Strategies to Measure Soil Moisture Using Traditional Methods, Automated Sensors, Remote Sensing, and Machine Learning Techniques: Review, Bibliometric Analysis, Applications, Research Findings, and Future Directions

This review provides a detailed synthesis of various in-situ, remote sensing, and machine learning approaches to estimate soil moisture. Bibliometric analysis of the published literature on soil moisture shows that Time-Domain Reflectometry (TDR) is the most widely used in-situ instrument, while remote sensing is the most preferred application, and random forest is the widely applied algorithm to simulate surface soil moisture. We have applied ten most widely used machine learning models on a publicly available dataset (in-situ soil moisture measurement and satellite images) to predict soil moisture and compared their results. We have briefly discussed the potential of using the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission images to estimate soil moisture. Finally, this review discusses the capabilities of physics-informed and automated machine learning (AutoML) models to predict the surface soil moisture at higher spatial and temporal resolutions. This review will assist researchers in investigating the applications of soil moisture in the broad domain of earth sciences.

Automated summary

This review provides a comprehensive summary of different approaches, including in-situ, remote sensing, and machine learning, to estimate soil moisture. The analysis shows that Time-Domain Reflectometry (TDR) is the most commonly used in-situ instrument, remote sensing is the preferred application, and random forest is the widely applied algorithm. The review also discusses the potential of using NASA-ISRO Synthetic Aperture Radar (NISAR) mission images and physics-informed and automated machine learning models for higher resolution soil moisture prediction.