Field-based soil moisture measurements are cumbersome. Remote sensing techniques based on active or passive microwave data have limitations. This paper presents and validates a new method based on land surface energy balances using remotely sensed optical data ~including thermal infrared!, which allows field and landscape-scale mapping of soil moisture depth-averaged through the root zone of ex...

Association rule mining is one of the most important problems of data mining. Since remotely sensed data contains huge amounts of information, it’s a very potent area to discover useful rules. Compared to traditional Market “basket data”, remote sensed imagery has specific characteristics and also presents specific difficulties. Two problems need to be solved to apply association rule mining on...

This work includes using two-flow and full radiative transfer models to evaluate the conditions under which subsurface optical structure is detectable, and to develop an inversion model to determine the vertical structure of the IOP based on the presence of horizontal gradients in the spectral reflectance. We are also examining the role of the volume scattering function in determining the measu...

This chapter explores the feasibility and utility of using aerial photography or remotely sensed satellite imagery to identify geographic or " place " features that may be associated with criminal activity. It assesses whether or not variables derived from satellite images can provide surrogate relationships between land use and crime. A review of the remote sensing literature suggests two basi...

Cotton constitutes 81% of the world’s natural fibers. Accurate and rapid cotton yield estimation is important for trade agricultural policy development. Therefore, we developed a remote sensing index that can intuitively represent boll characteristics support by extracting pixels. In our study, Density open Pixels (DCPs) was extracted designing different indices combined with threshold segmenta...