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Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions. [ABSTRACT FROM AUTHOR]

Abstract Soil base saturation percentage (BSP) plays an important role in the assessment of soil taxonomic classification and soil fertility. Conventionally, soil BSP measurement methods are fraught with many drawbacks such as being laborious, time consumptive, destructive to the samples, and can lead to the underestimation of true cation exchange capacity (CEC). Recently, proximal sensors such as portable X-ray fluorescence (PXRF) spectrometry have proven to be effective for rapid physicochemical analysis of soils. In this study, we proposed and examined a PXRF-based method to predict BSP using 300 soil samples from the active agricultural lands in six states across the USA; Colorado, California, Minnesota, Nebraska, Oklahoma, and Texas. An Olympus Vanta series PXRF analyzer was employed to measure Mg, Ca, and K for BSP prediction. Results were validated using four different multivariate models [generalized additive model (GAM), multiple linear regression (MLR), random forest (RF), regression tree (RT)] via R 3.5.1. Predictive model performance was assessed via root mean squared error (RMSE), coefficient of determination (R2), residual prediction deviation (RPD), the ratio of performance to interquartile (RPIQ) range, and bias. While predicting BSP from PXRF quantified elements, models exhibited R2, RMSE (%), and RPDs as follows: GAM = 0.58, 9.0, 1.6; MLR = 0.45, 10.4, 1.4; RF = 0.62, 8.7, 1.6; RT = 0.68, 7.9, 1.8, respectively. Soil cation exchange capacity was also predicted using a similar approach, with similar and moderate predictive performance; GAM produced R2, RMSE (cmol c kg−1), and RPD of 0.69, 5.6, 1.8, respectively, relative to laboratory data. This study showed that the PXRF elements can be used to predict BSP with fair accuracy for the range of agricultural soils examined. As such, further study and enhancement of the approach outlined herein on a wider array of soils is warranted. Highlights • Soil base saturation percentage was predicted via PXRF. • Na was negligible in agricultural soils. • Regression tree was optimal for BSP prediction. [ABSTRACT FROM AUTHOR]

This pilot study compared penalized spline regression (PSR) and random forest (RF) regression using visible and near-infrared diffuse reflectance spectroscopy (VisNIR DRS) derived spectra of 164 petroleum contaminated soils after two different spectral pretreatments [first derivative (FD) and standard normal variate (SNV) followed by detrending] for rapid quantification of soil petroleum contamination. Additionally, a new analytical approach was proposed for the recovery of the pure spectral and concentration profiles of n-hexane present in the unresolved mixture of petroleum contaminated soils using multivariate curve resolution alternating least squares (MCR-ALS). The PSR model using FD spectra (r 2 = 0.87, RMSE = 0.580 log10 mg kg−1, and residual prediction deviation = 2.78) outperformed all other models tested. Quantitative results obtained by MCR-ALS for n-hexane in presence of interferences (r 2 = 0.65 and RMSE 0.261 log10 mg kg−1) were comparable to those obtained using FD (PSR) model. Furthermore, MCR ALS was able to recover pure spectra of n-hexane. [Copyright &y& Elsevier]

• SOC of 371 soil samples was predicted via Nix, PXRF-Rb, and texture. • Combining Nix color parameters and texture showed the best prediction of SOC. • Using PXRF-Rb as a proxy for clay could not produce satisfactory SOC prediction. • Color variables b* and a* extracted by Nix appeared influential in predicting SOC. The optimum ecosystem functioning is reliant on soil organic carbon (SOC) content which is traditionally measured in the laboratory via a cumbersome wet-chemistry method. This preliminary research tested whether a combination of Nix color sensor and portable X-ray fluorescence (PXRF) spectrometer data with soil texture data can improve soil SOC prediction accuracy relative to using them independently. A total of 371 samples representing diverse soil texture and SOC content were collected from three different ecoregions of eastern India: coastal saline zone, red and lateritic zone, and Gangetic alluvial zone. All dried, ground, and sieved samples were scanned via Nix and PXRF and random forest (RF) regression was used to predict soil SOC with different combinations of data. Soils were grouped into nine textural classes while soil SOC content exhibited substantial variability (0.08–2.26%). Comparing soil SOC with texture (sand + silt + clay), satisfactory prediction accuracy was observed (validation R2 = 0.70). Combining Nix extracted color parameters with texture substantially improved the model performance, producing the validation determination coefficient of 0.81. In contrast, PXRF-Rb, as a proxy of soil clay content was unable to achieve satisfactory prediction performance (R2 = 0.24), indicating the heterogeneity in soil mineralogical composition. The RF variable importance plot using Nix alone identified redness (a*) and yellowness to blueness (b*) as influential predictors, manifesting the impact of red color from Fe and Al-oxides and their significant negative correlation with soil SOC (r = −0.62 and −0.57 for a* and b*, respectively). These color parameters were again identified by the RF variable importance plot of (Texture + Nix)-model, implying that the SOC prediction improvement may be linked with the Nix sensor's capability of extracting useful information in the visible range. Summarily, a combination of Nix color variables and texture data was adept at predicting soil SOC in lieu of traditional laboratory analysis. The robustness of the (Texture + Nix)-based SOC prediction model can be augmented by incorporating more soil samples representing all 12 soil textural classes and variable SOC content, showing all possible soil colors in the pedogenic environment. [ABSTRACT FROM AUTHOR]

