AI in soil moisture remote sensing

Abstract

Soil moisture, a pivotal component of the hydrological cycle, exerts a profound influence on land surface exchange processes, but its spatial variability poses challenges for large-scale field observations, increasing reliance on satellite-based retrievals. However, spaceborne estimates face limitations due to model uncertainties and sensor-related constraints. Recent advances in artificial intelligence (AI) offer promising alternatives to traditional methods by enabling data-driven estimation of soil moisture without strong physical assumptions. Thus, a critical review of emerging AI-based soil moisture retrieval methods with respect to their advantages and disadvantages is vital to ensure the best utilization of such tools for soil moisture sensing, especially with novel sensors and data constantly being generated. In this comprehensive review, we furnish the first structured overview of AI methods and their applications in soil moisture retrievals from remote sensing. AI is able to enhance soil moisture retrieval by learning complex (highly nonlinear) relationships between satellite observations and ground reference data, to support time series reconstruction by filling gaps in data sets, to estimate subsurface soil moisture conditions from surface signals and auxiliary inputs, to enable spatial scaling by translating soil moisture estimates across different resolutions using multi-resolution data, to predict temporal dynamics as a soil moisture forecast, and to contribute to broader assessments of the water cycle and beyond by integrating soil moisture with further hydrological variables. Future directions for each method are also identified to address the scientific challenges of soil moisture retrieval and help focus the research community on the key open questions in the new era of rapidly expanding AI applications.