基于Sentinel-2和LSTM模型的菜心种植分布识别OA

[Objective]Choi sum is a vegetable often grown in fragmented smallholder plots in China.Accurately mapping its regional planting distribution is important for agricultural management but remains challenging.This study developed a remote-sensing-based method to address this challenge.[Method]The method was based on multi-temporal Sentinel-2 imagery and a two-layer,long short-term memory(LSTM)model.Sentinel-2 L2A time-series imagery,combined with field survey data,was used as the primary data source.A multi-feature time-series dataset covering the entire growth period of choy sum from March to December 2025 was constructed.The differences in time-series patterns of the normalized difference vegetation index(NDVI)between choy sum and other crops,including spring wheat,maize and rice,were analyzed.Based on these temporal differences,a two-layer LSTM-based model was developed to identify the spatial distribution of choy sum.[Result]Temporally,the time series of the NDVI of choy sum exhibited alternating peak-trough fluctuations,with peak values ranging from 0.7 to 0.9 and trough values ranging from 0.1 to 0.2.Such a pattern differed markedly from the single-peak pattern of single-season crops.Overall,the LSTM model achieved an accuracy of 97.39%for all crops,with a Kappa coefficient of 0.92.Its accuracy for choy sum and other crops was 0.93 and 0.98,respectively.[Conclusion]The proposed two-layer LSTM-based method can effectively map the spatial distribution of choy sum.It provides a useful approach for assessing spatial planting patterns and supporting water-resource management and precision agriculture for irrigation districts.