一个数据集用于基于卫星遥感和天气变量的牧场参数估计
Guilherme Defalque1, Pedro Arfux1, Marcio Pache1,2
1College of Computing, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil.
Data in brief
|March 1, 2024
概括
这项研究引入了一个新的数据集,用于预测牧场质量和数量. 它结合了实验室分析,卫星数据和天气信息,以改善农业管理.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 准确的牧场参数估计对于畜牧和农业管理至关重要.
- 目前用于料分析的方法耗时且昂贵,限制了反复预测.
- 现有的数据集缺乏集成的料,遥感和天气数据.
研究的目的:
- 为预测牧场质量和数量提供一个全面的数据集.
- 促进用于料评估的机器学习模型的开发.
- 支持对牧场行为和管理策略的研究.
主要方法:
- 收集了300多个牧场样本,进行了近12个月的实验室分析.
- 获取每个样本的GPS坐标,通过Google Earth Engine与来自Sentinel-2卫星数据的多光谱信号进行连接.
- 来自API和气象站的综合天气数据.
主要成果:
- 该数据集包括详细的实验室分析,植被指数和环境数据.
- 为培训和验证牧场参数预测模型提供了宝贵的资源.
- 展示了结合各种数据源以获得农业洞察力的潜力.
结论:
- 提出的数据集解决了牧场分析综合信息的稀缺问题.
- 它可以更准确,更频繁地预测料的质量和数量.
- 这种资源可以刺激进一步研究精准农业和牧场管理.
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