数字农科院2.0

From Sparse to Refined Samples: Iterative Enhancement-Based PDLCM for Multi-Annual 10 m Rice Mapping in the Middle-Lower Yangtze

文献类型： 外文期刊

作者： Yang, Lingbo;Dong, Jiancong;Xu, Cong;Huang, Jingfeng;Wang, Yichen;Ma, Huiqin;Chen, Zhongxin;Wang, Limin;Zhang, Jingcheng

作者机构：

关键词： rice mapping;deep learning;time-series remote sensing;large-scale mapping;Yangtze River Basin

期刊名称： REMOTE SENSING

ISSN：

年卷期： 2026 年 18 卷 2 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Highlights What are the main findings? A Progressive Deep Learning Crop Mapping framework enables accurate 10 m rice mapping over more than one million square kilometers using only sparse initial samples, achieving consistently high accuracy from 2022 to 2024 with strong spatial and temporal generalization. Model transferability exhibits a clear distance decay pattern and is jointly influenced by multiple factors, including spatial distance from training areas, climatic and phenological gradients, terrain and soil heterogeneity, and regional differences in multi-temporal image availability and quality. What are the implications of the main findings? The proposed framework provides a scalable and transferable paradigm for operational crop mapping, reducing dependence on extensive labeled datasets and lowering the barrier for national or continental scale applications. The publicly available multi-year 10 m rice maps offer reliable spatial information to support food security assessment, agricultural management, and sustainability-related policy making, with clear relevance to SDG 2 monitoring.Highlights What are the main findings? A Progressive Deep Learning Crop Mapping framework enables accurate 10 m rice mapping over more than one million square kilometers using only sparse initial samples, achieving consistently high accuracy from 2022 to 2024 with strong spatial and temporal generalization. Model transferability exhibits a clear distance decay pattern and is jointly influenced by multiple factors, including spatial distance from training areas, climatic and phenological gradients, terrain and soil heterogeneity, and regional differences in multi-temporal image availability and quality. What are the implications of the main findings? The proposed framework provides a scalable and transferable paradigm for operational crop mapping, reducing dependence on extensive labeled datasets and lowering the barrier for national or continental scale applications. The publicly available multi-year 10 m rice maps offer reliable spatial information to support food security assessment, agricultural management, and sustainability-related policy making, with clear relevance to SDG 2 monitoring.Abstract Accurate mapping of rice cultivation is vital for ensuring food security, reducing greenhouse gas emissions, and achieving sustainable development goals. However, large-scale deep learning-based crop mapping remains limited due to the demand for vast, uniformly distributed, high-quality samples. To address this challenge, we propose a Progressive Deep Learning Crop Mapping (PDLCM) framework for national-scale, high-resolution rice mapping. Beginning with a small set of localized rice and non-rice samples, PDLCM progressively refines model performance through iterative enhancement of positive and negative samples, effectively mitigating sample scarcity and spatial heterogeneity. By combining time-series Sentinel-2 optical data with Sentinel-1 synthetic aperture radar imagery, the framework captures distinctive phenological characteristics of rice while resolving spatiotemporal inconsistencies in large datasets. Applying PDLCM, we produced 10 m rice maps from 2022 to 2024 across the middle and lower Yangtze River Basin, covering more than one million square kilometers. The results achieved an overall accuracy of 96.8% and an F1 score of 0.88, demonstrating strong spatial and temporal generalization. All datasets and source codes are publicly accessible, supporting SDG 2 and providing a transferable paradigm for operational large-scale crop mapping.

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