Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models

Soil Moisture (SM) monitoring is crucial for various applications in agriculture, hydrology, and climate science. Remote Sensing (RS) offers a powerful tool for large-scale SM retrieval. This paper explores the advancements in RS techniques for SM estimation. We discuss the applications of these techniques, along with the advantages and limitations of traditional physical models and data-driven Machine Learning (ML) based approaches. The paper emphasizes the potential of combining ML and physical models to leverage the strengths of both approaches. We explore the challenges associated with this integration and future research directions to improve the accuracy, scalability, and robustness of RS-based SM retrieval. Finally, the paper also discusses a few issues such as input data selection, data availability, ML complexity, the need for public datasets for benchmarking, and analysis.