  The microwave imaging system(MIS) stands out among prominent imaging tools
for capturing images of concealed obstacles. Leveraging its capability to
penetrate through heterogeneous environments MIS has been widely used for
subsurface imaging. Monitoring subsurface drip irrigation(SDI) as an efficient
procedure in agricultural irrigation is essential to maintain the required
moisture percentage for plant growth which is a novel MIS application. In this
research, we implement a laboratory-scale MIS for SDI reflecting real-world
conditions to evaluate leakage localization and quantification in a
heterogeneous area. We extract a model to quantify the moisture content by
exploiting an imaging approach that could be used in a scheduled SDI. We employ
the subspace information of images formed by back projection and Born
approximation algorithms for model parametrization and estimate the model
parameters using a statistical curve fitting technique. We then compare the
performance of these imaging techniques in the presence of environmental
clutter such as plant roots and pebbles. The proposed approach can well
contribute to efficient mechanistic subsurface irrigation for which the local
moisture around the root is obtained noninvasively and remotely with less than
20% estimation error.
