1. Introduction
Proximal hyperspectral spectroscopy in the visible near-infrared and shortwave infrared (VNIR-SWIR) spectral region (400–2500 nm) of soils presents a non-destructive and efficient approach for estimating numerous soil properties in a laboratory setting. The establishment and development of the calibration–validation strategy [
1] for analyzing the correlation between spectra and certain physical or chemical attributes have led to the development of a spectral quantitative estimation of soil properties [
2,
3,
4,
5,
6,
7,
8]. Despite the desired soil property estimations achieved with the proximal VNIR-SWIR hyperspectral measurement approach, a recognized limitation of this approach is the lack of a common laboratory protocol [
9,
10,
11].
Several key variables in performing the lab-based VNIR-SWIR spectral measurement procedure on soil samples significantly influence their spectra quality and subsequent estimation accuracy to soil properties brings challenges to establish the agreed-upon protocol for soil spectral measurement [
9,
10,
11,
12,
13,
14]. Variability in the sample collection, sample pretreatment, laboratory environment, and the spectrometer condition may lead to significant differences in spectra quality and subsequently hamper model sharing and comparison [
10,
12,
13,
14]. Hence, investigating and optimizing the key factors in the spectral measurement procedure are essential to develop an agreed-upon protocol [
15]. However, few studies have comprehensively investigated these factors, and their results have been inconsistent [
15,
16,
17,
18,
19].
First, the existing VNIR-SWIR spectroscopy laboratory procedure usually requires carrying out the measurement with a high-intensity contact probe or a halogen lamp to illuminate the darkroom environment [
15]. However, the different laboratory conditions and the special requirements of different operators (e.g., some laboratories prohibit the probe coming into contact with soil samples to avoid cross-contamination between samples and instruments) introduce the possibility of light interference in the measuring course. Second, soil temperature plays an important role in controlling the soil spectral characteristics [
3,
9]. In most cases, the spectral measurements are recommended to be carried out at room temperature [
10,
11,
15]. However, few studies have investigated the correlation between soil temperature and corresponding spectral characteristics until now; moreover, they have not proposed a common standard [
18,
20]. Third, soil moisture is a major chemical chromophore that significantly influences the soil’s spectral characteristics [
3,
21,
22,
23]. The moisture variation in soil samples caused by damp environmental storage leads to significant variations in soil spectra. In most cases, studies have suggested using an air-dried or oven-dried procedure in sample pretreatment to enhance the spectral quality [
9,
15]; nevertheless, studies have demonstrated that in some cases the wet samples did not show significantly degraded results in spectral analysis [
16,
24]. Additionally, the crushing and sieving pretreatment interferes significantly with the soil texture, which generates particle size differences in soil samples. It also interferes with the radiative transfer process and further influences the soil spectral characteristic significantly [
3,
21,
25,
26]. Although previous studies have demonstrated that the spectra quality of soil samples and corresponding estimation accuracy are enhanced with fine sieving, the sieving levels that derived the optimum results differed [
5,
15,
26,
27,
28].
Several reliable protocols, such as the CSIRO, TAU, CGS, and CULS [
10,
11,
15], have been developed for standardizing the proximal VNIR-SWIR spectral measurement procedure. However, with the rise in the number of spectral measurement protocols, as well as the generation of regional and national soil spectral libraries, the variation in the generated datasets has obviously not been corrected [
10]. Recent studies have attempted to standardize the spectra data obtained from different laboratory procedures into a uniform protocol from the perspective of mathematical calculation methods [
10,
11,
15]. Nevertheless, no one has comprehensively studied unifying the key factors in the protocol from the perspective of a sample processing and spectral measurement procedure. Moreover, the requirements of existing protocols have also not been perfected in common applications because of the limitations of the laboratory environment, instrument conditions, and different operational habits [
9,
29,
30]. Therefore, this study was carried out to investigate the variation in spectra quality and corresponding estimation accuracy generated by variations in key factors in the spectral measurement procedure with the aim of quantifying these variations. Moreover, we provide optional guidance for further proximal VNIR-SWIR spectral measurements and theoretical support for optimizing the protocols.
