Comparison of Field and Laboratory Wet Soil Spectra in the Vis-NIR Range for Soil Organic Carbon Prediction in the Absence of Laboratory Dry Measurements

Abstract

Spectroscopy has demonstrated the ability to predict specific soil properties. Consequently, it is a promising avenue to complement the traditional methods that are costly and time-consuming. In the visible-near infrared (Vis-NIR) region, spectroscopy has been widely used for the rapid determination of organic components, especially soil organic carbon (SOC) using laboratory dry (lab-dry) measurement. However, steps such as collecting, grinding, sieving and soil drying at ambient (room) temperature and humidity for several days, which is a vital process, make the lab-dry preparation a bit slow compared to the field or laboratory wet (lab-wet) measurement. The use of soil spectra measured directly in the field or on a wet sample remains challenging due to uncontrolled soil moisture variations and other environmental conditions. However, for direct and timely prediction and mapping of soil properties, especially SOC, the field or lab-wet measurement could be an option in place of the lab-dry measurement. This study focuses on comparison of field and naturally acquired laboratory measurement of wet samples in Visible (VIS), Near-Infrared (NIR) and Vis-NIR range using several pretreatment approaches including orthogonal signal correction (OSC). The comparison was concluded with the development of validation models for SOC prediction based on partial least squares regression (PLSR) and support vector machine (SVMR). Nonetheless, for the OSC implementation, we use principal component regression (PCR) together with PLSR as SVMR is not appropriate under OSC. For SOC prediction, the field measurement was better in the VIS range with R2CV = 0.47 and RMSEPcv = 0.24, while in Vis-NIR range the lab-wet measurement was better with R2CV = 0.44 and RMSEPcv = 0.25, both using the SVMR algorithm. However, the prediction accuracy improves with the introduction of OSC on both samples. The highest prediction was obtained with the lab-wet dataset (using PLSR) in the NIR and Vis-NIR range with R2CV = 0.54/0.55 and RMSEPcv = 0.24. This result indicates that the field and, in particular, lab-wet measurements, which are not commonly used, can also be useful for SOC prediction, just as the lab-dry method, with some adjustments.

Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery

“…For the modelling and pre-treatment algorithms used in this study, it can be noted that though the field spectra performance increases using SVMR with log transformation (Table 3), for RF, its prediction accuracy experiences a decrease even compared to the UAS dataset. This confirms the need for the use of more techniques for better comparison and to achieve a fair estimation of different form of datasets as noted by Moron and Cozzolino [93] and Mouazen [94] and have been also confirmed by some other studies [10,13]. Finally, the SOC maps derived from the predictive modeling based on the data from the different sensors used are shown in the Figure 5.…”

“…Sentinel-2 imagery showed a better similarity than UAS imagery to the reference map possibly due to SWIR bands in Sentinel-2, however, map based on UAV imagery on the other hand was similar to the reference map where SOC is lower. the need for the use of more techniques for better comparison and to achieve a fair estimation of different form of datasets as noted by Moron and Cozzolino [93] and Mouazen [94] and have been also confirmed by some other studies [10,13]. Finally, the SOC maps derived from the predictive modeling based on the data from the different sensors used are shown in the Figure 5.…”

“…Whereas, for remote sensing (RS), electromagnetic radiation is used to obtain, without physical contact data or occurrence [8]. Spectroscopy (visible near-infrared (vis-NIR)) under PSS is also classified as a useful tool for the accurate quantification, in laboratory [2,9,10], and in the field [11][12][13] of SOC content with limited resources. Its approach for soil assessment started in the years 1960 to 1980 [14] and intensified between 1990-2000s [15].…”

Vis-NIR-SWIR spectroscopy in mapping of soil physical-chemical attributes in areas cultivated with irrigated mango in the Brazilian semi-arid region

“…The OSC technique is based on the elimination of the variation that is not related to the variable of interest expected to be estimated, but without eliminating the essential information. This is done by omitting information that is mathematically orthogonal to the response, or as close as possible to the orthogonal (Biney at al., 2020). Moreover, the reduction in the number of PLSR model factors promoted by the application of OSC preprocessing leads to a simplification of the model (Balcerowska et al, 2005).…”

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

“…This method is often used for quantitative analysis of spectra. Hyperspectral data has many bands, and there are problems of information redundancy and multicollinearity between the bands [35,36]. PLSR first uses principal component analysis in the modeling process to project spectral data onto a set of orthogonal factors that become latent variables and determine the optimal number of factors for the latent variables using internal crossvalidation.…”