Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

Abstract

Soil organic matter (SOM) is an important parameter of soil fertility, and visible and near-infrared (VIS-NIR) spectroscopy combined with multivariate modeling techniques have provided new possibilities to estimate SOM. However, the spectral signal is strongly influenced by soil moisture (SM) in the field. Interest in using spectral classification to predict soils in the moist conditions to minimize the influence of SM is growing. The objective of this study was to investigate the transferability of two approaches, SM-based cluster method with known SM (classifying the VIS-NIR spectra into different SM clusters to develop models separately), the normalized soil moisture index (NSMI)-based cluster method with unknown SM (utilizing NSMI to indicate the SM and establish models separately), to predict SOM directly in moist soil spectra. One hundred and twenty one soil samples were collected from Central China, and eight SM levels were obtained for each sample through rewetting experiments. Their reflectance spectra and SOM concentrations were measured in the laboratory. Partial least square-support vector machine (PLS-SVM) was employed to construct SOM prediction models. Specifically, prediction models were developed for NSMI-based clusters with unknown SM data. The models were assessed through three statistics in the processes of calibration and validation: the coefficient of determination (R 2 ), root mean square error (RMSE) and the ratio of the performance to deviation (RPD). Results showed that the variable SM led to reduced VIS-NIR reflectance nonlinearly across the entire spectral range. NSMI was an effective spectral index to indicate the SM. Classifying the VIS-NIR spectra into different SM clusters in known SM states could improve the performance of PLS-SVM models to acceptable prediction accuracies (R 2 cv = 0.69-0.77, RPD = 1.79-2.08). The estimation of SOM, when using the NSMI-based cluster method with unknown SM (RPD = 1.95-2.04), was similar to the use of the SM-based cluster method with known SM (RPD = 1.79-2.08). The predictive results (RPD = 1.87-2.06) demonstrated that the NSMI-based cluster method has potential for application outside the laboratory for SOM prediction without knowing the SM explicitly, and this method is also easy to carry out and only requires spectral information.

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

“…The results from our study support their findings. Our previous study has showed that soil water had a nonlinear effect on reflectance spectra [5]. The wavebands after variable selection would definitely reduce the spectral variables involved in modeling, but these processes cannot change the nonlinear relation between soil water and reflectance spectra.…”

“…If the field/moist spectra can be directly applied to estimate SOM, then much of time and labor would be saved. However, the estimation of SOM with field/moist spectra may face some challenges: issues with field/moist spectra (e.g., soil particles, soil structure, soil surface and soil water content); difficulties in modeling a suitable VIS-NIR model due to the lack of available field/moist soil spectral libraries; unequal spectral responses in various soil types [4][5][6]. Variations from these factors (just mentioned above) might influence the model accuracy for SOM estimation.…”

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

“…Since laboratory measurements are mainly conducted on air-dried and sieved soil samples, transferring such models to field conditions entails issues to be addressed, notably soil moisture that is known to affect the soils spectral signature [20,23,65,66]. Therefore, several studies have attempted to create prediction models for different levels of soil moisture [67][68][69]. The main absorption peaks of water content can be found near 1400, 1900, and 2200 nm due to the overtones and vibrations of the O-H group [70].…”

The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy

“…In doing so, we ensured that the validation samples were evenly distributed in the range of the SOM concentration and covered the SOM diversity of expected future samples [44]. Moreover, such a division strategy was commonly adopted in previous studies using vis-NIR spectroscopy to estimate soil properties [27,38,61,62]. The raw spectra of the remaining 85 samples were pretreated by six methods, namely SG, FD, MC, log(1/R), MSC, and SNV.…”