Diffuse Reflectance Spectroscopy Approach Research Articles

A novel coal quality index and its estimation using diffuse reflectance spectroscopy

ABSTRACT An essential component of coal’s efficient use and large-scale environmental impact management is evaluating its quality. This study introduces a novel quality indexing technique that incorporates multiple coal properties, providing a comprehensive measure of coal quality. Conventional methods for assessing coal quality, such as proximate and ultimate analyses, are often time-consuming, costly, and require extensive sample preparation, prompting the need for a more efficient approach. In this study, we used the minimum dataset (MDS) approach to estimate a comprehensive coal quality index (CQI) and show that the diffuse reflectance spectroscopy (DRS) may be used as a rapid, nondestructive technique for estimating CQI as a single quality indicator for coal. A total of 212 coal samples of different ranks and grades are collected from the Tertiary and Gondwana coal basins of India. Ten chemical properties of coal were measured following the conventional laboratory-based methods. The spectral responses of the coal samples were measured across the visible-NIR-SWIR (350–2500 nm) range. The MDS approach was adopted while estimating the CQI from the lab-based coal quality parameters. We also examined different chemometric models for estimating CQI values from spectroscopic data. Results showed that the feature selection-based partial-least-square regression (PLSRFS) model may be used to estimate CQI with coefficient of determination (R2) values as high as 0.7 with root-mean-squared error (RMSE) of 0.06. While CQI serves as a single coal quality parameter, the capability to use the DRS approach as a rapid and noninvasive sensing approach for coal quality assessment provides new opportunity to characterize coal quality in an instant and cost-effective manner, especially, when a large number of coal samples have to be tested. The integration of the CQI with DRS could facilitate better decision-making in coal utilization and management, leading to optimized performance and reduced environmental impact.

Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Diffuse reflectance spectroscopy and digital soil mapping for assessing soil-associated off-road vehicle mobility risk

Soil attributes such as granulometric fractions and Atterberg limits (LL: liquid limit, PL: plastic limit, and PI: plasticity index) are needed to assess off-road vehicle mobility (OVM) risks. Parameters describing these attributes are generally measured in soil samples collected from a few locations through cumbersome laboratory methods. Although diffuse reflectance spectroscopy (DRS) can rapidly yield estimates for soil attributes in samples collected from specific locations and digital soil mapping (DSM) can transform such discrete measurements into spatially-continuous inference systems, these two technologies are rarely used for assessing OVM risks. In this study, we combined the DRS and DSM approaches for deriving spatially-continuous estimates for the key vehicle mobility parameters (gravel, sand, and fine particles; Cu: coefficients of uniformity; Cc: coefficient of curvature; LL; and PI) and classified soils using the Unified Soil Classification System (USCS). A total of 204 soil samples were collected from the north-eastern Himalayan state of Sikkim for measuring these parameters along with spectral reflectance over the visible and near-infrared region. Results of the chemometric models in the DRS approach showed that the USCS parameters may be estimated with the coefficient of determination (R2) values as high as 0.72. The fine (<2 mm diameter) fraction spectra provided the best estimates for the Atterberg limits while a combination of spectra collected from fine and coarse (>2 mm diameter) fractions was effective in estimating other granulometric fractions except for sand, which was best estimated using the coarse fraction spectra. With the DSM approach allowing effective mapping of these parameters, a spatially-continuous framework to quantify soil-associated OVM risks was developed for Sikkim for the first time.

Diffuse reflectance spectroscopy (DRS) for rapid soil testing and soil quality assessment in smallholder farms

