标题: Seamless global daily soil moisture mapping using deep learning based spatiotemporal fusion

作者: Jiang, MH (Jiang, Menghui); Qiu, T (Qiu, Tian); Wang, T (Wang, Ting); Zeng, C (Zeng, Chao); Zhang, BX (Zhang, Boxuan); Shen, HF (Shen, Huanfeng)

来源出版物: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 卷: 139 文献号: 104517 DOI: 10.1016/j.jag.2025.104517 Published Date: 2025 MAY

摘要: Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to tackle this challenge, we propose a deep learning-based spatiotemporal fusion framework to integrate the two data sources and generate a global soil moisture product with high spatial continuity and accuracy. Specifically, we leverage the high accuracy of the Soil Moisture Active and Passive (SMAP) microwave soil moisture data and the spatiotemporal continuity of the Noah assimilation soil moisture data. The proposed model employs a deep residual cycle GAN (DrcGAN) to capture the nonlinear complementary spatiotemporal features between the SMAP and Noah data, generating a seamless global daily product at a 36-km resolution, spanning April 4, 2015, to November 26, 2023, referred to as STSG-SM. Various validation methods, including spatial pattern analysis, time-series comparison, and in-situ validation, are utilized to assess the effectiveness and reliability of the product. In comparison to the selected in-situ measurements, the STSG-SM dataset (original SMAP-P36) exhibits a bias of 0.0230 m3/m3 (0.0243 m3/m3), R of 0.8388 (0.8405), RMSE of 0.0629 m3/m3 (0.0628 m3/m3), and ubRMSE of 0.0585 m3/m3 (0.0579 m3/m3), indicating that the proposed method sustains the high precision of satellite-retrieved soil moisture and demonstrates strong consistency with the in-situ measurements.

作者关键词: Soil moisture; Seamless reconstruction; Spatiotemporal fusion; deep residual cycle GAN

KeyWords Plus: PRODUCTS; ASSIMILATION; MICROWAVE

地址: [Jiang, Menghui; Qiu, Tian; Zeng, Chao; Zhang, Boxuan; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wang, Ting] Hubei Spatial Planning Res Inst, Wuhan 430079, Peoples R China.

[Shen, Huanfeng] Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China.

[Shen, Huanfeng] Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat, Wuhan 430079, Peoples R China.

通讯作者地址: Shen, HF (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn

影响因子：7.6