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通过整合遥感和人工智能技术,在土壤湿度估计方面取得了进展
Harani P1, Sneha Gautam2, Suneel Kumar Joshi3
1Division of Civil Engineering, Karunya Institute of Technology and Sciences, Coimbatore - 641114, Tamil Nadu, India.
The Science of the total environment
|September 23, 2025
概括
准确的土壤湿度监测对农业和气候至关重要. 本综述探讨了传统,遥感和人工智能方法,突出了改善水资源管理的进步和挑战.
科学领域:
- 水文学和水文地质学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 土壤湿度对于作物生产率,生态系统功能,气候反和地下水补充至关重要.
- 准确的土壤湿度估计受到空间覆盖,深度灵敏度和实时监测挑战的限制.
研究的目的:
- 提供关于土壤湿度估计技术的最新进展的全面概述.
- 为了比较评估传统,遥感和基于人工智能的方法.
- 确定可扩展和可解释的土壤湿度监测的挑战和未来方向.
主要方法:
- 审查传统的现场遥感 (宇宙射线中子子传感,GNSS反射计,SAR) 和基于人工智能的方法.
- 对运营机制,空间限制和潜在协同作用的比较分析.
- 将多传感器数据与人工智能和物理模型集成.
主要成果:
- 基于人工智能的模型在解释性,透明度和可转移性方面面临挑战.
- 可解释的人工智能与遥感相结合,提高了模型性能和可靠性.
- 多传感器数据集成可以提高准确性,空间/时间分辨率和根区估计.
结论:
- 在处理不确定性和跨不同地区的模型可转移性方面需要进一步的研究.
- 公民科学和众包可以在数据稀缺的领域实现实时监控.
- 开发可扩展,可解释和强大的系统对于可持续的水资源管理和适应气候的农业至关重要.
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