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Identification of Interannual Variation Frequency of Cropland Cropping Intensity Based on Remote Sensing Spatiotemporal Fusion and Crop Phenological Rhythm: A Case Study of Zhenjiang, Jiangsu

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  • Yaohui Zhu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Qingzhen Zhu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Yuanyuan Gao

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Liyuan Zhang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Aichen Wang

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Yongyun Zhu

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Chunshan Wang

    (School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China)

  • Bo Liu

    (School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China)

  • Fa Zhao

    (School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Peiying Li

    (College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

  • Xinhua Wei

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

  • Qi Song

    (School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China)

Abstract

The scientific evaluation of cropland resource utilization efficiency is crucial for ensuring food security and promoting sustainable agricultural development. At present, the research on the utilization of cropland resources primarily focuses on the multiple cropping index and cropping intensity, but these data are insufficient to reveal long-term trends and potential future changes in crop production. To fill this knowledge gap, this study took Zhenjiang City, Jiangsu Province, as a case study and proposed a method to determine the distribution and spatiotemporal change frequency of single- and double-season cropping patterns using spatiotemporal fusion and crop phenological rhythm. By combining Sentinel-2 NDVI and MOD13Q1 satellite data, a dataset with 10 m resolution was developed to show the interannual distribution frequency of the three cropping patterns in the study area. The accuracy evaluation revealed that the interannual cropping intensity distribution frequency of the three cropping patterns exhibited good verification accuracy, with an average overall accuracy and Kappa coefficient of 81.53% and 0.68, respectively. This study provides essential support for government agencies to assess future food production potential and develop policies for improving cropland use efficiency.

Suggested Citation

  • Yaohui Zhu & Qingzhen Zhu & Yuanyuan Gao & Liyuan Zhang & Aichen Wang & Yongyun Zhu & Chunshan Wang & Bo Liu & Fa Zhao & Peiying Li & Xinhua Wei & Qi Song, 2025. "Identification of Interannual Variation Frequency of Cropland Cropping Intensity Based on Remote Sensing Spatiotemporal Fusion and Crop Phenological Rhythm: A Case Study of Zhenjiang, Jiangsu," Agriculture, MDPI, vol. 15(9), pages 1-21, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:9:p:1004-:d:1650063
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    References listed on IDEAS

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    1. Huang, Dan & Lu, Yanchi & Liu, Yaolin & Liu, Yanfang & Tong, Zhaomin & Xing, Lijun & Dou, Chao, 2024. "Multifunctional evaluation and multiscenario regulation of non-grain farmlands from the grain security perspective: Evidence from the Wuhan Metropolitan Area, China," Land Use Policy, Elsevier, vol. 146(C).
    2. Muhammad Awais & Wei Li & Sajjad Hussain & Muhammad Jehanzeb Masud Cheema & Weiguo Li & Rui Song & Chenchen Liu, 2022. "Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data," Agriculture, MDPI, vol. 12(2), pages 1-19, January.
    3. Mohammed Ismail Alhussam & Jifan Ren & Hongxing Yao & Omar Abu Risha, 2023. "Food Trade Network and Food Security: From the Perspective of Belt and Road Initiative," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    4. Seyed Ahmadi & Elnaz Mosallaeepour & Ali Kamgar-Haghighi & Ali Sepaskhah, 2015. "Modeling Maize Yield and Soil Water Content with AquaCrop Under Full and Deficit Irrigation Managements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2837-2853, June.
    5. Zhang, Liyuan & Zhang, Huihui & Zhu, Qingzhen & Niu, Yaxiao, 2023. "Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value," Agricultural Water Management, Elsevier, vol. 285(C).
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