Data heterogeneity has become a challenging problem in modern data analysis. Classic statistical modeling methods, which assume the data are independent and identically distributed, often show unsatisfactory performance on heterogeneous data. This work is motivated by a multivariate calibration problem from a soil characterization study, where the samples were collected from five different locations. Newly proposed and existing signal regression models are applied to the multivariate calibration problem, where the models are adapted to handle such spatially clustered structure. When compared to a variety of other methods, e.g. kernel ridge regression, random forests, and partial least squares, we find that our newly proposed varying-coefficient signal regression model is highly competitive, often out-performing the other methods, in terms of external prediction error. • We propose a new varying-coefficient signal regression approach that outperforms several other Multivariate Calibration approaches. • We model clustered or spatial data ito improve performance based on external prediction error. • We overview Penalized Signal Regression, then propose a new extension. • We use a rich spectral dataset soil samples from four U.S. states and the country of France and provide a combined analysis. • Our methods aim for the dual role of quality external prediction and interpretability through statistical models. [ABSTRACT FROM AUTHOR]

Following the Fund ão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fund ão dam failure can be in detail characterized via pXRF ex situ , providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes. • pXRF data and random forest algorithm accurately predicted iron-rich tailings properties. • Potentially toxic elements (Ba, Pb, Cr, V, Cu, Co, and Ni) were successfully predicted. • Soil agronomic properties were also accurately predicted with high R2 values. • These findings constitute a secure dataset for environmental recomposition planning. [ABSTRACT FROM AUTHOR]

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors. [ABSTRACT FROM AUTHOR]

Proximal sensors have proven capable of predicting multiple soil properties under different conditions. However, doubts remain about which sensor is preferable for delivering optimal prediction models and which preprocessing methods produce the most accurate results. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) diffuse reflectance spectroscopy have been widely used, while the NixPro TM color sensor has been explored more recently. This study evaluated the use of pXRF, Vis-NIR, and NixPro TM data to predict soil organic matter content (SOM), pH, base saturation (BS), the sum of bases (SB), cation exchange capacity (CEC) at pH = 7 and effective CEC (eCEC), via each sensor in isolation, and via combined sensors data. Moreover, factors interfering in the prediction models' accuracy (data preprocessing methods, soil horizon, soil class, parent material) were used as auxiliary variables. 604 soil samples were collected in Brazil, encompassing ten soil orders and 19 parent materials. Numerical and categorical prediction models (7,980) were created for six soil properties using a random forest algorithm, totaling 7980 models, delivering almost 24,000 results., Coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were used for validation of numerical predictions., Overall accuracy and kappa coefficient were calcualted for categorical predictions. Although the combination of sensors provided most of the best predictions, pXRF in isolation achieved accuracies close to the three sensors combined. NixPro TM offered superior contributions to SOM and CEC predictions, but pXRF and Vis-NIR were responsible for the best results of most studied variables. On average, by adding pXRF to Vis-NIR data, predictive accuracy improved 32%; while adding Vis-NIR to pXRF data increased accuracy by ca. 6%. Soil-order-specific models improved predictions for Ultisols compared to general models (without soil order distinction), reaching R2 > 0.90. Soil parent material and horizon did not improve models significantly. Categorical predictions improved the accuracy for some properties, reaching an overall accuracy of 100% and kappa index of 1.0 for pH in A horizons of Ultisols via pXRF + Vis-NIR data. Proximal sensor data with no auxiliary variables provided almost all the best results. The fusion of proximal sensors can provide better predictions, but pXRF alone can deliver satisfactory results in most cases for the six soil properties. • Soil pH, SOM, BS, SB, eCEC, CEC can be predicted by pXRF sensor alone. • PXRF provided results similar to the best ones provided by both Vis-NIR and NixPro TM . • Soil horizon, parent material, or class did not significantly improve predictions. • PXRF alone delivers satisfactory results for all the six soil properties evaluated. [ABSTRACT FROM AUTHOR]

