Predicting carbon and nitrogen by visible near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy in soils of Northeast Brazil

Santos, Uemeson José dos; Demattê, José Alexandre de Melo; Menezes, Rômulo Simões Cézar; Dotto, André Carnieletto; Guimarães, Carlos Henrique; Alves, Bruno José Rodrigues; Primo, Dário Costa; Sampaio, Everardo Valadares de Sá Barretto

doi:10.1016/j.geodrs.2020.e00333

Cited by 32 publications

(19 citation statements)

References 69 publications

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“…Apart from the difference in costs of the used instruments and the level of portability, there was no obvious benefit of vis-NIR system over mid-IR, or vice versa. The stated error estimates reported in this study and in earlier studies ( Table 1 ) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 39 , 40 ] are higher than those corresponding to traditional analysis, where the laboratory method has a detection limit of 20 g kg −1 for sand and clay (dry basis), and generally, is reproducible to within ±80 g kg −1 , Ref. [ 49 ].…”

Section: Resultssupporting

confidence: 66%

“…These portable instruments are built using semiconductor technologies and they are relatively inexpensive, robust, and simpler to use when compared to laboratory-based bench-top instruments. They are suitable for deployment in the field for proximal sensing [ 1 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Numerous studies support their use for the simultaneous assessment of primary and secondary soil properties on the go [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

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In contrast with classic bench-top hyperspectral (multispectral)-sensor-based instruments (spectrophotometers), the portable ones are rugged, relatively inexpensive, and simple to use; therefore, they are suitable for field implementation to more closely examine various soil properties on the spot. The purpose of this study was to evaluate two portable spectrophotometers to predict key soil properties such as texture and soil organic carbon (SOC) in 282 soil samples collected from proportional fields in four Canadian provinces. Of the two instruments, one was the first of its kind (prototype) and was a mid-infrared (mid-IR) spectrophotometer operating between ~5500 and ~11,000 nm. The other instrument was a readily available dual-type spectrophotometer having a spectral range in both visible (vis) and near-infrared (NIR) regions with wavelengths ranging between ~400 and ~2220 nm. A large number of soil samples (n = 282) were used to represent a wide variety of soil textures, from clay loam to sandy soils, with a considerable range of SOC. These samples were subjected to routine laboratory soil analysis before both spectrophotometers were used to collect diffuse reflectance spectroscopy (DRS) measurements. After data collection, the mid-IR and vis-NIR spectra were randomly divided into calibration (70%) and validation (30%) sets. Partial least squares regression (PLSR) was used with leave one out cross-validation techniques to derive the spectral calibrations to predict SOC, sand, and clay content. The performances of the calibration models were reevaluated on the validation set. It was found that sand content can be predicted more accurately using the portable mid-IR spectrophotometer and clay content is better predicted using the readily available dual-type vis-NIR spectrophotometer. The coefficients of determination (R2) and root mean squared error (RMSE) were determined to be most favorable for clay (0.82 and 78 g kg−1) and sand (0.82 and 103 g kg−1), respectively. The ability to predict SOC content precisely was not particularly good for the dataset of soils used in this study with an R2 and RMSE of 0.54 and 4.1 g kg−1. The tested method demonstrated that both portable mid-IR and vis-NIR spectrophotometers were comparable in predicting soil texture on a large soil dataset collected from agricultural fields in four Canadian provinces.

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Section: Resultssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

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“…Due to the capacity of mineral and organic signature detection, the application of MIR is preferentially centered on clay mineral and humic and fulvic acid studies [21]. However, MIR is increasingly being introduced in similar research where NIR has been used or is even used simultaneously [22,23]. However, there is no research focused on paleoenvironmental reconstruction based on organic matter that uses this spectral range.…”

Section: Introductionmentioning

confidence: 99%

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Dudek

Kabała

Łabaz

et al. 2021

Land

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Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems, although no similar research was conducted based on mid-infrared (MIR). In this paper, the relevance of MIR spectroscopy was tested in studies on the origin/affinity of organic matter from chernozemic soils. Samples collected from three vegetation classes (grasslands, forests and arable lands) were investigated using MIR spectroscopy in order to create a statistical model, which was applied on buried profiles of unknown origin. The results showed a clear disjunction of vegetation classes. Samples of buried soil were placed in the space between all classes, indicating the relation to variable vegetation. Therefore, arable lands should not be omitted in paleoecological reconstructions, because we cannot exclude the cultivation of fertile soils before their burial. It was concluded that MIR methods may have similar applicability to NIR spectroscopy. Additionally, MIR spectra may also be discriminated according to the recognized soil type, which allows for direct reconstructions of the transformation trends in buried profiles.

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“…For example, Xu, Zhao, et al (2019) found that the models trained with MIR were more accurate for predicting soil fertility attributes (e.g., soil organic matter, total nitrogen and available phosphorus) than the VNIR models. Santos et al (2020) obtained better soil organic carbon and nitrogen concentration predictions using the MIR region than the VNIR region. The absorption features of common organic molecules such as amines, carboxylic acids, aromatic compounds, aliphatic hydrocarbons, amides and some phosphorus and sulphur compounds are better in the MIR region (McDowell et al, 2012), which may explain why the MIR models outperform the VNIR models in assessment of organic‐related properties.…”

Section: Discussionmentioning

confidence: 99%

Fusion of visible near‐infrared and mid‐infrared data for modelling key soil‐forming processes in loess soils

Zhao

Hong

Algeo

et al. 2022

European J Soil Science

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Pedogenic weathering of aeolian materials plays an essential role in global nutrient and carbon cycling. A quick and effective approach for temporally high‐resolution and spatially large‐scale pedogenic investigations has long been needed. Here, we used visible and near‐infrared, mid‐infrared and sensor‐fused data to predict pedogenesis‐related soil properties (i.e., grain‐size distribution, clay‐mineral properties and geochemical ratios) in a thick loess sequence in central China. Sensor fusion was achieved at three different levels: (1) direct combination of spectral parameters (low‐level fusion; Fusionpara); (2) combination of spectral features selected by principal component analysis (middle‐level fusion; FusionPCA) and (3) fusion using outer product analysis (high‐level fusion; FusionOPA). Sensor‐fusion generally improves the model predictions for all soil properties, with increases in the values of the model efficiency coefficient (MEC) of 8% and the performance‐to‐interquartile range of 12%. Whole‐soil properties are optimally predicted with the Fusionpara dataset using the random forest algorithm, yielding a mean MEC of 0.85. Spectral parameters D2200/D1900 and AS2200 are demonstrated as promising new pedogenic proxies, with higher values indicating more intense hydrolysis during pedogenesis. Spectral proxies along the loess sequence suggest that intense soil formation occurred during warm and humid interglacial periods when the East Asian summer monsoon intensified. The sensor‐fusion technique improved model performance for assessing mineral transformations and chemical weathering intensity, providing a quick and efficient means of interpreting pedogenic evolution, especially in the case of spatially large‐scale soil investigations. Highlights Fusion of VNIR and MIR spectra to model loess pedogenesis. Spectral modeling provides a robust surrogate for pedogenic evolution studies. Sensor fusion improves the performance of chemometric models. Sensor‐fusion models can reconstruct loess pedogenesis.

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Predicting carbon and nitrogen by visible near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy in soils of Northeast Brazil

Cited by 32 publications

References 69 publications

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils

Fusion of visible near‐infrared and mid‐infrared data for modelling key soil‐forming processes in loess soils

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