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Novel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2= 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets. Soil properties can be predicted via proximal sensors using multinational datasets.This study encompassed soil samples from Brazil, France, Mozambique, India, and United States.Soil organic carbon and texture were accurately predicted via proximal sensors.The broad application of proximal sensors to aid soil characterization is encouraged.

Mollisols are dark colored, carbon‐rich mineral soils occupying a large proportion (836 soil series) of the soils of the Central Plains of the United States. By contrast, only eight official soil series of Mollisols have been mapped in Alaska (six Haplocryolls, one Calcicryoll, one Haplogeloll). Little information exists about Geloll pedogenesis, taxonomic variability, and extent. In this study, 39 horizons were morphologically described across ten Geloll pedons in northern Alaska. Based on analogous taxonomic structure in Cryolls, two pedons would meet the criteria for a Fluventic subgroup as the organic carbon content was ≥0.3% (1.47% and 0.88%) at a depth of 125 cm below the mineral soil surface. Three pedons would meet the criteria for a Pachic subgroup because the mollic epipedon was thicker than 40 cm (52 cm, 53 cm, and 54 cm) and the texture class was finer than loamy fine sand (sandy loam). However, no Fluventic or Pachic subgroups currently exist for Haplogelolls. Two pedons were classified as Cumulic Haplogelolls, and three pedons were Typic Haplogelolls. Field and laboratory characterization allowed for the development of a Geloll pedogenic concept generally represented by well‐drained soils with limestone and/or dolomitic parent material. Such soils generally feature thin or no surficial organic horizons and large quantities of coarse fragments and coarse textured materials. Soil organic carbon calculations for suspected areas of Alaska Gelolls total 1.18 Pg; closely aligning with estimates of previous studies. Future work should expand the explanatory taxonomy based on new morphological expressions observed throughout gelic temperature regime regions.

Frozen soils or those with permafrost cover large areas of the earth's surface and support unique vegetative ecosystems. Plants growing in such harsh conditions have adapted to small niches, which allow them to survive. In northern Alaska, USA, both moist acidic and non-acidic tundra occur, yet determination of frozen soil pHs currently requires thawing of the soil so that electrometric pH methods can be utilized. Contrariwise, a portable X-ray fluorescence (PXRF) spectrometer was used in this study to assess elemental abundances and relate those characteristics to soil pH through predictive multiple linear regressions. Two operational modes, Soil Mode and Geochem Mode, were utilized to scan frozen soils in-situand under laboratory conditions, respectively, after soil samples were dried and ground. Results showed that lab scanning produced optimal results with adjusted coefficient of determination (R2) of 0.88 and 0.33 and root mean squared errors (RMSEs) of 0.87 and 0.34 between elemental data and lab-determined pH for Soil Mode and Geochem Mode, respectively. Even though the presence of ice attenuated fluoresced radiation under field conditions, adjusted R2and RMSEs between the datasets still provided reasonable model generalization (e.g., 0.73 and 0.49 for field Geochem Mode). Principal component analysis qualitatively separated multiple sampling sites based on elemental data provided by PXRF, reflecting differences in the chemical composition of the soils studied. Summarily, PXRF can be used for in-situdetermination of soil pH in arctic environments without the need for sample modification and thawing. Furthermore, use of PXRF for determination of soil pH may provide higher sample throughput than traditional eletrometric-based methods, while generating elemental data useful for the prediction of multiple soil parameters.

Saline soilsare defined as those containing appreciable salts more soluble than gypsum (e.g., various combinations of Na, Mg2, Ca2, K, Cl−, SO42-, HCO3−, and CO32-). Saline soils can occur across diverse climates and geological settings. As such, salinity is not germane to specific soil textures or parent materials. Traditional methods of measuring soil salinity (e.g., electrical conductance), although accurate, provide limited data and require laboratory analysis. Given the success of previous studies using portable X-ray fluorescence (PXRF) as a tool for measuring soil characteristics, this study evaluated its applicability for soil salinity determination. Portable X-ray fluorescence offers accurate quantifiable data that can be produced rapidly, in situ, and with minimal sample preparation. For this study, 122 surface soil samples (0–15 cm) were collected from salt-impacted soils of coastal Louisiana. Soil samples were subjected to standard soil characterization, including particle size analysis, loss-on-ignition organic matter, electrical conductivity (EC), and elemental quantification via PXRF. Simple and multiple linear regression models were developed to correlate elemental concentrations and auxiliary input parameters (simple: Cl; multiple: Cl, S, K, Ca, sand, clay, and organic matter) to EC results. In doing so, logarithmic transformation was used to normalize the variables to obtain a normal distribution for the error term (residual, ei). Although both models resulted in similar acceptable r2between soil EC and elemental data produced by PXRF (0.83 and 0.90, respectively), multiple linear regression is recommended. In summary, PXRF has the ability to predict soil EC with reasonable accuracy from elemental data.