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Data Details

A Plot-level Cropping Structure Dataset Based on Integrating Sentinel Images and Phenology Information in Changji Prefecture, Xinjiang Uygur Autonomous Region of China (2020-2024)


YU Lingxiang1WANG Xiaoqin*1ZHANG Hongyu1LIU Hongwei1
1 The Academy of Digital China ( Fujian)，Fuzhou University，Fuzhou 350108，China

DOI:10.3974/geodb.2025.10.01.V1

Published:Oct. 2025

Visitors：54       Data Files Downloaded：0      
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Key Words：

cropping structure,crop classification,MSTPI model,multi-source remote sensing features

Abstract：

As an important agricultural production base in China, Changji Prefecture, Xinjiang Uygur Autonomous Region has a diverse agricultural cropping structure and a rich variety of crops. The authors took time-series Sentinel-2 optical and Sentinel-1 radar data to construct a plot-level crop remote sensing classification model (Multi-source Spatial-Temporal-Phenological Integration, MSTPI), then developed a plot-level cropping structure dataset in Changji Prefecture (2020-2024). The data result shows that, wheat and corn dominated in the eastern region, cotton dominated in the western area, while economic crops interspersed in the central and southern regions. The dataset has a spatial resolution of 10 m, and a temporal resolution of 1 year, recording the cropping structure of Changji Prefecture for the 5 years from 2020 to 2024. The dataset is archived in .tif and .txt formats, and consists of 6 files with data size of 245 MB (compressed to one file with 97.8 MB).

Foundation Item：

Department of Science and Technology in Fujian (2023I0007)

Data Citation：

YU Lingxiang, WANG Xiaoqin*, ZHANG Hongyu, LIU Hongwei. A Plot-level Cropping Structure Dataset Based on Integrating Sentinel Images and Phenology Information in Changji Prefecture, Xinjiang Uygur Autonomous Region of China (2020-2024)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.10.01.V1.

References：


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     [7] Orynbaikyzy, A., Gessner, U., Conrad, C., Crop type classification using a combination of optical and radar remote sensing data: a review [J]. International journal of remote sensing, 2019, 40(17): 6553-6595.
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     [12] Zhang, H. Y., Lin, X. Y., Long, J., et al. SegFormer-based cotton planting areas extraction from high-resolution remote sensing images [C]//2023 11th International Conference on Agro-Geoinformatics. IEEE, 2023: 1-6.
     [13] Sakoe, H., Chiba, S., Dynamic programming algorithm optimization for spoken word recognition [J]. IEEE transactions on acoustics, speech, and signal processing, 1978, 26(1): 43-49.
     [14] Long, J., Li, M. M., Wang, X. Q. Integrating spatial details with long-range contexts for semantic segmentation of very high-resolution remote-sensing images [J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1-5.
     [15] Bureau of Agriculture and Rural Affairs, Changji Hui Autonomous Prefecture. Letter of reply to proposal No. 64 of the economic construction category of the third session of the 13th CPPCC of the autonomous prefecture [EB/OL]. (2024-05-09) [2025-03-08]. https://www.cj.gov.cn/p126/zxta/20240509/246810.html.
     [16] Kang, X. Y., Huang, C. P., Chen, J. M., et al. The 10-m cotton maps in Xinjiang, China during 2018–2021 [J]. Scientific Data, 2023, 10(1): 688. DOI: 10.1038/s41597-023-02597-5.
     [17] Peng, Q. Y., Shen, R. Q., Li, X. Q., et al. A twenty-year dataset of high-resolution maize distribution in China [J]. Scientific Data, 2023, 10(1): 658. DOI: 10.1038/s41597-023-02461-6.
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Data Product:

ID Data Name Data Size Operation
1 PLOTS_CRSP_XJ2020_24.rar 100163.28KB
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