Soil moisture monitoring using unmanned aerial system

Quantification of high-resolution soil moisture (SM) is needed in numerous applications, yet its retrieval remains challenging even today. Unmanned aerial system (UAS)-based remote sensing provides a possibility of high spatial-resolution land surface data acquisition. This chapter aims at introducing SM data monitoring approaches using images from sensors mounted on UAS and provides practical guidelines for each method. Four representative approaches [i.e., apparent thermal inertia method, Kubelka–Munk (KM) method, simplified temperature–vegetation triangle method, and random forest (RF) model] that differ in the data from different spectral regions, requirements, and applicability are introduced. The thermal inertia model builds upon the dependence of the thermal diffusion on SM, which can be inferred from thermal infrared data. The KM model is a spectral model to retrieve the surface reflectance and then the SM using the available visible and near-infrared data. The simplified temperature–vegetation triangle model can be used to map surface SM and evapotranspiration based on the root–leave connection. In addition, we also introduce an SM downscaling method using the RF regression model. Overall, the thermal inertia method is recommended in bare soil or sparsely vegetated area, while the simplified temperature–vegetation triangle model is suitable for the vegetated area. The KM model is an ideal choice to generate higher resolution SM when high-quality spectral data are also available representing the power of the reflectance data. If multiple surveys are required, the RF model can be an alternative to estimate high-resolution surface SM with the help of coarse resolution satellites-based SM data and land surface features.