Browsing by Author "Chakraborty, Somsubhra"
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Item Combination of proximal and remote sensing methods for rapid soil salinity quantification(2015) Aldabaa, Abdalsamad Abdalsatar Ali; Weindorf, David C. (TTU); Chakraborty, Somsubhra; Sharma, Aakriti (TTU); Li, BinSalt affected soils are pervasive in semiarid and arid regions worldwide. Traditionally, soil salinity has been measured via electrical conductivity (EC). This study evaluated the feasibility of using three different methods for prediction of surface soil salinity, namely visible near infrared diffuse reflectance spectroscopy (VisNIR DRS), portable x-ray fluorescence (PXRF) spectrometry, and remote sensing (RS). Two saline playas were evaluated in West Texas, USA featuring 91 and 74 soils collected via random stratified sampling at 0-5cm and representing a wide variety of soil salinity from high levels inside the playa bottoms to lower levels on the annulus and surrounding uplands. Samples were subjected to PXRF and VisNIR DRS scanning under laboratory conditions, and compared to Landsat spectral data and traditional laboratory analyses of salinity (e.g., 1:5 v/v suspensions). Results showed a broad range of EC (1:5) (0.028 to 43.41dSm-1). Derived from PXRF, both Cl and S were significantly and positively correlated with log10 transformed EC (1:5). VisNIR partial least squares prediction models produced strong residual prediction deviations (RPDs) of 2.49-2.91. Validation statistics of Savitzky-Golay support vector regression outperformed all other VisNIR models tested with an RPD of 3.1. The model using Landsat band reflectance alone produced lowest prediction accuracy (RPD=1.27). While the performance of each technique produced variable success independently, combining the three techniques produced the highest predictability (RPD=3.35). Given that, laboratory determination of EC (1:5) is time consuming and all three types of data (VisNIR DRS, PXRF, and RS) are being quick and easy to collect, their synthesis in predictive models offers excellent potential for providing soil salinity measurements comparable to standard, laboratory derived data. Furthermore, remotely sensed data can potentially be used to map topsoil salinity across large areas with suitable calibrations.Item Rapid quantification of lignite sulfur content: Combining optical and X-ray approaches(2019) Kagiliery, Julia; Chakraborty, Somsubhra; Acree, Autumn (TTU); Weindorf, David C. (TTU); Brevik, Eric C.; Jelinski, Nicolas A.; Li, Bin; Jordan, Cynthia (TTU)Coal is an important natural resource for global energy production. However, certain types of coal (e.g., lignite) often contain abundant sulfur (S) which can lead to gaseous sulfur dioxide (SO2) emissions when burned. Such emissions subsequently create sulfuric acid (H2SO4), thus causing highly acidic rain which can alter the pH of soil and surface waters. Traditional laboratory analysis (e.g., dry combustion) is commonly used to characterize the S content of lignite, but such approaches are laborious and expensive. By comparison, proximal sensing techniques such as portable X-ray fluorescence (PXRF) spectrometry, visible near infrared (VisNIR) spectroscopy, and optical sensors (e.g., NixPro) can acquire voluminous data which has been successfully used to elucidate fundamental chemistry in a wide variety of matrices. In this study, four active lignite mines were sampled in North Dakota, USA. A total of 249 samples were dried, powdered, then subjected to laboratory-based dry combustion analysis and scanned with the NixPro, VisNIR, and PXRF sensors. 75% of samples (n = 186) were used for model calibration, while 25% (n = 63) were used for validation. A strong relationship was observed between dry combustion and PXRF S content (r = 0.90). Portable X-ray fluorescence S and Fe as well as various NixPro color data were the most important variables for predicting S content. When using PXRF data in isolation, random forest regression produced a validation R2 of 0.80 in predicting total S content. Combining PXRF + NixPro improved R2 to 0.85. Dry combustion S + PXRF S and Fe correctly identified the source mine of the lignite at 55.42% via discriminant analysis. Adding the NixPro color data to the PXRF and dry combustion data, the location classification accuracy increased to 63.45%. Even with VisNIR reflectance values of 10–20%, spectral absorbance associated with water at 1940 nm was still observed. Principal component analysis was unable to resolve the mine source of the coal in PCA space, but several NixPro vectors were closely clustered. In sum, the combination of the NixPro optical sensor with PXRF data successfully augmented the predictive capability of S determination in lignite ex-situ. Future studies should extend the approach developed herein to in-situ application with special consideration of moisture and matrix efflorescence effects.