协同多尺度遥感影像的2000—2021年湖北省耕地种植强度时空演变分析

doi:10.3864/j.issn.0578-1752.2025.22.007

中国农业科学 ›› 2025, Vol. 58 ›› Issue (22): 4638-4655.doi: 10.3864/j.issn.0578-1752.2025.22.007

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

协同多尺度遥感影像的2000—2021年湖北省耕地种植强度时空演变分析

胡洁¹(), 马海荣¹, 罗治情¹, 陈娉婷¹, 郑明雪¹, 官波¹, 徐保东², 宋茜³()

¹ 湖北省农业科学院农业经济技术研究所/湖北省农业科技创新中心农业经济技术研究分中心/湖北省乡村振兴研究院，武汉 430064
² 华中农业大学资源与环境学院/数字农业研究院，武汉 430070
³ 中国农业科学院农业资源与农业区划研究所/农业农村部农业遥感重点实验室/北方干旱半干旱耕地高效利用全国重点实验室，北京 100081

收稿日期:2025-04-18 接受日期:2025-08-01 出版日期:2025-11-16 发布日期:2025-11-21
通信作者:
宋茜，E-mail：songqian01@caas.cn。
联系方式: 胡洁，E-mail：hujiejie520@163.com。
基金资助:
国家重点研发计划(2023YFD1900101); 北方干旱半干旱耕地高效利用全国重点实验室开放课题(EUAL-2024-01); 国家自然科学基金(42471431); 湖北省农业科技创新中心“农业经济与信息研究”团队项目(2025-620-000-001-025)

Spatiotemporal Analysis of Cropland Cropping Intensity in Hubei Province from 2000 to 2021 by Integrating Multi-Scale Remote Sensing Imagery

HU Jie¹(), MA HaiRong¹, LUO ZhiQing¹, CHEN PingTing¹, ZHENG MingXue¹, GUAN Bo¹, XU BaoDong², SONG Qian³()

¹ Institute of Agricultural Economics and Technology, Hubei Academy of Agricultural Sciences/Agricultural Economics and Technology Research Division, Hubei Agricultural Science and Technology Innovation Center/Hubei Institute of Rural Revitalization, Wuhan 430064
² College of Resources and Environment/Digital Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
³ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/State Key Laboratory of Efficient Utilization of Arable Land in China, Beijing 100081

Received:2025-04-18 Accepted:2025-08-01 Published:2025-11-16 Online:2025-11-21

摘要/Abstract

摘要：

【目的】针对我国南方地区云雨频繁、耕地细碎及多熟种植所造成的耕地种植强度（cropping intensity，CI）提取与动态监测不确定性问题，拟充分发挥多尺度遥感观测的优势，实现2000—2021年湖北省耕地CI的高效精准提取，并解析区域农业生产格局的时空演变规律。【方法】协同250 m MODIS 归一化差值植被指数（NDVI）和30 m Landsat NDVI时序数据，分别利用STARFM、ESTARFM、STNLFFM和GF-SG 4种典型的时空融合算法生成高时空分辨率NDVI数据，从光谱保真度（AD/RMSE）、空间细节精度（Edge/LBP）及CI提取效果对融合数据进行综合评估。利用优选的时空融合算法生成2000—2021年湖北省30 m/8 d时空分辨率NDVI数据集，并基于物候的峰值探测法，开展长时序耕地种植强度提取及时空演变分析。【结果】与其他3种时空融合算法相比，GF-SG算法在光谱保真度与空间细节精度方面表现最佳（|AD|<0.021，RMSE<0.111，|Edge|<0.55，|LBP|<0.10），利用该算法重构的NDVI时序数据集提取耕地CI的精度提升了0.02%—5.53%。基于地面实地采样数据开展精度评价，湖北省耕地CI分类的总体精度达86.60%。2000—2021年耕地CI时空演变分析显示，研究区每5年约有20%—25%的耕地发生种植强度转变，其中2005—2010年变动最显著（25.79%），2010—2015年变动最小（20.07%）。主导转变类型由“一熟转二熟”（13.49%）逐步演变为“二熟转一熟”（9.35%）和“一熟转休耕”（4.90%）。【结论】近20年来湖北省形成以“一熟为主、二熟与休耕共存”的多元耕作格局，耕地CI演变由政策引导、劳动力变化、资源投入与种植结构调整等因素共同驱动。通过协同MODIS与Landsat多尺度遥感数据构建的高时空分辨率NDVI数据集，可有效支撑复杂农业景观下长时序耕地CI的高效精准提取，可为农业生产管理与耕地保护政策制定提供重要支撑。

关键词: 耕地种植强度, 多尺度遥感影像, 时空融合算法, NDVI时序, 时空演变

Abstract:

