As a crucial grain production base in Northern Xinjiang, Changji City regards winter wheat as one of its core food crops, which is essential for regional food security. In recent years, climate change has led to water shortages and alterations in crop overwintering environments in Changji City, making accurate mapping of winter wheat distribution an urgent need for agricultural management and policy formulation. This dataset was generated using Sentinel-1 SAR and Sentinel-2 MSI images from March to May each year between 2019 and 2025 in Changji City, Xinjiang, based on the Google Earth Engine platform through a synergistic use of active and passive remote sensing combined with a multi-feature optimization method. The dataset comprises annual spatial distribution data of winter wheat planting in Changji City, with a spatial resolution of 10 meters, covering the entire area of approximately 7,974 km². The dataset utilizes radiometrically calibrated and atmospherically corrected L2A-level optical imagery and GRD-level radar imagery to construct a multidimensional feature set including polarization, texture, spectral, vegetation indices, normalized difference indices, and phenological change features. A random forest forward selection algorithm was employed for feature optimization, and a random forest classifier was used to identify winter wheat planting areas. The dataset was validated through field survey samples and visual interpretation of high-resolution imagery, achieving an overall classification accuracy of 94.60%. The data are stored in GeoTIFF format with standardized metadata descriptions, providing reliable spatial data support for dynamic monitoring of crop planting structures in arid regions, optimized allocation of water resources, crop yield estimation, and agricultural policy formulation.
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Liangzhong CAO
Jianlei YUAN
Fang Xia
China Scientific Data
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CAO et al. (Thu,) studied this question.
www.synapsesocial.com/papers/69a75f8fc6e9836116a2b060 — DOI: https://doi.org/10.11922/11-6035.ncdc.2025.0170.zh