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相关实验视频

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In Situ Soil Moisture Sensors in Undisturbed Soils
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通过融合CYGNSS和使用机器学习方法的多源辅助数据,每天检索土壤水分.

Ting Yang1,2, Jundong Wang3,4, Zhigang Sun1,2,3,4

  • 1CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
概括

这项研究通过将Cyclone全球导航卫星系统 (CYGNSS) 数据与光学/微波遥感变量结合起来,提高了土壤水分检索的准确性. 一个渐变增强回归树模型改善了大规模的土壤湿度映射,即使环境干扰.

关键词:
这是一个CYGNSSSS.在GBRT中,GBRT就是GBRT.数据融合数据融合土地覆盖面 土地覆盖面土壤水分 土壤水分

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科学领域:

  • 地球科学 地球科学 地球科学
  • 遥感 遥感 遥感 遥感
  • 环境监测 环境监测

背景情况:

  • 气旋全球导航卫星系统 (CYGNSS) 提供太空全球导航卫星系统反射计 (GNSS-R) 用于土壤水分检索.
  • 复杂的环境因素,如植被和土壤粗性,干扰了准确的土壤湿度 (SM) 测量.

研究的目的:

  • 开发基于CYGNSS的土壤水分检索高精度模型.
  • 减轻环境干扰对SM估计的影响.

主要方法:

  • 融合CYGNSS规范表面反射率与光学/微波遥感变量.
  • 采用了梯度增强回归树 (GBRT) 模型,用于多变量SM检索的土地类型数据.
  • 开发了针对六种不同的土地类型量身定制的多种模型.

主要成果:

  • 验证了中国东南部的方法,显示与现有的卫星产品和现场数据有很强的相关性.
  • 实现了高精度指标:R = 0.765,ubRMSE = 0.054 m3m−3与SMAP相比;R = 0.653,ubRMSE = 0.057 m3m−3与ERA5 SM相比;R = 0.691,ubRMSE = 0.057 m3m−3与现场SM相比.
  • 通过数据融合和多模型方法,证明了CYGNSS SM检索准确度的提高.

结论:

  • 该研究通过整合辅助遥感数据,成功提高了CYGNSS的土壤湿度检索准确度.
  • 开发的多层,多模型方法有效地解决了不同的土地属性,以进行可靠的SM估计.