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| DOI：10.13522/j.cnki.ggps.2026075 |
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| Mapping the spatial distribution of choi sum using Sentinel-2 imagery and an LSTM model |
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Cheng Jiaxin, Li Yuanyang, Du Bin, Ye Yili, Zhang Jia, Ding Yimin
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1. School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China;
2. Ningxia Key Laboratory of Digital Water Regulation of the Yellow River Water Network, Yinchuan 750021, China;
3. Zhongshui Northern Survey, Design and Research Co., Ltd., Tianjin 300222, China;
4. Ningxia Hydraulic Research Institute, Yinchuan 750021, China
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| Abstract: |
| 【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. |
| Key words: time-series deep learning satellite remote sensing vegetation index hanyanqu irrigation district flowering Chinese cabbage |
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