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Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images

Author

Listed:
  • Jing Tang

    (Beijing Key Laboratory of High Dynamic Navigation, Beijing Information Science and Technology University, Beijing 100101, China)

  • Xiaoyong Zhang

    (Beijing Key Laboratory of High Dynamic Navigation, Beijing Information Science and Technology University, Beijing 100101, China)

  • Zhengchao Chen

    (Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Yongqing Bai

    (Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

The Hetao Plain and Xing’an League are the major cultivated areas and main grain-producing areas in Inner Mongolia, and their crop planting structure significantly affects the grain output and economic development in Northern China. Timely and accurate identification, extraction, and analysis of typical crops in Xing’an League and Hetao Plain can provide scientific guidance and decision support for crop planting structure research and food security in ecological barrier areas in Northern China. The pixel samples and the neighborhood information were fused to generate a spectral spatial dataset based on single-phase Sentinel-2 images. Skcnn_Tabnet, a typical crop remote sensing classification model, was built at the pixel scale by adding the channel attention mechanism, and the corn, sunflower, and rice in the Hetao Plain were quickly identified and studied. The results of this study suggest that the model exhibits high crop recognition ability, and the overall accuracy of the three crops is 0.9270, which is 0.1121, 0.1004, and 0.0874 higher than the Deeplabv3+, UNet, and RF methods, respectively. This study confirms the feasibility of the deep learning model in the application research of large-scale crop classification and mapping and provides a technical reference for achieving the automatic national crop census.

Suggested Citation

  • Jing Tang & Xiaoyong Zhang & Zhengchao Chen & Yongqing Bai, 2022. "Crop Identification and Analysis in Typical Cultivated Areas of Inner Mongolia with Single-Phase Sentinel-2 Images," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12789-:d:935667
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    References listed on IDEAS

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    1. Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    2. Yang, Qi & Zhang, Daojun, 2021. "The influence of agricultural industrial policy on non-grain production of cultivated land: A case study of the “one village, one product” strategy implemented in Guanzhong Plain of China," Land Use Policy, Elsevier, vol. 108(C).
    3. Zhang, Xiaoxing & Guo, Ping & Zhang, Fan & Liu, Xiao & Yue, Qiong & Wang, Youzhi, 2021. "Optimal irrigation water allocation in Hetao Irrigation District considering decision makers’ preference under uncertainties," Agricultural Water Management, Elsevier, vol. 246(C).
    4. Li-Tao Yang & Jun-Fang Zhao & Xiang-Ping Jiang & Sheng Wang & Lin-Hui Li & Hong-Fei Xie, 2022. "Effects of Climate Change on the Climatic Production Potential of Potatoes in Inner Mongolia, China," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
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    Cited by:

    1. Xiaolei Wang & Mei Hou & Shouhai Shi & Zirong Hu & Chuanxin Yin & Lei Xu, 2023. "Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China," Sustainability, MDPI, vol. 15(2), pages 1-17, January.

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