The results section is structured as follows. First, the calibration procedure is carried out to determine the N0 values of survey one and two at the hygrophilous grassland and pine forest sites. Next, the average N0 values obtained from the calibration (surveys one and two), are used to produce the CRNR soil water maps for surveys three and four. The CRNR soil water maps produced (surveys three and four), will be validated against the hydro-sense soil water measurements (surveys three and four), which were obtained during each corresponding survey. The validation is composed of a visual observation on the CRNR’s ability to estimate spatial soil water, followed by a more statistical validation approach.
3.2. Cosmic Ray Neutron Rover Validation
The average N0
values 133.441 cpm (hygrophilous grassland) and 132.668 cpm (pine) were then used in Equation (6) to determine the CRNR soil water maps for surveys three and four at both sites. The CRNR soil water maps were then validated against the hydro-sense soil water maps for the corresponding surveys. First, general observations on soil water patterns and gradients across the landscape are discussed. Next, a statistical approach is used to validate the CRNR soil water estimates. The generated hydro-sense and CRNR soil water maps for the hygrophilous grassland site are illustrated in Figure 7
Maps produced using the hydro-sense data exhibited clear soil water patterns of the hygrophilous grassland survey site. The high soil water values in the landscape occur in the landscape depressions, which also coincide with a change in vegetation. There is temporal stability within the site, as the overall soil water patterns are constant and represented in both surveys, even though the locations of the hydro-sense points differ from survey to survey. Overall, the hydro-sense soil water maps differentiated the spatial patterns of soil water across the landscape.
The CRNR provided spatial estimates of soil water that represent the general soil water pattern of the landscape. Although the CRNR discriminates the overall soil water pattern of the area, the soil water gradients are slightly skewed (compared to the hydro-sense soil water gradients) due to the difference in measurement volume between the hydro-sense and the CRNR. The hydro-sense soil water map value range (survey three: 0.74 to 41.98 VWC (%) and survey four: 1.63 to 50.37 VWC (%)) is larger than that of the subsequent CRNR soil water value range (survey three: 2.08 to 13.48 VWC (%) and survey four: 9.59 to 22.37 VWC (%)) This is primarily due to the difference in support volumes of the two instruments used to produce the soil water maps.
The landscape peat depressions could not be driven over, without the risk of the vehicle getting stuck and were therefore navigated around (surveying along the border of the depression). Therefore, the high soil water values within the depression zones, which were measured by the hydro-sense (accessible by foot), were not effectively captured by the CRNR, which is due to the CRNR’s measurement footprint, as well as, the CRNR not measuring the neutron count directly over the depression zones, which is supported by the research findings by Köhli, et al. [13
], which stated that the signal strength of cosmic ray neutron technology is non-linear per radial distance and is extraordinarily sensitive to the first few meters of the instrument, resulting in almost half of the neutron intensity arising from the first 50 m from the instrument.
The generated hydro-sense and CRNR soil water maps for the pine site are illustrated in Figure 8
. The maps produced using the hydro-sense data exhibited clear soil water patterns of the pine survey site. The soil water pattern of the hydro-sense surveys was evident in the surveys, such that the change to hygrophilous vegetation, which occurred towards the centre of the site showed the expected increase in soil water indicative of these depressions. This was expected, as the pine trees are better adapted to the drier raised areas in study site where soil water values were relatively low. In addition, the pine trees probably use more water than the hygrophilous vegetation found in these depressions. The temporal soil water stability is also seen, as the general soil water patterns are maintained throughout the two surveys, even though the hydro-sense soil water points were not taken at the same locations within the site for the two surveys. The hydro-sense soil water maps differentiated the spatial patterns of soil water across the landscape.
The CRNR provided spatial estimates of soil water over the site and the soil water patterns were consistent to that of the hydro-sense soil water measurements. There is a difference in the soil water gradients between the hydro-sense and CRNR soil water maps, which is due to the difference in support volume of the two instruments used. The hydro-sense soil water map value range (survey three: 0.33 to 6.92 VWC (%) and survey four: 4.24 to 18.61 VWC (%)) is slightly larger than that of the subsequent CRNR soil water value range (survey three: 0.00 to 5.89 VWC (%) and survey four: 4.91 to 9.15 VWC (%)). This is primarily due to the difference in support volumes of the two instruments used to produce the soil water maps.
The surveys (three and four) were conducted in different seasons, which explains the difference in soil water ranges between hydro-sense surveys three and four and CRNR surveys three and four. Survey three was conducted in the dry season (winter), therefore the soil water values from the hydro-sense and CRNR are lower than that of subsequent survey four values, which was conducted in the wet season (summer).
The range of soil water values of the hydro-sense soil water maps were generally larger than that of the subsequent CRNR soil water range. This occurs as the hydro-sense has a small support volume and is therefore able to pick up maximum and minimum values within the landscape, especially on a small spatial scale. The CRNR has a smaller range of soil water values as it measures area-averaged soil water at an intermediate scale and therefore averages the fluctuations in soil water over the landscape. It is also important to note that more advanced interpolation techniques may reduce the mismatch in soil water ranges of the hydro-sense and CRNR estimates.
Intensive hydro-sense soil water surveys were conducted for the purpose of calibrating and validating the CRNR within a research framework. The CRNR would be calibrated less intensively for real world applications and if larger survey areas were mapped. Thus, the labour and time required to produce representative soil water maps of a survey area would potentially be less than that used in this research study.