【Objective】 To address the uncertainty in the extraction and dynamic monitoring of cropland cropping intensity (CI) caused by frequent cloud cover, fragmented farmland, and multi-cropping systems in southern China, this study aimed to fully leverage the advantages of multi-scale remote sensing observations to achieve efficient and accurate CI mapping for Hubei Province from 2000 to 2021, and to analyze the spatiotemporal evolution of regional agricultural production patterns. 【Method】 Time-series NDVI data from 250 m MODIS and 30 m Landsat were integrated using four representative spatiotemporal fusion algorithms: STARFM, ESTARFM, STNLFFM, and GF-SG. Fusion performance was comprehensively evaluated based on spectral fidelity (AD, RMSE) and spatial detail accuracy (Edge, LBP). The optimal algorithm was used to generate a 30 m/8-day NDVI dataset for 2000-2021. Cropland CI was extracted using a phenology-based peak detection method, and then its spatiotemporal variation was analyzed. 【Result】Compared with the other three spatiotemporal fusion algorithms, the GF-SG algorithm demonstrated the best performance in both spectral fidelity and spatial detail accuracy (|AD|<0.021, RMSE<0.111; |Edge|<0.55, |LBP|<0.10). The reconstructed NDVI time series using this algorithm improved the accuracy of cropland CI extraction by 0.02%-5.53%. Based on ground samples, the overall classification accuracy of cropland CI in Hubei Province reached 86.60%. From 2000 to 2021, approximately 20%-25% of croplands in the study area experienced CI transitions every five years, with the most significant changes occurring between 2005-2010 (25.79%) and the least between 2010-2015 (20.07%). The dominant transition type shifted from 'single-cropping to double-cropping' (13.49%) in the early years to 'double-cropping to single-cropping' (9.35%) and 'single-cropping to fallow' (4.90%) in the later years. 【Conclusion】Over the past two decades, Hubei Province has developed a diversified cultivation pattern dominated by single cropping, with coexistence of double cropping and fallow practices. The evolution of cropland CI has been jointly driven by policy guidance, labor force changes, resource input, and adjustments in cropping structure. By integrating multi-scale remote sensing data from MODIS and Landsat, this study constructed a high spatiotemporal resolution NDVI dataset, which enabled efficient and accurate extraction of long-term cropland CI in complex agricultural landscapes. The findings offered the critical support for agricultural production management and the development of cropland protection policies.

Key words: cropland cropping intensity, multi-scale remote sensing data, spatiotemporal fusion algorithm, NDVI time series, spatiotemporal dynamics

胡洁, 马海荣, 罗治情, 陈娉婷, 郑明雪, 官波, 徐保东, 宋茜. 协同多尺度遥感影像的2000—2021年湖北省耕地种植强度时空演变分析[J]. 中国农业科学, 2025, 58(22): 4638-4655.

HU Jie, MA HaiRong, LUO ZhiQing, CHEN PingTing, ZHENG MingXue, GUAN Bo, XU BaoDong, SONG Qian. Spatiotemporal Analysis of Cropland Cropping Intensity in Hubei Province from 2000 to 2021 by Integrating Multi-Scale Remote Sensing Imagery[J]. Scientia Agricultura Sinica, 2025, 58(22): 4638-4655.

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评价角度 Evaluation aspect	评价指标 Metric name	其值范围 Value range	数值含义 Variable explanation
光谱 Spectral accuracy	均方根误差 RMSE	[0,1]	数值0代表精准的融合影像 A value of 0 indicates a precise fusion image
	均方根误差 RMSE	[0,1]	数值越大，表示融合图像的光谱误差越大 A larger value indicates greater spectral error in the fused image
	平均差异 AD	[-1,1]	数值0代表精准的融合影像 A value of 0 indicates a precise fusion image
			负值表示融合影像中光谱信息的低估 A negative value indicates an underestimation of spectral information
			正值表示融合影像中光谱信息的高估 A positive value indicates an overestimation of spectral information
空间 Spatial accuracy	边缘特征 Edge	[-1,1]	值0代表精准的融合影像 A value of 0 indicates a precise fusion image
	边缘特征 Edge	[-1,1]	负值越大，表示融合影像中的边缘特征过度平滑 A large negative value indicates over smoothing of edge features in the fused image
	局部二值模式 LBP	[-1,1]	正值越大，表示融合影像中的边缘特征过度锐化 A large positive value indicates over sharpening of edge features in the fused image

分类类别 Classes	一熟耕地 Single-cropping cropland	二熟耕地 Double-cropping cropland	休耕地 Fallow cropland	PA (%)
一熟耕地 Single-cropping cropland	988	66	34	90.81
二熟耕地 Double-cropping cropland	153	890	21	83.65
休耕地 Fallow cropland	31	16	196	80.66
UA (%)	84.30	91.56	78.09
OA (%)	86.60

协同多尺度遥感影像的2000—2021年湖北省耕地种植强度时空演变分析

Spatiotemporal Analysis of Cropland Cropping Intensity in Hubei Province from 2000 to 2021 by Integrating Multi-Scale Remote Sensing Imagery

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