预测地面数据稀疏地区的水生产率:可转移的框架及其应用于朝鲜
Yu Shi1, Linchao Li2, Bingyan Wu2
1Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, Shaanxi 712100, China; International Center for Climate and Global Change Research, College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA.
The Science of the total environment
|June 27, 2024
概括
这项研究开发了一个可转移的框架,使用机器学习和遥感来评估像朝鲜这样的数据稀疏地区的水生产率. 随机森林模型准确预测了产量,有助于评估粮食安全.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 在数据稀疏的地区评估作物生产率是一项挑战.
- 由于数据的可用性有限,依赖观察的方法往往会失败.
研究的目的:
- 开发一个可转移的框架来评估朝鲜的生产力.
- 利用气候相似性,机器学习和多源数据进行准确的产量预测.
主要方法:
- 使用现象学阶段和中等分辨率成像光谱辐射计 (MODIS) 产品提取了动态大米分布.
- 对比了四种机器学习模型 (线性回归,神经网络,支持向量机,随机森林) 来模拟大米的生产力.
- 整合了一个最佳模型与农业管理实践进行转移和预测.
主要成果:
- 随机森林表现出卓越的表现,达到0.87的R平方 (整个生长期) 和0.83 (播种头期).
- 太阳引起的叶绿素光,最大温度和蒸发是主要的产量决定因素 (约. 40%). 在这个问题上.
- 种植面积显著影响了产量 (超过42%),与粮农组织数据相比,该框架解释了大量的产量和产量变化.
结论:
- 通过遥感的现象识别能够准确地捕捉到大米的生长和分布.
- 拟议的可转让框架加强了作物生产率评估和粮食安全早期预警系统.
- 该框架显示了可扩展到全球其他作物和数据稀疏地区的潜力.
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