The site boundaries of both survey areas are defined as the outer survey path of the CRNR. The CRNR at both sites has a measurement footprint of ~200 m. Therefore, the area 200 m beyond the border of the site influences the neutron count, when surveying along the border of the site. This could potentially result in differences in soil patterns between the hydro-sense and CRNR soil water maps, as the boundary measurements of the CRNR is affected by the conditions beyond the survey site, whilst the hydro-sense soil water map is an interpolation of the various sample points within the survey site.
The hygrophilous grassland survey area is narrow in width (~300 m) and is smaller than the diameter of the CRNR’s measurement footprint. Therefore, the CRNR would not be able to adequately differentiate the east-to-west soil water gradients of the survey area. This is further highlighted by the large changes in the soil water content over relatively small distances, within the hygrophilous grassland survey area.
The average VWC (%) from the hydro-sense and corresponding CRNR soil water maps were obtained and plotted, with the 1:1 line (dashed) Figure 9
. The graph shows that the average value of the CRNR soil water maps correlate well with that of the average value of the hydro-sense soil water maps and have an R2
value of 0.993. This shows that the calibration procedure carried out was adequate and that the calibration values are valid over different seasons, as survey three was conducted in the dry season and survey four was conducted in the wet season.
To further assess the capability of the CRNR to provide spatial estimates of soil water, the spatial soil water values of the hydro-sense and CRNR were compared. This was carried out by creating a spatial grid (fishnet), at a pre-defined resolution, and using this spatial grid to obtain pixel values from both the hydro-sense and CRNR soil water maps across the survey landscape (an example of the fishnet grid, at the pine site is illustrated in (Figure 10
For the purpose of this research, a 25 m grid size was chosen, according to the survey site sizes and the overall spatial characteristics of the soil water maps. The grid was overlaid onto the hydro-sense and CRNR soil water maps (which have identical spatial extents), and the pixel values were obtained. The pairs of pixel values obtained for each survey were then assessed.
First, the descriptive statistics were determined for each dataset. From Table 3
, the hydro-sense and corresponding CRNR survey mean values are similar. The range, standard error, kurtosis, skewness and standard deviation of the hydro-sense values are generally larger than the corresponding CRNR values.
Scattergraphs of the hydro-sense (x
-axis) against the CRNR soil water values (both extracted using the 25m grid) were plotted for both the hygrophilous grassland and pine site Figure 11
. The data from surveys three (red) and four (blue) were combined in each graph to cover a larger range of CRNR soil water values, as survey three was conducted in the dry season (winter) and survey four was conducted in the wet season (summer). The pine site has a higher R2
(0.793) between the hydro-sense and CRNR than the hygrophilous grassland (0.439).
The performance metrics were determined for each survey (Table 4
). With regards to the hygrophilous grassland surveys, the unbiased Root Mean Squared Error (ubRMSE) [37
] ranges from 5.220 (Survey three) to 5.400 VWC (%) (Survey four). With regards to the pine surveys, the ubRMSE ranges from 0.910 (Survey three) to 1.832 VWC (%) (Survey four). The pine surveys have a lower ubRMSE compared to the hygrophilous grassland surveys.
From the validation of the CRNR with hydro-sense soil water data, it is apparent that the CRNR can provide spatial estimates of soil water, which correlate well with the hydro-sense data. There are a few issues that have become apparent in this study. Firstly, a limitation of this study is that the soil water measurements from the 0–20 cm depth (hydro-sense) are used to calibrate the CRNR. It is worth noting again that the calibration used in this study may not hold for times without a hydrostatic profile. Therefore, the revised calibration approach, which uses a horizontal and depth weighting scheme is recommended, as it has been shown to improve the neutron to soil water estimates in these instances and would result in a decrease in the ubRMSE. Moreover, the low altitude of the site, as well as the number of neutron tubes (six) that this particular CRNR possesses, results in relatively low neutron counts within the one-minute interval, which results in a decrease in the instruments precision. This could be overcome by using a CRNR that consists of more neutron tubes; however, this is a financial limitation.
The shape of the hygrophilous grassland survey area does not allow for the CRNR to have enough distance to spatially measure the variability of soil water, as the survey area is too narrow. The long and narrow shape of the catchment also results in a large proportion of measurements being obtained on the border of the survey area, which is an issue, as the measurements along the border are affected by the area beyond the survey area that are not accounted for.
The large change in soil water over small spatial scales occurred in the hygrophilous grassland survey area, which was picked up by the hydro-sense measurements, but not adequately represented by the CRNR due to the footprint scale of the CRNR. The hygrophilous grassland had several depressions, which exhibited high soil water values in relation to the surrounding raised areas. These depressions were navigated around, to avoid the vehicle getting stuck. The CRNR is most sensitive to the area nearest to the sensor, therefore navigating around the depression does not adequately obtain the count rate that driving directly over the depression would.
The direction and speed at which a soil water gradient is approached affects the output CRNR map. When surveying, the neutron counts are recorded at one-minute intervals. When surveying across a soil water gradient, for example moving from a high soil water content to a low soil water content, the location at which the recorded value is noted, results in a bias, as the wet soil water value could be recorded beyond the wet boundary. This is highly possible as the transitions/gradients in soil water are not linear.