收稿日期: 2025-07-10
修回日期: 2025-10-14
网络出版日期: 2025-11-10
基金资助
国家重点研发计划项目(2022YFB3903502);湖北省自然科学基金创新群体项目(2024AFA032);华中师范大学优秀研究生教育创新资助项目(2024CXZZ002)
Investigation of Farmland Utilization in Naiman Banner Using Time-Series Remote Sensing Imagery and Deep Learning
Received date: 2025-07-10
Revised date: 2025-10-14
Online published: 2025-11-10
Supported by
the National Key Research and Development Program of China(2022YFB3903502);Hubei Provincial Natural Science Foundation of China(2024AFA032);The 2024 Excellent Graduate Education Innovation Funding Project of Central China Normal University(2024CXZZ002)
开展奈曼旗地区的农田利用现状分析，有助于推动农牧交错区的农业高质量发展，支撑国家粮食安全战略。基于2020—2024年Landsat 8时序数据，采用随机森林分类提取主要粮食作物种植信息，并利用吉林一号数据，结合多尺度分割算法与U-Net深度学习模型，提取农田地块与道路分布，系统评估区域的主要粮食作物种植格局与农田基础设施建设水平的关系。研究结果表明，近5年来，奈曼旗主要粮食作物种植面积稳步增长，累计净增22 673.46 hm2，增幅达12.9%，种植区域逐步由一般耕地向高标准农田转移。区域农田基础设施建设水平显著提升，高标准农田建设区的农田基础设施优于一般耕地区域。农田基础设施建设水平越高的区域，主要粮食作物种植面积越大，呈现出显著的空间一致性与正相关关系。
谢君洋 , 王轶 , 黎孟琦 , 余强毅 , 吴文斌 , 吴浩 . 基于时序遥感影像和深度学习的奈曼旗地区农田利用现状分析[J]. 地球科学进展, 2025 , 40(12) : 1307 -1322 . DOI: 10.11867/j.issn.1001-8166.2025.081
Naiman Banner is located in an arid and semi-arid region and represents a typical agro-pastoral ecotone. Agricultural production in this area has long been constrained by complex natural conditions and the fragmented spatial distribution of cropland, resulting in an unclear understanding of the current state of farmland utilization. This, in turn, limits the region’s capacity for agricultural resource management and targeted policy implementation. Conducting an analysis of farmland utilization in Naiman Banner is therefore crucial for promoting high-quality agricultural development in agro-pastoral transitional zones and supporting the national food security strategy. In this study, Naiman Banner was selected as the research area. Based on Landsat 8 time-series imagery, the spatial distribution of major grain crops from 2020 to 2024 was extracted using the random forest method. Additionally, Jilin-1 high-resolution remote sensing imagery was used in combination with a multi-scale segmentation algorithm and the U-Net model to obtain cropland field parcels and road data for 2020 and 2024, respectively. On this basis, an evaluation index system for assessing the level of farmland infrastructure construction in the region was established. By integrating the extracted spatial distribution of major grain crops, this study focuses on analyzing the spatiotemporal patterns of major grain crops, the level of farmland infrastructure construction, and their interrelationship in Naiman Banner. Results showed a steady increase in the planting area of major grain crops over the five-year period, with a cumulative net growth of 22 673.46 hm2, representing a 12.9% increase. Planting areas gradually shifted from general farmland to well-facilitated farmland. Meanwhile, agricultural infrastructure was continuously improved, with significantly higher development levels observed in well-facilitated farmland areas compared to general farmland. Further analysis revealed that regions with higher levels of agricultural infrastructure exhibited higher utilization rates of staple crops, demonstrating clear spatial consistency and a positive correlation. These findings provide provide technical support and decision-making references for regional agricultural resource management, optimization of infrastructure allocation, and food security assurance.
Key words： Remote sensing; Staple crops; Agricultural infrastructure; U-Net; Naiman Banner.
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