This work sought to predict soil microbiological attributes based on soil fertility and texture, elemental contents determined by portable X-ray fluorescence spectrometry, and terrain attribute data with and without addition of season (dry or rainy) and phytophysiognomy as auxiliary predictors. Soil samples were collected in both seasons in four phytophysiognomies. Analyses for prediction of basal soil respiration, microbial biomass carbon, metabolic quotient, and microbial quotient were performed. Terrain attributes, total elemental concentrations obtained by portable X-ray fluorescence spectrometry, soil fertility and texture as well as phytophysiognomy and season were used as predictor variables. Prediction models were created via conditional random forest algorithm and validated with leave-one-out cross-validation through coefficient of determination (R2), root mean square error, mean absolute error and ratio percent deviation. The best results were delivered when phytophysiognomy and season were included as predictors. Metabolic quotient, microbial quotient, microbial biomass carbon and basal soil respiration achieved the best prediction using only soil fertility and texture data (R2 = 0.79, 0.66, 0.65, 0.91, respectively). Predictions of basal soil respiration and metabolic quotient using only terrain data achieved R2 values of 0.91 and 0.73, respectively. Elemental concentrations determined by portable X-ray fluorescence spectrometry reasonably predicted two microbiological attributes. It is possible to adequately predict these four microbiological attributes both locally and spatially through terrain and soil properties data. We encourage further investigations on prediction of these and other microbiological attributes under different environmental conditions and at shorter spatial and temporal scales. • Microbiological attributes were accurately predicted from soil and terrain data. • Phytophysiognomy and season (dry or rainy) data improved predictions. • Soil fertility and texture best predicted microbiological attributes. • Terrain data accurately predicted basal soil respiration and metabolic quotient. • Spatial variability map of microbiological attributes enhanced data visualization. [ABSTRACT FROM AUTHOR]

Traditional methods of soil chemical analysis are time consuming, costly, and generate chemical waste. Proximal sensors, such as portable X-ray fluorescence (pXRF) spectrometry, may help to overcome these issues since they have been shown to produce accurate predictions of many soil properties. However, such processes need to be further investigated in Brazilian soils. This work aimed to assess the influence of soil management and mineralogy on elemental composition of soils and predict exchangeable Al3+, Ca2+, Mg2+, and available K+, and P contents from pXRF data alone and associated with soil texture through machine learning algorithms [stepwise generalized linear models (SGLM), and random forest (RF)] in soils of the Brazilian Coastal Plains (BCP). A total of 285 soil samples were collected from the A (n = 123) and B (n = 162) horizons and subjected to laboratory analyses and pXRF scans. Samples were randomly separated into 70% for modeling and 30% for validation. Soil mineralogy and management mainly influenced Al, and Ca and K total content, respectively. In general, the inclusion of the auxiliary input data of soil texture did not change the predictive power of the models. The best results highlight a considerable promise of pXRF technique for rapidly assessing exchangeable Ca2+ (RMSE = 176.3 mg kg−1, R2 = 0.71), Mg2+ (37.7 mg kg−1, 0.60), and available K+ (27.46 mg kg−1, 0.67). The algorithms could not generate reliable models to predict exchangeable Al3+ (30.6 mg kg−1, 0.47) and available P (19.9 mg kg−1, 0.14). In sum, pXRF can be used to reasonably predict soil fertility properties in the BCP soils. Further studies may extend predictions to other soil properties. • pXRF elemental data was used to available/exchangeable soil nutrients. • Soil textural data as auxiliary input data minimally improved predictive algorithms. • Random forest algorithm provided optimal prediction of soil parameters. [ABSTRACT FROM AUTHOR]