Potential of integrated field spectroscopy and spatial analysis for enhanced assessment of soil contamination: A prospective review

In recent years portable X-ray fluorescence (pXRF) spectrometers have been recognised as an in-field tool for soil analyses as they offer rapid, real-time, simultaneous multi-element analysis of soil samples with no or minimal sample preparation. Portable XRF has been used quite extensively to estimate the degree of soil contamination with heavy metals and to measure the concentration of elements in soils that are important for the soil's fertility, but more recently it has also been used to calibrate for a range of soil chemical and physical properties to perform digital soil morphometrics. However, this still relatively new technology also offers an opportunity for soil scientists to investigate processes of pedogenesis in the field, which in turn will complement the in-field descriptions of soils profoundly. Here, we employed a handheld pXRF spectrometer to investigate the pedogenesis pathways and likely parent materials of three different soil types, utilizing elemental concentration data and geochemical indices. Our study site, a property called ‘Nowley’, is situated on the Liverpool Plains in the Spring Ridge district on the North West Slopes of New South Wales, Australia, in one of the most versatile dryland cropping regions of the continent. The variation in soil types and parent materials across the property offers an unique opportunity to study the impact of parent materials and topography on soil formation. A total of three soil pits were excavated to a depth of 1.0 m to expose the lower B horizons. Soil pit faces were then scanned at 0.1 m depth increments starting from the top of the soil profile to the lowest B horizon exposed. Our results show that elemental data acquired in field condition, which are affected by sample heterogeneities and soil moisture content, are comparable to elemental data acquired in air-dried, ground condition, as geochemical index values show similar trends down the soil profile. We found that the use of pXRF is a very effective in-field tool to assess pedogenic pathways and parent material origin, because of the instant computation of weathering indices and elemental ratios. We conclude that pXRF can be applied successfully and readily to study the soil's weathering history and identify processes of soil formation in the field.

Utilizing portable X-ray fluorescence spectrometry for in-field investigation of pedogenesis

Synthesized use of VisNIR DRS and PXRF for soil characterization: Total carbon and total nitrogen☆

An assessment of model averaging to improve predictive power of portable vis-NIR and XRF for the determination of agronomic soil properties

D . X u a , R . Z h a o a, S . L i a , S . C h e n b,c , Q . J i a n g a,d, L . Z h o u a & Z . S h i a,e aInstitute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China, bINRA, Unité InfoSol, 45075 Orléans, France, cUMR SAS, INRA, Agrocampus Ouest, 35042 Rennes, France, dCollege of Information Engineering, Tarim University, Alar 843300, China, and eKey Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Abstract Multiple proximal soil sensors provide more useful and complementary information than a single sensor does for improving the prediction of soil properties. In this study, two data fusion methods (i.e., sample data fusion and sensor data fusion) were performed using a Fourier transform near-infrared (NIR) spectrometer and a portable X-ray fluorescence (PXRF) analyzer for predicting the contents of soil clay, silt, and sand in three sets of soil sample data, which were collected in a medium-scale area and two small-scale areas, respectively. To verify the performance of these methods and confirm related theoretical analysis, calibration models on individual sample and sensor data sets, fused sample data sets, and fused sample and sensor data sets were constructed for three soil textural fractions. First, we applied stepwise multiple linear regression to PXRF measurements and partial least-squares regression to NIR spectral data in the three different sampling areas. Then, we used the sample data set from the medium-scale area as reference and combined it with other two sample data sets to calibrate the models. Finally, we further fused the principal components of NIR spectral data from principal component analysis with the PXRF measurements to improve the accuracy of soil texture prediction. It was found that the models based on fused data could significantly improve the accuracy of soil texture prediction compared with those based on individual sample or sensor data sets. The best model for the prediction of clay and sand contents was calibrated on fused PXRF measured data and NIR principal components from different sampling areas. Whereas geographical locations and soil types might impact the accuracy and fitness of the models, data fusion through multiple sensors and different sampling areas showed great promise as a technique for rapidly assessing soil textural fractions. Further evaluation of the data fusion methods suggested here may provide a practical technique for future applications in soil research.

Prediction of Soil Texture Using FT-NIR Spectroscopy and PXRF Spectrometry With Data Fusion

Reference evapotranspiration (ET0) is an important component of the global water cycle, and its long-term change directly influences the regional water supply and demand balance. Under the background of global change, investigating spatiotemporal trends in ET0 and its response to climate change is of great importance for the conservation and rational utilization of water resources. Based on daily climate data from 91 meteorological stations during 1960–2017 in Northeast China, this study calculated ET0 using the Penman-Monteith method and analyzed its spatiotemporal change trends and primary driving factors. The results show the following: (1) During 1960–2017, the annual ET0 in Northeast China showed a nonsignificant upward trend at a rate of 1.45 mm/10a. A mutation point of ET0 was detected in 1993. From 1960 to 1993, ET0 experienced a significant decrease (p < 0.1), while annual air temperature showed a significant upward trend (p < 0.01), which indicated the appearance of an evaporation paradox. This was because the remarkable drop in wind speed and sunshine duration played a great role in the reduction of ET0. From 1994 to 2017, the evaporation paradox disappeared. (2) ET0 trend in Northeast China was significantly and positively related to altitude. In the lower altitude regions (<500 m), ET0 generally decreased, while in the higher altitude areas (>500 m), ET0 displayed an upward trend. (3) Based on the results of multiple regression analysis, relative humidity was the primary driving factor for ET0 trends in Northeast China during 1960–2017. At diverse altitudes, the primary climatic factors influencing ET0 were different. In high-altitude areas (>500 m), the change in ET0 was mainly influenced by relative humidity, while wind speed was the primary driving factor at low altitudes (<500 m).

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Xiao-shan Liu

Papers

Prediction of Soil Texture Using FT-NIR Spectroscopy and PXRF Spectrometry With Data Fusion

Spatial and Temporal Variation in Reference Evapotranspiration and Its Climatic Drivers in Northeast China