AbstractRapid soil testing and soil quality assessment are essential to address soil degradation and low farm incomes in smallholder farms. With the objective of testing diffuse reflectance spectroscopy (DRS) to rapidly assess soil chemical properties, nutrient content and a soil quality index (SQI), samples of surface soil were collected from 1113 smallholder farms in seven districts in Bundelkhand region of Uttar Pradesh, India. A minimum dataset (MDS) approach was followed to estimate SQI using the three chemical parameters of soil pH, electrical conductivity (EC) and soil organic carbon (SOC), and 11 different soil nutrients. Principal component and correlation analyses showed that soil pH, SOC content and three available nutrients − copper (Cu), iron (Fe) and sulphur (S) − may constitute the MDS. Estimated SQI values showed strong positive correlation with crop yields. Results of chemometric modelling showed that the DRS approach could yield the coefficient of determination (R2) values in the validation datasets ranging from 0.79 to 0.94 for exchangeable calcium (Ca) followed by 0.67–0.88 for exchangeable potassium (K), 0.52–0.86 for SOC and 0.53–0.81 for available boron (B) content. Except in one district, the DRS approach could be used to estimate SQI values with R2 values in the range of 0.63–0.81; an R2 value of 0.71 was obtained in the pooled dataset. We also estimated the three‐tier soil test crop response (STCR) ratings to compare DRS and wet chemistry soil testing approaches. Similar STCR ratings were obtained for both these approaches in more than 86% of the samples. Parameters for which both the methods yielded similar ratings in more than 80% of the samples were EC (&gt;98%), pH and exchangeable Ca (&gt;81%) and available B (&gt;89%). With similar ratings, these results suggest that the DRS approach may safely be used for farmers' fields, replacing the traditional wet analysis approach of soil testing.

Assessment of Soil Properties using Spectral Signatures of Bulk Soils and Their Aggregate Size Fractions

Diffuse reflectance spectroscopy (DRS) is an emerging alternative to conventional laboratory methods of soil testing although poor estimation accuracies continue to limit its wide-spread adoption. Spectral reflectance values of different aggregate size fractions and their bulk soil have recently been shown to improve the estimation accuracy of soil textural fractions in the DRS approach. The objective of the present study is to assess this approach for estimating nine different soil parameters covering soil structure, soil chemical properties and micronutrient contents. We used three different chemometric models (PLSR: partial-least-squares regression, PLSRFS: PLSR with feature selection, and PLSRLW: locally-weighted PLSR) on three different spectral libraries (total of 1013 soil samples) to test this objective. On an average, 29% improvement in the coefficient of determination (R2) was achieved with the addition of aggregate spectra to bulk soil spectra. Improvement in the root-mean-squared error (RMSE) ranged from 5% to 48% for aggregate size distribution parameters, 7% to 40% for chemical properties, and 7% to 26% for micronutrient contents. These improvements were also observed to be a result of high correlation coefficients between estimated soil properties and the best-performing aggregate fraction spectra, which suggested that the spectral reflectance of individual aggregate size fractions holds important soil information for improving the performance of a DRS model. Segregation of soils into individual size fractions and the collection of corresponding spectra are additional burdens in the proposed approach. Nevertheless, the extent of improvements in the model performance observed in this study opens an opportunity to develop an automated system for separating soil aggregates and collecting spectra so that the information may readily be used for improving the performance of the DRS approach.

Assessment of cocoa input needs using soil types and soil spectral analysis

AbstractIn this study, diffuse reflectance spectroscopy (DRS) approach was examined for making input recommendations in the smallholder cocoa farms of Papua New Guinea (PNG). Soil samples were collected from four provinces of PNG. Soil samples from four different depths (0–10, 10–30, 30–60 and 60–90 cm) of 32 profiles in each of these site were used to create a database of soil chemical and physical properties. Spectral reflectance values at 1 nm interval covering visible to shortwave‐infrared (350–2,500 nm) were collected for each of these soil samples to develop partial least squares regression models. Soil textural fractions, soil organic carbon contents and available N were well predicted by the DRS approach with R2 values larger than 0.75. Moderate to poor estimation efficiencies were observed for remaining parameters. Nevertheless, the estimated soil attributes and their corresponding measured soil parameters were used as inputs to an input recommendation model of soil diagnosis to create input recommendation for a targeted cocoa yield of 1,000 kg dry cocoa beans ha‐1 Resulting input recommendations were similar for both of these input sources (measured and DRS‐estimated) suggesting that the DRS approach may provide an easy way to create input recommendations.

