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Crop Mapping Improvement by Combination of Optical and SAR datasets

机译:通过结合光学和SAR数据集来改善作物制图

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Investigation of radar and optical data indices that contain a lot more information on landscapes and vegetation dynamics can be useful to identify opportunities and challenges in agricultural activities. In addition, the potential of synchronous implications of radar and optical data will be an effective method for agro-environmental monitoring and management to promote economic and environmental sustainability as monitoring programs. Crop discrimination as an agricultural monitoring system is a critical step regarding to estimate the area allocated to each crop type, computing statistics for crop control of area-based subsidies or crop production forecasting, environmental impact analysis and some other applications. Integrating both optical (reflectance) and Synthetic Aperture Radar (backscatter) multi-temporal features provides some advantages in terms of a more reliable crop map. We utilize multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery and Sentinel-2 optical datasets in order to investigate the performance of the sensors backscatter and reflectance for temporal crop type mapping and the sustainable management of agricultural activities. Multi-temporal Sentinel-1, C-band VV and VH polarized SAR data and Sentinel2 optical data were acquired simultaneously by in-situ measurements for the study area. As preliminary results, it is concluded that the classification accuracies were improved results (5%) with using combinations of sensors. Classification accuracies of 93% were achieved in this study with integration use of SAR and optical data.
机译:雷达和光学数据索引的调查包含有关景观和植被动态的更多信息,这对于识别农业活动中的机遇和挑战很有用。此外,雷达和光学数据同步影响的潜力将是一种农业环境监测和管理的有效方法,以促进经济和环境可持续性作为监测计划。作物歧视作为一种农业监测系统,对于估算分配给每种作物类型的面积,计算用于基于作物的面积控制的作物控制或作物产量预测的统计数据,环境影响分析以及其他一些应用而言,是至关重要的一步。将光学(反射率)和合成孔径雷达(后向散射)多时相特征集成在一起,在更可靠的作物图方面提供了一些优势。我们利用多时间Sentinel-1合成孔径雷达(SAR)图像和Sentinel-2光学数据集,以调查传感器的反向散射和反射性能,以进行临时作物类型制图和农业活动的可持续管理。通过对研究区域进行现场测量,同时获取了多时相Sentinel-1,C波段VV和VH极化SAR数据和Sentinel2光学数据。作为初步结果,可以得出结论,使用传感器组合可以提高分类精度(5%)。在这项研究中,SAR和光学数据的综合使用实现了93%的分类精度。

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