Visible Near-Infrared Reflectance and Laser-Induced Breakdown Spectroscopy for Estimating Soil Quality in Arid and Semiarid Agroecosystems

Abstract

<jats:p>Visible near-infrared reflectance spectroscopy (VNIRS) and laser-induced breakdown spectroscopy (LIBS) are potential methods for the rapid and less expensive assessment of soil quality indicators (SQIs). The specific objective of this study was to compare VNIRS and LIBS for assessing SQIs. Data was collected from over 140 soil samples taken from multiple agricultural management systems in New Mexico, belonging to arid and semiarid agroecosystems. Sampled sites included New Mexico State University Agricultural Science Center research fields and several commercial farm fields in New Mexico. Partial least squares regression (PLSR) was used to establish predictive relationships between spectral data and SQIs. Fifteen soil measurements were modeled including the soil organic matter (SOM), permanganate oxidizable carbon (POXC), total microbial biomass (TMB), total bacteria biomass (TBB), total fungi biomass (TFB), mean weight diameter of dry aggregates (MWD), aggregates 2–4 mm (AGG &gt; 2 mm), aggregates &lt; 0.25 mm (AGG &lt; 0.25 mm), wet aggregate stability (WAS), electrical conductivity (EC), calcium (Ca), magnesium (Mg), sodium (Na), and iron (Fe). Overall, calibrations based on measurements irrespective of locations performed better for LIBS and combined VNIRS-LIBS. Measurements separated according to locations highly improved the quality of prediction for VNIRS as compared to combined locations. For example, the prediction R2 values for regression of VNIRS were 0.19 for SOM, 0.30 for POXC, 0.24 for MWD, 0.15 for AGG &gt; 2 mm, and 0.13 for EC in combined datasets irrespective of location. When separated according to locations, for one of the locations, the predictive R2 values for VNIRS were 0.48 for SOM, 0.70 for POXC, 0.67 for MWD, 0.60 for AGG &gt; 2 mm, and 0.51 for EC. The prediction values varied with the sampling time for both LIBS and VNIRS. For example, the prediction values of some SQIs using VNIRS were higher in samples collected in winter for measurements, including SOM (0.90), MWD (0.96), WAS (0.66), and EC (0.94). Using the VNIRS, the corresponding predictive values for the same SQIs were lower for samples collected in the fall (SOM (0.61), MWD (0.45), WAS (0.46), and EC (0.65)). While this study illustrates the prospects of VNIRS and LIBS for estimating SQIs, a more comprehensive evaluation, using a larger regional dataset, is required to understand how the site and soil factors affect VNIRS and LIBS, in order to enhance the utility of these methods for soil quality assessment in arid and semiarid agroecosystems.</jats:p>

Prediction of nitrogen, active carbon, and organic carbon‐to‐clay ratio in agricultural soils by in‐situ spectroscopy

“…For the POXC, our results indicate that vis-NIR spectroscopy can well predict the amount of soil active C (Calderon et al, 2017;Omer et al, 2020;Reijneveld et al, 2023). As previously observed (e.g., Plaza-Bonilla et al, 2014), in our dataset, there was a positive correlation between SOC and POXC (r = 0.88) suggesting that, as recently highlighted by Woodings and Margenot (2023), POXC may indeed reflect a more processed fraction of soil C. After taking into account the standardization of analytical methods for measuring POXC in soil samples (Wade et al, 2020), the reliable estimation of POXC by vis-NIR can represent a helpful tool to easily follow the temporal and spatial trends of a soil quality indicator that is expected to react more rapidly to agronomic management (Lucas & Weil, 2021;Plaza-Bonilla et al, 2014;Singh et al, 2023;Zhao et al, 2022).…”

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

“…For example, Liu et al [35] and Wang et al [37] used different vegetation indexes extracted from remote sensing images to represent the soil fertility and soil moisture of cultivated land. Askari et al [14] and Omer et al [38] evaluated soil quality using visible and near-infrared spectroscopy. Ma et al [34] used gross primary productivity (GPP) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data to characterize the productivity of cultivated land.…”

Evaluation of Soil Quality in Arid Western Fringes of the Nile Delta for Sustainable Agriculture

“…erefore, the estimation of soil quality indices is a complicated process and quite a hard mission [16]. Recently, several techniques have been successfully utilized for evaluating soil quality such as spectroscopy fingerprint [17], the Soil Management Assessment Framework (SMAF) [18], and spatial variability of physicochemical soil properties [19]. Most researchers estimated soil quality using only one technique with some exclusion [17][18][19][20][21].…”

“…Recently, several techniques have been successfully utilized for evaluating soil quality such as spectroscopy fingerprint [17], the Soil Management Assessment Framework (SMAF) [18], and spatial variability of physicochemical soil properties [19]. Most researchers estimated soil quality using only one technique with some exclusion [17][18][19][20][21]. Principal component analysis (PCA) and linear regression coefficients are also widely used for minimizing the number of soil variables included in estimating soil quality [21].…”