Assessment of soil texture from spectral reflectance data of bulk soil samples and their dry-sieved aggregate size fractions

Diffuse reflectance spectroscopy (DRS) continues to emerge as a rapid approach for estimating several soil properties including soil texture. To improve the accuracy of DRS approach for soil texture assessment, a data fusion approach was tested in which the reflectance spectra of bulk soil and its aggregate size fractions obtained by dry sieving approach were combined with three chemometric models such as partial-least-squares regression (PLSR), a locally-weighted PLSR (PLSRLW) and PLSR with feature selection (PLSRFS). This newly developed approach was tested using three different soil (Vertisols, Alfisols and Mixed) spectral libraries containing a total of 1013 soil samples each with 9 different aggregate size fractions. Because soil aggregation is influenced by the nature of clay minerals in addition to organic carbon, iron and aluminum oxides, XRD analyses were carried out in selected aggregate fractions. Results showed that there was reasonable improvement in the estimation accuracy of sand, silt and clay contents in KVS and KAS when an aggregate size fraction spectrum was added to bulk soil spectrum. For example, consideration of PLSRFS or PLSRLW models along with best-performing aggregate spectra could reduce RMSE values in the validation datasets in the order of 18% for clay to 31% for silt in KVS samples, 32% for silt to 49% for clay in KAS samples, and 6% for sand to 15% for silt in WBODS samples compared to when PLSR model was used alone in the F0 spectra. Correlation analysis showed that textural fractions were more strongly correlated to spectra of macroaggregates (aggregate size >0.20 mm) than those of microaggregates (aggregate size <0.13 mm). Correlation analysis between soil separates and the amount of soil mass within each aggregate size fraction showed that the microaggregates contained more information for soil textural components than macroaggregates. The proposed approach of using bulk soil spectra and aggregate fraction spectra may be a step forward in texture analysis through DRS approach. Although extra time is required for collecting soil aggregate fraction spectra, the method has advantage of allowing the measurement of geometric mean diameter using traditional methods.

Rapid assessment of black tea quality using diffuse reflectance spectroscopy

Tea quality assessment methods generally require elaborate sample preparation steps and often yield in consistent results. Over the last two decades, the diffuse reflectance spectroscopy (DRS) is emerging as a rapid and non-invasive tool for assessing quality parameters of several materials. In this study¸ we examined the DRS approach by creating a reflectance spectral imaging of 81 black crush, tear and curl (CTC) tea samples and eight tea quality parameters. Spectral models for each parameter were developed using the partial-least-squares regression (PLSR) approach. The ratio of performance deviation (RPD) was used to assess such spectral models. Results showed that moisture content, thearubigin components of TRSI and TRSII, total polyphenol contents and liquor brightness were accurately predicted with RPD values 3.63, 2.32, 2.24, 2.23 and 2.02, respectively. Prediction accuracies were moderate for thearubigin, total liquor color and theaflavin. The variable important projection (VIP) showed the wavelengths around near-infrared and shortwave-infrared regions strongly influence spectral reflectance of black tea.

Hyperspectral remote sensing: opportunities, status and challenges for rapid soil assessment in India

Rapid and reliable assessment of soil characteristics is an important step in agricultural and natural resource management. Over the last few decades, diffuse reflec-tance spectroscopy (DRS) has emerged as a new tool to obtain both qualitative and quantitative informa-tion on soil in a non-invasive manner. The DRS ap-proach is attractive because both the proximal and remote mode of measurements may be adopted to es-timate multiple attributes of soil such as physical and chemical soil properties and nutrient contents from a single reflectance spectrum. Hyperspectral imaging cameras onboard remote sensing platforms are already providing hundreds of narrow, contiguous bands of reflectance values and the technology is becoming popular as the hyperspectral remote sensing (HRS) approach. The main objective of this review is to summarize the preparedness and opportunities for using the HRS approach for soil assessment in India. Detailed literature review suggests that the HRS approach requires large spectral databases and robust spectral algorithms in addition to the capability to in-terpret HRS images. Over the last decade, few efforts have been made to create spectral libraries for Indian soils. However, most of these libraries are very small, precluding the development of robust spectral algo-rithms. Specifically, the availability of HRS data and robust retrieval algorithms for soil properties from HRS data through unmixing procedures require spe-cial attention. With several global initiatives to make HRS data available, coordinated efforts are needed in India to build comprehensive spectral libraries, algo-rithms and create trained human resources to take full advantage of this emerging technology. Specifi-cally, a dedicated spaceborne mission will provide quality hyperspectral data for the effective application of HRS for soil assessment in India. Keywords:

Comparison of Data Mining Approaches for Estimating Soil Nutrient Contents Using Diffuse Reflectance Spectroscopy

