基于Sentinel-2与机器学习的河流流量估算研究——以唐乃亥水文站为例

National Remote Sensing Bulletin 2026 Open access

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Authors: Wenjie Yin, Wang Xuelei, WANG Chen, WANG Hang, HUANG Caisheng, ZHAO Ruixue, Meng Fanle, LIU Jinxiu
DOI: 10.11834/jrs.20265294
Cited by: 0
References: 14

Abstract

3. 中国地质大学(北京)信息工程学院,北京 100083NSE、RMSE 和 MAE 分别为 0.96，0.89，172.81m³/s 和 147.33 m³/s。SHAP 方法表明水面宽度在流量监测模型 中具有最显著的贡献 (189.02)，其次为土壤湿度 (145.11)和温度 (97.41) 。本研究证实了联合卫星遥感和机器

Fields of Study & Subjects

Hydrological Forecasting Using AI Hydrology and Watershed Management Studies Soil Moisture and Remote Sensing

Publication details

Journal / source
National Remote Sensing Bulletin
Volume
30
Issue
7
Pages
2070–2082
Publication date
1 January 2026
Language
EN