To this aim, we designed and performed four groups of control experiments in the standard spectral laboratory at Jilin University, China, to illustrate the spectral response under the influence of four independent variables: light interference, soil temperature, soil moisture, and soil particle size. Moreover, we chose the soil organic carbon (SOC) content, which is an important property of soil, as an indicator when performing the partial least squares regression (PLSR) calibration–validation to show the estimation ability of the spectra obtained from different groups of control experiments. Moreover, our study also considered employing spectral transformations, which have been applied in previous studies [
31,
32,
33,
34,
35,
36,
37], for PLSR calibration–validation to provide conclusions that could be employed in future comparisons.
4. Discussions
First, as a well-accepted method of soil spectral measurement in most laboratories over the world [
15], measurements with a high-intensity contact probe usually require close probe–soil contact conditions [
6,
8,
10,
15]. This was consistent with the results of our study, where the close contact measurement indeed obtained the optimum estimation accuracy in validation (
Table 3 and
Figure S5). However, the different laboratory conditions and the special requirements of different operators (e.g., some laboratories prohibit the probe coming into contact with soil samples to avoid cross-contamination between samples and instruments) introduced the possibility of light interference in the measuring course. Correspondingly, our study presented a theoretical response to this problem by demonstrating that the close-non-contact measurement could also obtain a relatively lower but acceptable accuracy, even if under the illuminating interference environment. We also demonstrated that the large gap non-contact measurement could lead to a serious reduction in the spectra quality as well as the estimation accuracy. Hence, if there are no special requirements for experiments, we recommend using contact measurement to obtain the optimal spectra quality as far as possible.
Second, the soil moisture control experiment demonstrated that soil moisture interferes the most significantly in the soil spectra quality of the four factors: The result showed a common decrease in spectral reflectance with increasing moisture content, which was consistent with previous studies [
21,
22,
48]. Moreover, enhancement of the water absorption range in the SWIR domain with the increasing soil moisture [
49,
50,
51] was distinctly detected in our experiment. The estimation accuracy significantly declined with the increase in soil moisture, which was consistent with most cases in previous studies [
48,
52]. Water in soil has been demonstrated to have a significant effect on the H
2O expression spectral bands, which masks the major chemical chromophores in soils [
3,
53] and leads to unstable statistical characteristics of the modeling spectra. Moreover, this leads to poor accuracies in estimation results. Hence, we suggest that complete drying of soil samples is necessary in standard laboratory-based proximal spectral measurements.
Third, the spectral reflectance of soil samples showed a general increase in the whole VNIR-SWIR spectral region with the decrease in soil particle size, a stable tendency observed in
Figure 12 and
Figure S16. Moreover, we demonstrated that the validation accuracies of the models were effectively enhanced by finely sieving, which was consistent with previous reports that indicated the transmission of light through soil samples would be affected and derived different spectral reflectance characteristics when the particle size changed, which can lead to significant variations in the estimation accuracies [
54,
55,
56]. However, the optimum particle size suggested in previous reports differed (ranging from 0.88 to 2 mm) from our result [
5,
15,
26,
27,
28]. One reason for this difference might be the effects of the different test methods used to obtain the modeling parameters. For instance, the laboratory methods for testing the SOC content are different, ranging from the dry combustion [
9,
16,
18,
56] to Walkley’s rapid method [
54]. Different test methods require different soil particle sizes. For instance, this study employed the potassium dichromate volumetric method, which required the particle size of the employed soil sample to be less than 0.8 mm [
39], which could explain why the particle sizes of soil samples that produced the optimum estimation accuracies were consistent with previous studies (less than 0.1 mm) employing the same methods [
28,
57]. In other words, the principles of existing modeling methods almost all depend on establishing the statistical relationship between the soil spectra and corresponding properties. Employing the same sieving level for geochemical tests and spectral measurement of the soil samples, in the meantime, helps to maintain the consistency of the target when establishing statistical relations. Hence, we suggest that preparing soil samples with a particle size under 1 mm can be accepted. Moreover, it is not necessary to require a particular unified sieving level in the development of a laboratory-based proximal spectral measurement protocol, but this should depend on different targets and methods.