Diffuse reflectance spectroscopy (DRS) operating in wavelength range of 350-2500 nm is emerging as a rapid and non-invasive approach for estimating soil nutrient content. The success of the DRS approach relies on the ability of the data mining algorithms to extract appropriate spectral features while accounting for non-linearity and complexity of the reflectance spectra. There is no comparative assessment of spectral algorithms for estimating nutrient content of Indian soils. We compare the performance of partialleast- squares regression (PLSR), support vector regression (SVR), discrete wavelet transformation (DWT) and their combinations (DWT-PLSR and DWT-SVR) to estimate soil nutrient content. The DRS models were generated for extractable phosphorus (P), potassium (K), sulphur (S), boron (B), zinc (Zn), iron (Fe) and aluminium (Al) content in Vertisols and Alfisols and were compared using residual prediction deviation (RPD) of validation dataset. The best DRS models yielded accurate predictions for P (RPD = 2.27), Fe (RPD = 2.91) in Vertisols and Fe (RPD = 2.43) in Alfisols, while B (RPD = 1.63), Zn (RPD = 1.49) in Vertisols and K (RPD = 1.89), Zn (RPD = 1.41) in Alfisols were predicted with moderate accuracy. The DWT-SVR outperformed all other approaches in case of P, K and Fe in Vertisols and P, K and Zn in Alfisols; whereas, the PLSR approach was better for B, Zn and Al in Vertisols and B, Fe and Al in Alfisols. The DWT-SVR approach yielded parsimonious DRS models with similar or better prediction accuracy than PLSR approach. Hence, the DWT-SVR may be considered as a suitable data mining approach for estimating soil nutrients in Alfisols and Vertisols of India.

Diffuse Reflectance Spectroscopic Approach for the Characterization of Soil Aggregate Size Distribution

Assessment of soil structure and soil aggregation remains a challenging task. Routine methods such as dry- and wet-sieving approaches are generally time consuming and tedious, which calls for a robust, fast, and nondestructive method of soil aggregate characterization. Over the last two decades, diffuse reflectance spectroscopy (DRS) has emerged as a rapid and noninvasive technique for soil characterization. Combined with chemometric and data-mining algorithms, it provides an effective way of measuring several soil attributes and has the added advantage of being amenable to a remote sensing mode of operation. The objective of this study was to determine if the DRS approach could be used as a rapid, noninvasive technique to estimate soil aggregate characteristics. The DRS approach was examined for the estimation of soil aggregate characteristics such as the geometric mean diameter and two statistical parameters of the lognormal aggregate size distribution (ASD) functions using 910 soil samples from India representing three important soil groups. Results showed that the geometric mean diameter and the median aggregate size parameter provided excellent predictions, with ratio of performance deviation (RPD) values ranging from 1.99 to 2.28. The RPD value for the standard deviation of the ASD ranged from 1.36 to 1.72, suggesting moderate prediction. It was further observed that soil aggregates influence the incident electromagnetic radiation on soils primarily in the visible region and to some extent the shortwave- and near-infrared regions. Electronic transitions of Fe-bearing minerals, clay minerals, and C–H functional groups of organic matter may be responsible for modifying the spectral reflectance from soils in addition to the self-shadowing effects of surface roughness. The results of this study suggest that the chemometric approach may be combined with DRS to estimate soil aggregate size characteristics.

Evolution of the Ni2+ Environment During the Formation of a MgO–Al2O3–SiO2 Glass‐Ceramic: A Combined XRD and Diffuse Reflectance Spectroscopy Approach

The effect of the addition of NiO on the crystallization mechanisms has been studied for SiO2–Al2O3–MgO glass‐ceramics. A detailed Rietveld analysis has been made to determine the chemical composition and the cation distribution in the spinel phase and the evolution of Ni2+ coordination site using diffuse reflectance spectroscopy. Powder X‐ray diffraction (XRD) results show the formation of β‐quartz and spinel and the nickel fraction in the spinel phase decreasing as the plateau temperature increases. The determination of cation distribution in spinel using XRD reveals a preference of Ni2+ and Al3+ for octahedral sites and an increasing proportion of Ni2+ in tetrahedral sites for heat treatments at high temperature (1100°C–1200°C). Diffuse reflectance spectroscopy shows that Ni2+ is mainly fivefold coordinated in the starting glass, playing an important role in the crystallization of the spinel phase and it confirms the presence of tetrahedral and octahedral Ni2+ in the glass‐ceramics. The combination of these two techniques also indicates a chemical heterogeneity of spinel crystals with Ni2+‐rich and Mg2+‐rich zones.