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Smart Agriculture ›› 2025, Vol. 7 ›› Issue (6): 174-184.doi: 10.12133/j.smartag.SA202508024

• 专刊--遥感+AI 赋能农业农村现代化 • 上一篇    下一篇

典型露天蔬菜种植区耕地复种指数遥感监测方法

张云翔, 吴学群(), 何勇林, 马俊伟   

  1. 昆明理工大学 国土资源工程学院,云南 昆明 650093,中国
  • 收稿日期:2025-08-26 出版日期:2025-11-30
  • 基金项目:
    国家自然科学基金(42261074,42464001)
  • 作者简介:

    张云翔,硕士研究生,研究方向为农业遥感。E-mail:

  • 通信作者:
    吴学群,博士,副教授,研究方向为空间数据分析研究。E-mail:

Remote Sensing Monitoring Method of Cropping Index in Typical Open-Field Vegetable Production Areas

ZHANG Yunxiang, WU Xuequn(), HE Yonglin, MA Junwei   

  1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
  • Received:2025-08-26 Online:2025-11-30
  • Foundation items:National Natural Science Foundation of China(42261074,42464001)
  • About author:

    ZHANG Yunxiang, E-mail:

  • Corresponding author:
    WU Xuequn, E-mail:

摘要:

目的/意义 现有复种指数提取研究多集中于粮食作物,而针对蔬菜种植制度的研究相对不足,限制了对典型高复种蔬菜种植地区耕地利用特征的深入认识。本研究旨在完善针对蔬菜种植制度的复种指数提取方法,从而深化对典型高复种蔬菜种植区耕地利用特征的认识。 方法 基于Sentinel-2数据,采用Whittaker Smoothing(WS)对归一化差分植被指数(Normalized Difference Vegetation Index, NDVI)时间序列进行平滑重构,结合二次差分法以10 m空间分辨率提取了2020—2024年通海县耕地复种指数,并分析了其空间分布和时空演变特征。 结果和讨论 通过实地调查的2024年验证数据对提取结果进行验证,通海县耕地复种指数提取总精度达89.94%,Kappa系数为0.84,平均绝对误差(Mean Absolute Error, MAE)为0.11,均方根误差(Root Mean Square Error, RMSE)为0.36。2020—2024年通海县耕地复种指数分别为221.45%、217.80%、275.37%、232.41%、237.50%,复种水平较高。通海县耕地复种指数在2020—2024年整体表现出先升后降的变化趋势,即从2020年的221.45%增加到2022年的275.37%,随后降低至2024年的237.50%,其变化主要受双季与3季种植制度相互转换的影响。 结论 2020、2021、2023和2024年以双季种植为主,3季种植次之;而2022年则以3季种植为主,双季种植次之。多季种植(≥3季)主要分布于杞麓湖沿岸城区。研究结果可为通海县耕地资源管理、区域蔬菜生产优化及可持续发展提供理论与技术参考。

关键词: 耕地复种指数, Whittaker Smoothing, 时序曲线重构, 蔬菜种植制度, 活跃耕地, 时空变化

Abstract:

Objective Most existing studies on cropping index extraction have primarily focused on cereal crops, whereas investigations targeting vegetable-based cropping systems remain relatively limited. Current national-scale cropping index products are largely designed for major cereal-producing regions, with model parameters calibrated according to the phenological characteristics and growth cycles of cereal crops. Such parameterization neglects the distinctive multi-season rotation patterns of open-field vegetable cultivation, leading to reduced accuracy in capturing the actual cropping dynamics of vegetable-growing areas. Consequently, these limitations hinder a comprehensive understanding of cropland utilization characteristics in regions characterized by intensive vegetable production. This study aims to improve the extraction method of the cropping index for vegetable cultivation systems and to reveal the characteristics of cropland use in typical regions with intensive vegetable multiple cropping. Methods Sentinel-2 MSI surface reflectance (SR) data released by the European space agency (ESA) was employed. Greedy algorithm was used to identify the optimal grid and orbit combination (47QRG, 61) covering Tonghai county, Yunnan Province. Based on this configuration, a total of 362 images acquired from 2020 to 2024 were compiled to construct a 5-day, 10 m spatial resolution time series. The normalized difference vegetation index (NDVI) time series was smoothed and reconstructed using the Whittaker smoothing (WS) method. The cropland extent was defined using the cropland mask from the GLC_FCS10 land cover dataset. Active croplands and greenhouse-covered areas were further identified using the vegetation-soil-pigment indices and synthetic-aperture radar (SAR) time-series images (VSPS) and the advanced plastic greenhouse index (APGI). Non-active croplands and greenhouse areas were excluded to refine the open-field cropland boundaries. Subsequently, the second-order difference method was applied to detect NDVI peaks in the reconstructed time series, with rule-based constraints used to eliminate false peaks. The number of valid peaks per pixel was then used to calculate the annual cropping index of Tonghai county from 2020 to 2024, and its spatial distribution and spatiotemporal variations were analyzed. Results and Discussions Compared with conventional 10-day median or maximum value compositing approaches, the time-series reconstruction based on specific grid and orbit combinations provides a more accurate representation of crop growth dynamics and peak patterns. Validation using 338 ground samples of cropping index obtained from field surveys in 2024 demonstrated an overall accuracy of 89.94%, a Kappa coefficient of 0.84, mean absolute error (MAE) of 0.11, and root mean square error (RMSE) of 0.36, indicating satisfactory reliability of the extracted results. From 2020 to 2024, the average cropping indices of croplands in Tonghai county were 221.45%, 217.80%, 275.37%, 232.41%, and 237.50%, respectively, reflecting a generally high level of land-use intensity. In 2020, 2021, 2023, and 2024, double cropping systems dominated, with triple cropping being secondary, whereas in 2022, triple cropping became predominant. Multi-season cropping (≥3 seasons) was mainly concentrated along the urban zones adjacent to Qilu lake, where abundant water resources provide favorable conditions for open-field vegetable cultivation. Interannual variations in the cropping index were largely driven by the alternation between double- and triple-cropping systems. Specifically, from 2020 to 2021, the cropping index decreased by 3.67%; cropland areas with decreased, unchanged, and increased indices accounted for 31.08%, 40.23%, and 28.69% of the total cropland area, respectively, with 10.45% of croplands shifting from triple to double cropping. From 2021 to 2022, the index increased substantially by 57.57%; decreased, unchanged, and increased areas accounted for 13.79%, 33.02%, and 53.18%, respectively, with 17.60% of croplands converting from double to triple cropping. Between 2022 and 2023, the index decreased by 42.96%, with corresponding area proportions of 47.96%, 35.06%, and 16.99%, and 16.62% of croplands shifting from triple to double cropping. From 2023 to 2024, the index slightly increased by 5.09%, with 27.49%, 39.19%, and 33.32% of croplands showing decreases, stability, and increases, respectively; 11.21% of croplands converted from double to triple cropping. Overall, the interannual variations were mainly influenced by the mutual transitions between double- and triple-cropping systems. Conclusions The results provide valuable theoretical and technical references for cropland resource management, optimization of regional vegetable production, and the promotion of sustainable agricultural development in Tonghai county.

Key words: cropping intensity, Whittaker smoothing, time-series curve construction, vegetable cropping system, active cropland, spatiotemporal dynamics

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