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
Varun Tiwari1, Kelly Thorp2, Mirela G Tulbure1
1Center for Geospatial Analytics, North Carolina State University (NCSU), Raleigh, NC, United States of America.
PloS one
|December 12, 2024
概括
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.
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.
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