通过多源数据和机器学习融合模型提高网格雪深估计的准确性
Dejing Qiao1,2,3, Xiaoxiao Chen4, Jianmin Zhou5
1College of Surveying and Geo-Informatics , North China University of Water Resources and Electric Power , 450046, Zhengzhou, China. qiao_dejing@163.com.
Scientific reports
|November 20, 2025
概括
一种新的随机森林 (RF) 融合方法通过整合多个数据源来改善雪深 (SD) 估计. 这种方法提高了水资源和气候变化研究的空间分布精度.
科学领域:
- 地球科学 地球科学 地球科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 雪深 (SD) 对于水资源评估和气候变化监测至关重要.
- 来自各种来源 (被动微波,再分析,现场) 的现有SD数据往往缺乏完整性和一致性.
- 这些局限性阻碍了它们对科学研究的有用性.
研究的目的:
- 开发和验证使用随机森林算法 (RF) 的新型雪深融合方法.
- 为了产生一个更准确和更一致的空间分布的雪深在中国.
- 通过整合多源数据来改进雪地覆盖的时空表现.
主要方法:
- 开发了一个基于随机森林 (RF) 算法的雪深融合模型.
- 整合了五个SD产品 (WESTDC,ERA-Interim,CMC,GLDAS-NOAH,MERRA2) 作为输入数据.
- 纳入辅助数据,包括土地覆盖类型,森林覆盖,地理信息和表面粗度.
主要成果:
- 与单个产品相比,融雪深度数据 (RF-SD) 的准确性有所提高.
- 克林-古普塔效率 (KGE) 从0.21-0.64增加到0.73与现场观测相比.
- 根平均平方误差 (RMSE) 减少到5.1厘米,表明精度提高.
结论:
- 基于射频的融合方法有效地整合了多源SD数据,利用它们各自的优势.
- 这种方法显著提高了雪深估计的准确性和一致性.
- 改进的RF-SD数据对中国的水文和气候变化研究有价值.
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