phenological features. The main results are as follows: (1) A multimodal time-series vegetation index dataset for the entire growth period of garlic was constructed, and after smoothing by Savitzky–Golay filtering, a continuous and stable vegetation index variation curve was obtained to identify key turning points for dividing critical phenological periods. (2) To address the problem that optical data is easily affected by weather, a method integrating optical and SAR data was proposed, extracting optical and radar features at different phenological periods to construct optimal recognition features. Classification results show that after integrating optical and SAR data with phenological information, the overall accuracy reached 96.27%, with a Kappa coefficient of 0.95. This method effectively overcomes the problems of spectral confusion and mixed pixels in garlic identification, significantly improves classification accuracy and spatial consistency, and provides effective technical support for the accurate acquisition of garlic planting structure information.