CN_Wheat10: a 10 m resolution dataset of spring and winter wheat distribution in China (2018–2024) derived from time-series remote sensing

Abstract. Wheat, as one of the main food crops in the world, plays a vital role in shaping agricultural trade patterns. China is the largest producer and consumer of wheat globally, characterized by extensive cultivation areas and diverse planting systems. However, current remote sensing-based wheat mapping studies often rely on uniform phenological feature variables, without adequately accounting for the significant differences in wheat growth cycles across China's diverse agro-ecological zones. In addition, the lack of large-scale training samples severely limits both the accuracy and the spatial-temporal generalization capacity. Furthermore, existing research in China focuses mainly on winter wheat monitoring and mapping, while spring wheat research remains largely inadequate, especially in the major spring wheat-producing areas of northern China, resulting in limited availability of targeted remote sensing products. These limitations hinder the development of high-accuracy, spatially comprehensive wheat mapping datasets and reduce the completeness of agricultural monitoring and food security assessments. To address these issues, this study proposes a cross-regional training sample generation method that integrates time-series remote sensing data with crop distribution products. Furthermore, a province-level, differentiated feature selection strategy is introduced to enhance the regional adaptability and classification performance of the model. Based on these methods, we developed 10 m resolution wheat distribution dataset (CN_Wheat10) covering the years 2018–2024. The dataset includes spring and winter wheat harvested area maps for 15 provinces and detailed winter wheat planted area maps for 10 provinces across China. Validation using a large-scale reference dataset built from field surveys and high-resolution imagery visual interpretation indicates that CN_Wheat10 achieves mapping accuracies above 0.93 for winter wheat and above 0.91 for spring wheat. When compared with wheat area statistics from the China Statistical Yearbook, the coefficient of determination (R2) exceeds 0.94 at the provincial level and remains above 0.88 at the municipal level. Spatially, wheat cultivation in China is characterized by a pattern of concentration in the east, dispersion in the west, a dominance of winter wheat, and a supplementary role of spring wheat. CN_Wheat10 provides spatial distribution information on both spring and winter wheat harvested areas and winter wheat planted regions in key production areas. Compared with existing products that mainly focus on winter wheat, this dataset expands both the spatial coverage and the crop types, offering more comprehensive data support for agricultural monitoring and management in China. The CN_Wheat10 product is freely accessible at https://doi.org/10.6084/m9.figshare.28852220.v2 (Liu et al., 2025a).

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Publication Details

Journal
Earth system science data
Published
2026-01-19
DOI
https://doi.org/10.5194/essd-18-465-2026
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

CN_Wheat10: a 10 m resolution dataset of spring and winter wheat distribution in China (2018–2024) derived from time-series remote sensing

Man Liu, Hongyan Zhang, Wei He
Earth system science data
Remote Sensing in Agriculture
article

CN_Wheat10: a 10 m resolution dataset of spring and winter wheat distribution in China (2018–2024) derived from time-series remote sensing

Man Liu, Hongyan Zhang, Wei He
article en

Abstract

Abstract. Wheat, as one of the main food crops in the world, plays a vital role in shaping agricultural trade patterns. China is the largest producer and consumer of wheat globally, characterized by extensive cultivation areas and diverse planting systems. However, current remote sensing-based wheat mapping studies often rely on uniform phenological feature variables, without adequately accounting for the significant differences in wheat growth cycles across China's diverse agro-ecological zones. In addition, the lack of large-scale training samples severely limits both the accuracy and the spatial-temporal generalization capacity. Furthermore, existing research in China focuses mainly on winter wheat monitoring and mapping, while spring wheat research remains largely inadequate, especially in the major spring wheat-producing areas of northern China, resulting in limited availability of targeted remote sensing products. These limitations hinder the development of high-accuracy, spatially comprehensive wheat mapping datasets and reduce the completeness of agricultural monitoring and food security assessments. To address these issues, this study proposes a cross-regional training sample generation method that integrates time-series remote sensing data with crop distribution products. Furthermore, a province-level, differentiated feature selection strategy is introduced to enhance the regional adaptability and classification performance of the model. Based on these methods, we developed 10 m resolution wheat distribution dataset (CN_Wheat10) covering the years 2018–2024. The dataset includes spring and winter wheat harvested area maps for 15 provinces and detailed winter wheat planted area maps for 10 provinces across China. Validation using a large-scale reference dataset built from field surveys and high-resolution imagery visual interpretation indicates that CN_Wheat10 achieves mapping accuracies above 0.93 for winter wheat and above 0.91 for spring wheat. When compared with wheat area statistics from the China Statistical Yearbook, the coefficient of determination (R2) exceeds 0.94 at the provincial level and remains above 0.88 at the municipal level. Spatially, wheat cultivation in China is characterized by a pattern of concentration in the east, dispersion in the west, a dominance of winter wheat, and a supplementary role of spring wheat. CN_Wheat10 provides spatial distribution information on both spring and winter wheat harvested areas and winter wheat planted regions in key production areas. Compared with existing products that mainly focus on winter wheat, this dataset expands both the spatial coverage and the crop types, offering more comprehensive data support for agricultural monitoring and management in China. The CN_Wheat10 product is freely accessible at https://doi.org/10.6084/m9.figshare.28852220.v2 (Liu et al., 2025a).

Earth system science dataVol. 18(1)
China University of Geosciences (CN), Wuhan University (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
Zero hunger
Openalex Percentile: Top 87%
Remote Sensing in Agriculture
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