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To meet the challenge of food security in the twenty-first century, global agricultural output must be increased. This will put pressure on already strained surface and groundwater resources. The incorporation of new techniques and technologies into agricultural resource management has the potential to improve the ability of farmers, scientists, and policymakers in assuring food security. The Soil and Water Management and Crop Nutrition Subprogramme of the Joint FAO/IAEA Division focuses on the development of improved soil, water, and crop management technologies and practices for sustainable agricultural intensification through the use of nuclear and conventional techniques.

When designing experiments, environmental scientists face the challenge of how to accurately represent nature. The idea of sampling patterns and strategies truly reflecting research variables is intrinsic to scientific pursuits. This is particularly true in environmental science due to the complex heterogeneity present in nature. It is vitally important in most studies for researchers to account for natural variations in soil, air, water, and vegetation that can change in space and time. Many strategies focus on the use of strategically placed transects or plot-based sampling campaigns designed to include as many aspects of a particular variable as possible within a study area. Determining how many samples must be taken, whether they are of soil, plant matter, water, etc., depends entirely on the balance of time, effort, and cost while in the field. As a rule of thumb, the more samples that can be gathered correctly, the more trustworthy eventual results will be. Unfortunately, environmental sampling can be time-consuming and expensive depending on its location or the procedures for its procurement. This is one of the reasons why the use of satellite-based remote sensing, computer modeling, and proximal sensing has gained popularity within the scientific community in recent decades. However, the heterogeneity and scale of the environment again make large spatial-scale research difficult and often require in situ validation campaigns to ensure data quality. This is one of the main advantages of the use of the CRNS, due to the significant spatial and temporal variations soil moisture can exhibit.

Healthy green vegetation absorbs red and blue light wavelengths preferentially for use in photosynthesis. Green light (wavelength 545–565 nm), however, is mostly reflected leading to the green appearance of living biomass. This characteristic coincides with interactions outside the visible portion of the electromagnetic spectrum. Near-infrared (NIR, wavelength 841–876 nm) light also interacts with healthy vegetation in a slightly different way. The presence of chlorophyll in green vegetation does not utilize green light due to properties of the molecules themselves and the harnessing of energy by the plant. NIR light is reflected mainly due to the physical structure of healthy leaf tissue (see Fig. 3.1 for a representation of these phenomena). These characteristics are not static in time, as plants continue to develop and transition through their life cycle; they eventually loose leaf structure and chlorophyll concentrations for many reasons including seasonal changes, disease, age, water scarcity, etc. These realities change the relationship between vegetation and light. This is particularly apparent in agricultural systems where plants undergo a predictable transition from planting to maturity and eventually senescence at the end of the growing season. This senescence is characterized mainly by a loss of chlorophyll, a collapse of leaf structure, and an investment by the plant of resources into the production of fruiting bodies and grain. These principles are the basis for much of remote sensing within agricultural ecosystems.

The CRNS functions at its most fundamental level as a detector of the hydrogen within its area of influence (circle of radius ~ 250 m). As such, hydrogen other than that within the water molecules in the soil is detected. A series of calibration equations have been developed to quantify and eliminate these sources of hydrogen so that the signal of soil moisture can be isolated [1–6]. McJannet et al. [7] demonstrated that soil moisture is the largest contributor of hydrogen to the signal of the CRNS with growing biomass contributing only slightly. These data show that in an agricultural environment, the most significant source of error comes in the form of soil lattice water (i.e., hydrogen molecules integrated into mineral structures and bound water between mineral grains not released at oven drying temperatures of 105 °C for 24 h) and from water vapor in the atmosphere. Despite this, growing biomass if left unquantified remains a source of uncertainty that must be addressed, partly in fast-growing agricultural crops. Much of the current and past research into the CRNS in agricultural environments focuses on its use as a sensor of soil moisture. However, there have also been investigations into its use as a tool for estimating growing crop biomass itself [8, 9]. Note that the biomass signal is fairly small and challenging to remove from the soil moisture signal and inherent noise in the neutron counts. This requires use of large detectors (i.e., high count rates on the order of 5000 to 10,000 to minimize uncertainty) and certain biomass detection limits (i.e., on the order of 0.5 kg/m). Nevertheless the technique is theoretically sound [6] and an area of active research.