The passive microwave remote sensing in soil moisture retrieval: Products, models, applications and challenges

Soil moisture (SM) is a pivotal component of the global water cycle and a critical environmental parameter in terrestrial carbon cycling, significantly influencing energy exchange, vegetation dynamics, and ecosystem productivity. Its spatial and temporal distribution offers critical insights for climate models and water resource management, particularly in predicting extreme weather events and optimizing agricultural practices. Passive microwave remote sensing, characterized by its all-weather capability, extensive coverage, and high efficiency, has emerged as the primary method for SM estimation. Over recent decades, significant progress in microwave SM retrieval has been driven by technological innovations and increased satellite data availability, resulting in the development and widespread adoption of passive microwave SM products. Notwithstanding the significant progress in microwave SM retrieval techniques, a comprehensive literature review on passive microwave SM, encompassing research advancements, retrieval methods, application hotspots, shortcomings, and future prospects, remains notably absent. To address this gap, this study undertakes a systematic and in-depth exploration of passive microwave SM research. Initially, the fundamental principles of passive microwave SM retrieval are introduced, establishing the groundwork for understanding the underlying mechanisms. Subsequently, the primary retrieval methods are reviewed, including physically-based radiative transfer models and machine learning algorithms, with a focus on their respective strengths and limitations. Additionally, the study introduces the current mainstream passive microwave SM products, detailing their specific attributes such as spatial resolution, temporal frequency, and available time. Following this, a thorough classification of the specific applications of existing passive microwave SM products is conducted through the VOS (Visualization of Similarities) viewer-based keyword co-occurrence technique. The findings indicate that these products are predominantly utilized in environmental science and ecology for applications such as data assimilation, drought monitoring, rainfall estimation, and flood forecasting. Moreover, passive microwave SM data are increasingly being applied in fields related to human activities, such as disaster early warning, military simulation, and vehicle speed modeling, underscoring their versatility and expanding relevance in both natural and anthropogenic contexts. Despite the notable progress in these areas, persistent challenges such as data gaps, lower spatial resolution, and degraded accuracy caused by external environmental factors continue to limit the full potential of passive microwave SM products across diverse application scenarios. In the end, this study offers a forward-looking perspective on passive microwave SM research, with the goal of addressing current challenges and advancing the development of microwave SM products. In conclusion, the study seeks to contribute to the continued evolution of passive microwave SM retrieval and application, thereby strengthening the scientific foundation and practical effectiveness of soil and water conservation efforts.