Artificial Intelligence Techniques Enabled Soil Moisture Estimation Frameworks Using Remote Sensing Satellite Images: Challenges and Future Directions‐ Review

ABSTRACT Forecasting soil moisture is critical for keeping groundwater levels stable, monitoring droughts, and assisting agricultural productivity. Surface soil moisture has a tremendous impact on both the environment and society. To provide proper soil moisture, the right tools are required. Gravimetric, physical, and empirical models produce reliable results, but they are generally context‐dependent and inappropriate for large‐scale investigations. Remote sensing has developed as a credible technology for estimating large‐scale soil moisture levels. However, various obstacles exist when getting soil moisture data using remote sensing, including the availability and precision of data sources. The spatial and temporal limits of many remote sensing sources, such as microwave and optical sensors, combined with environmental conditions, provide considerable feasibility issues. As a result, a robust model capable of accurately capturing both linear and nonlinear connections between multiple surface soil variables is critical. Recently, AI approaches have been identified as promising options for managing complicated factors in this domain. This review paper investigates the use of several AI algorithms for estimating soil moisture content (SMC). It focusses on AI‐enabled frameworks built with remote sensing satellite imagery. In addition to including in situ observations, the study discusses the advantages of AI approaches, the issues they solve, and provides a detailed description of the integration of microwave, optical, and combination (synergistic) data sources. This paper also addresses the most common AI approaches applied with various types of remote sensing data and the results they produced. By exploring the strengths and technical problems associated with diverse data sources, this work hopes to help researchers make wise choices about data selection and model construction. Finally, the proposed future research directions are likely to assist emerging researchers in broadening the scope of this critical topic in a way that corresponds with future demands. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Technologies > Prediction