A review of recent developments related to soil hydraulic property estimation using remote sensing is presented. Several soil hydraulic parameter estimation techniques using proximal-, air-, or satellite-based remotely sensed soil moisture, land surface temperature, and/or evapotranspiration time series have evolved over the past decades. In particular, microwave remote sensing of near-surface soil moisture has played a key role in this respect. Inverse modeling, regression techniques, data assimilation methods by utilizing soil–vegetation–atmosphere transfer models, genetic algorithm based optimization, and uncertainty quantification using ensemble based approaches under synthetic and field conditions have been developed and adapted over these years. In this context multiple approaches for estimating effective soil hydraulic parameters at different spatial scales (e.g., point to remote sensing footprints) by various scaling methods are also summarized. Methods reviewed include traditional bottom-up approaches, such as homogenization of local scale properties up to footprint scale, as well as newer top-down approaches, such as use of inverse modeling of remotely sensed soil moisture or temperature using genetic algorithms, Markov Chain Monte Carlo simulations with data assimilation, and multi-scale Bayesian neural networks. While new remote sensing platforms provide powerful tools and techniques for noninvasive soil hydraulic property estimation of the vadose zone at multiple scales, current limitations including vision for future developments are also summarized.