Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (22): 4638-4655.doi: 10.3864/j.issn.0578-1752.2025.22.007

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

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. 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 Online:2025-11-16 Published:2025-11-21
  • Contact: SONG Qian

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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

Fig. 1

Overview of the study area and crop field samples"

Fig. 2

Pixel-level counts of high-quality Landsat images from 2000 to 2021 in Hubei Province"

Fig. 3

The workflow of this study"

Fig. 4

Time-series NDVI and phenology windows of winter rapeseed-rice rotation sample in study area"

Table 1

Characteristics of metrics used to evaluate the spectral and spatial accuracy of fused images"

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
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
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

Fig. 5

Original Landsat NDVI and NDVI generated by four spatiotemporal fusion algorithms (STARFM, ESTARFM, STNLFFM, and GF-SG)"

Fig. 6

Scatter density plots between NDVI generated by four fusion algorithms and reference Landsat NDVI in two test areas"

Fig. 7

Comparison of spatial detail accuracy between NDVI generated by four fusion algorithms and reference Landsat NDVI in two test areas"

Fig. 8

Cropland CI extraction and accuracy assessment based on NDVI time series reconstructed by STARFM, ESTARFM, STNLFFM, and GF-SG algorithms"

Fig. 9

Spatial distribution and temporal variation of cropland CI in Hubei Province from 2000 to 2021 based on reconstructed NDVI time series"

Table 2

A confusion matrix for cropping intensity map in 2021"

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

Fig. 10

The transfer maps based on cropping intensity maps at five-year interval"

Fig. 11

Temporal changes in the proportion and area of cropping intensity transfer at five-year interval"

Fig. 12

Cropland CI distribution and corresponding Google Earth imagery (April 2021) in typical areas"

Fig. 13

Changes in the proportions of different cropland CI and related agricultural factors in Hubei Province from 2000 to 2021"

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