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• Data from a simple color sensor and VisNIR DRS were combined to predict soil OC. • Variables measured by the color sensor showed negative and non-linear effects on OC. • PLSR produced satisfactory OC prediction using DRS spectra alone. • The combined model outperformed model from either sensor. Contemporary soil characterization is increasingly dependent upon proximal sensor data whereby high sample throughput and low-cost analysis are realized. Recent research studies have shown that combined sensor platforms generally offer greater predictive model stability and increased accuracy than the use of sensors in isolation. In this study, data from an inexpensive ($350 USD) Nix Pro color sensor, which measures the true color of an object by using red, blue, and green filters, was used with diffuse reflectance spectroscopy (DRS) (590–2490 nm) to predict soil organic carbon (OC) content in highly disturbed, landfill soils of India ex-situ. Generalized additive model (GAM) and partial least squares regression (PLSR) were applied to model DRS and Nix Pro data, respectively, both independently and by combining model predictions using a bilinear regression. Results showed that the combined model outperformed either sensor independently where the 30% external test set achieved a validation R2 of 0.95, residual prediction deviation (RPD) of 4.54, and the ratio of performance to interquartile range of 6.25 relative to laboratory-measured OC data. In contrary, the GAM-OC model using Nix Pro data alone and the PLSR-OC model using DRS data alone exhibited validation R2 values of 0.58 and 0.81, respectively. In sum, the addition of the inexpensive Nix Pro sensor substantively improved the prediction of soil OC relative to the use of DRS in isolation. Future studies should evaluate the effectiveness of such an approach on a wider variety of soil types (e.g., colors), its effectiveness in-situ under variable moisture conditions, and in possible combination with other proximal sensing platforms. [ABSTRACT FROM AUTHOR]

• A field-portable acoustic sensing device was evaluated for GMC measurements. • Acoustic velocity in soils exhibited the impact of soil texture and compaction. • LME model showed an increase in the acoustic velocity with a decrease in GMC. • Models fitted for individual LME-based clusters produced satisfactory R2 values. For the last couple of decades, the idea of soil water measurement has gained widespread importance due to the spiraling crisis of global water resources. While several methods are being commonly used for soil water measurement, most of them do not satisfy all the essential requirements for water monitoring under field conditions including rapid on-site measurement and cost-effectiveness. This study was conducted to evaluate the efficacy of a novel field-portable acoustic sensing device on soil moisture measurements. The conventional Brutsaert's model was followed involving the continuous wave zero-crossing method to measure the acoustic velocity and considering the phase shift due to the amplifier and ambient temperature. The gravimetric moisture content (GMC) was calibrated using the acoustic velocity for 70 samples representing seven textural classes and variable soil organic carbon (OC). Results indicated the monotonous reduction of the acoustic velocities of p-wave with increasing GMC. In general, soils with higher clay content reflected higher acoustic velocities. However, sandy soils with high OC content were less prone to compaction and produced comparatively higher acoustic velocities than soils with higher clay content. A linear mixed effect (LME) model indicated that one unit increase in the acoustic velocity resulted in a 0.086% decrease in GMC. Moreover, LME parameters-based K-means clustering further highlighted the influence of soil texture for controlling the acoustic velocity. The use of LME justified the use of GMC as the target instead of the degree of saturation. Linear and quadratic models fitted for individual LME-based clusters produced R2 values ranging from 0.80 to 0.88 and 0.82–0.88, respectively. Moreover, combined linear (R2 = 0.61; RMSE = 52.12 m/s) and quadratic (R2 = 0.64; RMSE = 50.48 m/s) models incorporating all 70 samples via bootstrapping produced comparable accuracies for GMC prediction, but yielded inferior results than the individual cluster-based models. In conclusion, the acoustic sensing device showed its potential for soil water content monitoring, leading to efficient irrigation planning. More research is warranted for texture-wise calibration development incorporating a wide assortment of soil types and on-site evaluation. [ABSTRACT FROM AUTHOR]

• Soils from three agro-climatic zones were classified using MSCC and Nix. • Soil OC was predicted using MSCC and Nix in different combinations. • Both non– standardized and MSCC-standardized Nix values were used. • Results indicated the reproducibility and potential accuracy of the Nix sensor. • Nix + MSCC produced satisfactory surface OC prediction. Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (Nix STD) and without MSCC standardization (Nix NON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the Nix STD , an acceptable mean color difference (ΔE* ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the Nix STD /Nix NON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using Nix NON-STD data alone. Besides, the combination of the MSCC and the Nix NON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ. [ABSTRACT FROM AUTHOR]