Last, the soil temperature control experiment did not establish a distinct correlation between the soil temperature and the corresponding estimation results. In most statistics, the estimation accuracy showed a lower R
2 and a higher RMSE with the increase in soil temperature. However, this result was not obvious. We did not obtain consistent extension in the group of models developed with the spectral reflectance (R), in which the soil samples at 50 °C reached an even higher R
2c with an ideal R
2v than the other three controlled conditions (20, 30, and 40 °C) (
Table 4 and
Figure S10), as the previous study demonstrated [
18]. According to the results of the additional experiment in our study (
Section 3.6), we suppose that one factor causing the varying results might be the temperature disturbance by the contact probe.
Figure 13b shows that the probe kept a 50 °C working temperature after 24 min of warming up. This means that even though the soil samples were processed to a uniform temperature for measurement, when the sample came into contact with the probe, the surface was heated by the 50 °C bulb, resulting in an uncontrollable temperature gain that might lead to the unstable variation in the obtained spectra. Overall, the estimation results did not demonstrate obvious regular variation with the change in the soil temperature; the estimation accuracies of the models developed with the spectral reflectance of soil samples under four temperature conditions (20–50 °C) were all acceptable (0.902 ≤ R
2c ≤ 0.947; 0.798 ≤ R
2v ≤ 0.873). Hence, we suggest that in order to ensure the efficiency of the spectral measurement procedure, the method of employing the room temperature samples for spectral measurement should continue to be popularized when developing standard laboratory protocol and future studies [
10,
11,
15]. Moreover, the results of the additional experiment also demonstrated that the spectrometer and the contact probe bulb reached the stable working temperature in about 30 min, which also presents a guideline for specifying the warm-up time of the devices from the perspective of users.
5. Conclusions
For this study, we designed and performed control experiments to investigate the influence on the soil spectra quality and subsequent estimation accuracy of four key factors in a proximal spectral measurement procedure in a laboratory setting. Control experiments were performed in the standard spectral laboratory at Jilin University (JLU), China, which has a constant laboratory environment and devices needed to support the control experiments. Among the four key factors, light interference, soil moisture, and soil particle size demonstrated obvious influences on soil spectral characteristics and subsequent estimation accuracies. Furthermore, soil moisture interfered the most significantly in the soil spectra quality, with an evident decrease in spectral reflectance and significant decline in the estimation accuracy with increasing moisture content. However, the soil temperature control experiment did not obtain the ideal result and could not determine a distinct correlation between the soil temperature and the corresponding estimation results. From the results of the control experiments and comparative analysis, the conclusions presented below can be drawn to guide optimizing the process of laboratory-based proximal soil spectral measurements to derive a higher spectra quality and corresponding ideal estimation accuracies.
The soil–probe contact measurement derives the optimum spectra quality and estimation accuracy; however, close-non-contact measurement can also obtain a relatively lower but acceptable accuracy, even if under the illuminating interference environment. The complete drying procedure of soil samples is necessary in soil sample processing. Sieving below 1 mm particle size can produce soil samples with a high spectra quality and ideal estimation accuracy; moreover, specific sieving levels in further studies should be designed based on the different research objects and the referenced geochemical test methods. The method of employing the room temperature samples for spectral measurement can continue to be promoted in future studies; moreover, a 30-min warm-up time for the spectrometer and contact probe was demonstrated to be effective by the additional temperature observation experiment.
Despite carrying out this investigation and optimizing the four key factors in soil proximal spectral measurement, there is still room for further research to comprehensively investigate additional factors in the spectral measurement procedure that will influence the spectra quality and subsequent soil property estimation. Furthermore, as we mentioned in
Section 3.4, the geochemical references are usually derived from different laboratory methods, and further comparative study to characterize their influence on the corresponding estimation accuracies can also promote the development of spectral measurement protocols. Moreover, although the reference factor used in this research, the SOC content, is an important soil property, the estimation results might be different when other soil properties are employed. Hence, the conclusions of this study must be confirmed by further detailed applications.