Coupling Non-destructive INS Belowground Measurements with Remote Sensing.

            It is widely recognized that the plethora of instruments available for aboveground measurements, spanning from monitoring soil emissions to remote satellite sensing, operated individually or jointly yield extensive amounts of information from which we can draw inferences about belowground processes. Presently, no technology for measuring belowground processes precludes our interfering with those same processes that we wish to monitor; they remain invisible to the human eye. The complexity of the three-phase soil matrix renders it impenetrable to most sensing radiations, except microwaves, high-energy gamma rays and neutrons. Furthermore, both gamma rays and neutrons provide highly specific elemental information about the soil’s composition non-destructively while interrogating large soil masses, >200 kg. We constructed a unique system based on inelastic neutron scattering (INS) induced gamma ray spectroscopy, and tested it in a field in a stationary mode, and during continuous scanning. The concept underlying the INS system fundamentally differs from the state-of-the-art conventional wisdom wherein discrete samples taken from a field are analyzed in the laboratory to provide point measurements from which to predict a mean field value. In contrast, in a stationary mode, the INS’s response is a weighted integral over a large volume; in a scanning mode, it provides a weighted mean value for the field.

Remote observations of soil reflectance in the visible and shortwave infrared regions have gained popularity firstly because of the ubiquitous presence of soils over the earth’s land surface, and secondly, due to their value in pedological research that encompasses the quantitative characterization, classification, and mapping of soils. We propose to directly couple quantitatively the aboveground reflectance measurements with belowground measurements, for example, of carbon, on an equal pixel-size basis. Their direct correlation will strengthen tremendously the value of remote sensing.