TY - JOUR
T1 - Global multi-scale surface soil moisture retrieval coupling physical mechanisms and machine learning in the cloud environment
AU - Li, Zhenghao
AU - Yang, Qianqian
AU - Li, Jie
AU - Jin, Taoyong
AU - Yuan, Qiangqiang
AU - Shen, Huanfeng
AU - Zhang, Liangpei
N1 - This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3903403 and National Natural Science Foundation of China under Grant 42471414.
Publisher Copyright:
© 2025 Elsevier Inc.
PY - 2025/11/1
Y1 - 2025/11/1
N2 - Surface soil moisture (SSM) is a critical state variable for water cycle research, and the advances in satellite remote sensing technology have provided a novel means for acquiring large-scale SSM data. While satellite microwave remote sensing-based SSM retrieval has emerged as the dominant approach for global SSM product development, offering numerous advantages, it still faces significant challenges. These include the trade-off between model accuracy and generalizability, the limitations of applying uniform retrieval models across diverse environments, and the inherent complexity and computational demands of the retrieval process. To address these common issues in microwave remote sensing-based SSM retrieval studies, this study proposed a cloud-based intelligent retrieval framework for global high-accuracy SSM estimation. This framework integrated physical mechanisms with machine learning models to ensure robust generalization and high retrieval accuracy; additionally, a model selection module was incorporated to enhance the overall retrieval accuracy by providing environment-specific retrieval models. In an assessment based on global validation sites for 1-km resolution SSM retrieval, the proposed framework performed well, with an R value of 0.851 and an ubRMSE of 0.058 m3·m−3. Furthermore, to mitigate the computational resource demands and time-consuming of the retrieval process, the SSM retrieval framework was implemented in a cloud environment utilizing Google Earth Engine, Drive, and Colab, thereby enabling seamless online operation of the entire retrieval process. This cloud-based intelligent retrieval framework facilitates real-time point-scale SSM retrieval on a global scale and rapid production of high-accuracy SSM products at the regional scale (SSM products for China at 1 km resolution can be accessed via https://tinyurl.com/SSMproduct). The SSM retrieval framework can significantly contribute to agricultural, environmental, and other related fields, and serve as a reference for the retrieval of other environmental variables.
AB - Surface soil moisture (SSM) is a critical state variable for water cycle research, and the advances in satellite remote sensing technology have provided a novel means for acquiring large-scale SSM data. While satellite microwave remote sensing-based SSM retrieval has emerged as the dominant approach for global SSM product development, offering numerous advantages, it still faces significant challenges. These include the trade-off between model accuracy and generalizability, the limitations of applying uniform retrieval models across diverse environments, and the inherent complexity and computational demands of the retrieval process. To address these common issues in microwave remote sensing-based SSM retrieval studies, this study proposed a cloud-based intelligent retrieval framework for global high-accuracy SSM estimation. This framework integrated physical mechanisms with machine learning models to ensure robust generalization and high retrieval accuracy; additionally, a model selection module was incorporated to enhance the overall retrieval accuracy by providing environment-specific retrieval models. In an assessment based on global validation sites for 1-km resolution SSM retrieval, the proposed framework performed well, with an R value of 0.851 and an ubRMSE of 0.058 m3·m−3. Furthermore, to mitigate the computational resource demands and time-consuming of the retrieval process, the SSM retrieval framework was implemented in a cloud environment utilizing Google Earth Engine, Drive, and Colab, thereby enabling seamless online operation of the entire retrieval process. This cloud-based intelligent retrieval framework facilitates real-time point-scale SSM retrieval on a global scale and rapid production of high-accuracy SSM products at the regional scale (SSM products for China at 1 km resolution can be accessed via https://tinyurl.com/SSMproduct). The SSM retrieval framework can significantly contribute to agricultural, environmental, and other related fields, and serve as a reference for the retrieval of other environmental variables.
KW - Cloud environment
KW - Global scale
KW - Machine learning
KW - Microwave remote sensing
KW - Physical mechanism
KW - Surface soil moisture
UR - http://www.scopus.com/inward/record.url?scp=105010887966&partnerID=8YFLogxK
UR - https://www.sciencedirect.com/science/article/pii/S0034425725003323?via%3Dihub
U2 - 10.1016/j.rse.2025.114928
DO - 10.1016/j.rse.2025.114928
M3 - Journal article
AN - SCOPUS:105010887966
SN - 0034-4257
VL - 329
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 114928
ER -
