数字农科院2.0

A spatiotemporal collaborative approach for precise crop planting structure mapping based on multi-source remote-sensing data

文献类型： 外文期刊

作者： Sun, Yingwei;Yao, Na;Luo, Jiancheng;Leng, Pei;Liu, Xiangyang

作者机构：

关键词： remote sensing;crop classification;multi-platform integration;fine-scale mapping;time-series analysis

期刊名称： INTERNATIONAL JOURNAL OF REMOTE SENSING

ISSN： 0143-1161

年卷期： 2023 年

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： In order to ensure food security, it is crucial to collect agricultural information efficiently and accurately. Remote sensing has become increasingly important in obtaining crop distribution information on a large scale. However, current research based on satellite platforms struggles to meet the requirements of high-precision and large-scale crop monitoring simultaneously. To address this challenge, we propose a method for achieving fine-scale crop classification by integrating remote-sensing data from various satellite platforms by constructing temporal-scale crop features within the parcels using Sentinel-2A, Landsat-8, and Gaofen-6. We adopt a feature-matching method to fill in missing values in the time-series feature construction process, to avoid issues with unidentifiable crops. The classification results of the Yellow River basin of the Ningxia region show that our method can achieve a wide range of crop discrimination on a fine scale, with an overall accuracy of 80%. Our proposed method demonstrates the potential of integrating multi-platform remote-sensing data to achieve fine-scale crop classification, which can aid decision-making for farmers, government agencies, and other stakeholders involved in the agricultural sector.

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