数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (5): 18-33.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.05.002

doi: 10.11871/jfdc.issn.2096-742X.2026.05.002

• 专刊：“东方”超级计算系统，助力青年科学家科技创新 • 上一篇    下一篇

联合风云三号B星与D星微波辐射计的全球长时序土壤水分反演研究

井润生1,2(),白瑜1,*(),曹祎芃1,张浩杰2,段潘3,赵天杰1,薛华柱4,郑东海2,徐榕焓5   

  1. 1 中国科学院空天信息创新研究院， 遥感与数字地球全国重点实验室， 北京 100101
    2 中国科学院青藏高原研究所， 青藏高原地球系统与资源环境全国重点实验室；国家青藏高原科学数据中心， 北京 100101
    3 河南理工大学郑州高等研究院， 河南 郑州 451464
    4 河南理工大学测绘与国土信息工程学院， 河南 焦作 454003
    5 中国气象局中国遥感卫星辐射测量和定标重点开放实验室；国家卫星气象中心（国家空间天气监测预警中心）， 北京 100081
  • 收稿日期:2026-05-28 出版日期:2026-10-20 发布日期:2026-10-20
  • 通讯作者: *白瑜（E-mail: baiyu@radi.ac.cn）
  • 作者简介:井润生，中国科学院空天信息创新研究院，科研助理，现为中国科学院青藏高原研究所博士研究生，研究方向为微波遥感。
    本文主要承担工作为论文构思、数据处理、模型构建与论文撰写。
    JING Runsheng was a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences, and is currently a Ph.D. candidate at the Institute of Tibetan Plateau Research, Chinese Academy of Sciences. His research interest is microwave remote sensing.
    In this paper, he is mainly responsible for paper conceptualization, data processing, model construction and article writing. E-mail: jingrs2000@163.com|白瑜，中国科学院空天信息创新研究院，助理研究员，研究方向为微波遥感等。
    本文主要承担工作为研究设计、方法指导与论文修改。
    BAI Yu is an assistant researcher at Aerospace Information Research Institute, Chinese Academy of Sciences. His research interests include microwave remote sensing, etc.
    In this paper, he is mainly responsible for research design, methodology guidance and article revision. E-mail: baiyu@radi.ac.cn
  • 基金资助:
    国家自然科学基金青年科学基金项目（C类）(42501450);东方之星青年科学家创新基金(DFZX202519);风云三号03批气象卫星工程地面应用系统生态监测评估应用项目（第一期）(ZQC-R22227)

Global Long-Term Soil Moisture Retrieval Combining Fengyun-3B and Fengyun-3D Microwave Radiometers

JING Runsheng1,2(),BAI Yu1,*(),CAO Yipeng1,ZHANG Haojie2,DUAN Pan3,ZHAO Tianjie1,XUE Huazhu4,ZHENG Donghai2,XU Ronghan5   

  1. 1 National Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
    2 State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences; National Tibetan Plateau Data Center, Beijing 100101, China
    3 Zhengzhou Institute for Advanced Research, Henan Polytechnic University, Zhengzhou, Henan 451464, China
    4 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan 454003, China
    5 Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration; National Satellite Meteorological Center (National Center for Space Weather), Beijing 100081, China
  • Received:2026-05-28 Online:2026-10-20 Published:2026-10-20

RichHTML

1

PDF

6

摘要：

【目的】针对风云三号（FY-3）B星与D星系统性差异导致的长时序土壤水分衔接问题，构建跨星一致的全球土壤水分数据集。【方法】提出基于微波发射率特征的长短期记忆网络反演方法，以多频双极化发射率时序为输入、MCCA SMAP土壤水分为训练目标，分别建立FY-3B和FY-3D反演模型，生成2012—2025年25 km全球日尺度土壤水分产品。【结果】地面网络验证结果表明，反演产品与青藏高原那曲、五道梁观测网络实测土壤水分一致性较高，相关系数（R）超过0.91，无偏均方根误差（ubRMSE）低于0.039 m3/m3。【局限】受可用地面观测资料限制，密集森林区产品不确定性仍需后续原位观测资料进一步评估。【结论】相比FY-3 L2土壤水分产品，本研究提高了FY-3B/3D反演结果的一致性与稳定性，可为国产卫星长时序水文监测提供数据支撑。

关键词: 遥感, 土壤水分, 风云三号（FY-3）, 长短期记忆网络（LSTM）, 长时序数据

Abstract:

[Objective] To address the long-term soil moisture continuity problem caused by systematic differences between Fengyun-3 (FY-3) B and D satellites, this study develops a cross-satellite consistent global soil moisture dataset. [Methods] A long short-term memory (LSTM) retrieval method based on microwave emissivity features is proposed. Multi-frequency dual-polarization emissivity time series are used as inputs, and MCCA SMAP soil moisture is used as the training target. Separate retrieval models are established for FY-3B and FY-3D to generate a global daily soil moisture product at 25 km resolution for 2012-2025. [Results] Ground-network validation shows good agreement between the retrieved product and in situ soil moisture observations from the Naqu and Wudaoliang networks on the Tibetan Plateau, with correlation coefficients (R) exceeding 0.91 and unbiased root mean square errors (ubRMSE) below 0.039 m3/m3. [Limitations] Due to the limited availability of in situ observations, product uncertainties over dense forested areas require further assessment using additional in situ measurements. [Conclusions] Compared with the FY-3 Level-2 (L2) soil moisture products, this study improves the consistency and stability of FY-3B/3D retrievals and provides data support for long-term hydrological monitoring using domestic satellites.

Key words: remote sensing, soil moisture, Fengyun-3 (FY-3), Long Short-Term Memory network (LSTM), long time series data