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An automatic rice mapping method based on constrained feature matching exploiting Sentinel-1 data for arbitrary length time series
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-26 , DOI: 10.1016/j.jag.2022.103032
Xueqin Jiang, Shanjun Luo, Song Gao, Shenghui Fang, Yanyan Wang, Kaili Yang, Qiang Xiong, Yuanjin Li

Rice is one of the staple food crops worldwide, and timely and accurate paddy rice mapping (PRM) is essential to coordinate agricultural production and assure grain security. In cloudy and foggy regions, there are low exploitation rates of optical images, and accurate PRM is a commonly occurring difficulty. To achieve a precise and timely PRM in these regions, an automatic PRM method based on constrained feature matching (Auto-CFM) for arbitrary length time series was proposed using Sentinel-1 Synthetic Aperture Radar (SAR) data, which takes into account the local shape differences of the time-series σ0 VH curves of different land cover types and the overall deviation of the curves due to the discrepancy in rice planting time. Moreover, it solved the problem of high precision extraction of rice when the Sentinel-1 data might suffer from partial missing images. In this study, the PRM was conducted in Hunan Province and validated in Hubei, Guangdong, and Heilongjiang Provinces, which featured different planting times, climates, and topographies. The results showed that the Auto-CFM improved the accuracy by 3%–9% compared to current competing methods and the PRM accuracy exceeded 92% in different validation areas, proving the effectiveness and robustness of the method. To obtain cultivation areas as early as possible, the PRM was performed by reducing the number of images one by one to eventually acquired the rice maps in mid to late August with an overall accuracy of no less than 90%, achieving the goal of access to the spatial distribution of rice with high accuracy before harvest.



中文翻译:

基于 Sentinel-1 数据的任意长度时间序列的约束特征匹配的自动水稻映射方法

水稻是全球主要粮食作物之一,及时准确的水稻测绘(PRM)对于协调农业生产和保障粮食安全至关重要。在多云和多雾地区,光学图像的利用率较低,准确的PRM是一个普遍存在的困难。为了在这些区域实现精确、及时的 PRM,利用 Sentinel-1 合成孔径雷达 (SAR) 数据,提出了一种基于约束特征匹配 (Auto-CFM) 的任意长度时间序列的自动 PRM 方法,该方法考虑了局部不同土地覆盖类型的时间序列σ0 VH曲线的形状差异以及由于水稻种植时间的差异导致曲线的整体偏差。而且,它解决了Sentinel-1数据可能存在部分缺失图像时的水稻高精度提取问题。在这项研究中,PRM 在湖南省进行,并在湖北、广东和黑龙江省进行了验证,这些省具有不同的种植时间、气候和地形。结果表明,与当前竞争方法相比,Auto-CFM 的准确度提高了 3%–9%,PRM 准确度在不同验证区域均超过 92%,证明了该方法的有效性和稳健性。为了尽早获得种植面积,PRM通过一张一张减少图像进行,最终获得了8月中下旬的水稻图,整体准确率不低于90%,

更新日期:2022-09-27
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