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推进粮食安全:使用时间序列卫星数据和机器学习的产估计框架.

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此摘要是机器生成的。

准确的米产估计对于粮食安全至关重要. 这项研究为孟加拉国开发了一种高分辨率遥感工作流程,为农业管理和政策提供了关键数据.

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科学领域:

  • 农业科学 农业科学
  • 遥感 遥感 遥感 遥感
  • 粮食安全 粮食安全

背景情况:

  • 准确的水产量数据对于孟加拉国粮食安全管理和农业政策至关重要.
  • 现有的遥感方法缺乏高空间分辨率和与国家报告系统的整合.
  • 需要在分区规模上制定一致的年度博罗大米产量地图.

研究的目的:

  • 引入一个工作流程,以便在分区规模 (1,000米分辨率) 上及时准确地估计大米产量.
  • 为孟加拉国从2002年到2021年制定一个一致的年度博罗大米产量图.
  • 分析20多年来博罗大米产量的趋势.

主要方法:

  • 利用MODIS卫星数据和年度地区级产量数据.
  • 在1000米分辨率上训练了一种随机森林模型来估计米产量.
  • 使用修改后的曼-肯达尔趋势测试进行趋势分析.

主要成果:

  • 实现了8.07%的平均百分比根的平均平方误差 (RMSE) (地区产量) 和12.96% (作物切割数据).
  • 据估计,米产量的不确定性在每公0.40至0.45之间.
  • 在23%的水面积中发现了产量上升的趋势,在0.11%的水面积下降,在76.51%的水面积中没有趋势.

结论:

  • 开发的工作流提供了可扩展的,高分辨率的米产量估计,这对孟加拉国粮食安全至关重要.
  • 这是第一个为孟加拉国提供二十年来每年1000米分辨率的博罗大米产量地图的研究.
  • 该工作流可适应全球其他地区的产量估计.