Prediction of Soil Clay Content and Cation Exchange Capacity Using Visible Near-Infrared Spectroscopy, Portable X-ray Fluorescence, and X-ray Diffraction Techniques

Chen, Yuting; Gao, Shibo; Jones, E.V.; Singh, Balwant

doi:10.1021/acs.est.0c04130

Cited by 24 publications

(5 citation statements)

References 52 publications

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“…Nevertheless, the strategy of putting together the areas from different regions is also observed and stated as an effective approach depending on the results demonstrated (49). In this study, although smaller prediction errors were obtained from local model prediction of CEC, geological and global models presented better R 2 and RPIQ and metrics similar to others (18,50,51). However, it is noted that the values obtained from different strategies led to different patterns of attributes spatialization in the field, affecting the spatial dependence as for the geological model or the inversion of patterns as for the global model.…”

Section: Claysupporting

confidence: 63%

Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy

Filho

Molin

2022

Front. Soil Sci.

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In soil science, near-infrared (NIR) spectra are being largely tested to acquire data directly in the field. Machine learning (ML) models using these spectra can be calibrated, adding only samples from one field or gathering different areas to augment the data inserted and enhance the models’ accuracy. Robustness assessment of prediction models usually rely on statistical metrics. However, how the spatial distribution of predicted soil attributes can be affected is still little explored, despite the fact that agriculture productive decisions depend on the spatial variability of these attributes. The objective of this study was to use online NIR spectra to predict soil attributes at field level, evaluating the statistical metrics and also the spatial distribution observed in prediction to compare a local prediction model with models that gathered samples from other areas. A total of 383 online NIR spectra were acquired in an experimental field to predict clay, sand, organic matter (OM), cation exchange capacity (CEC), potassium (K), calcium (Ca), and magnesium (Mg). To build ML calibrations, 72 soil spectra from the experimental field (local dataset) were gathered, with 59 samples from another area nearby, in the same geological region (geological dataset) and with this area nearby and more 60 samples from another area in a different region (global dataset). Principal components regression was performed using k-fold (k=10) cross-validation. Clay models reported similar errors of prediction, and although the local model presented a lower R2 (0.17), the spatial distribution of prediction proved that the models had similar performance. Although OM patterns were comparable between the three datasets, local prediction, with the lower R2 (0.75), was the best fitted. However, for secondary NIR response attributes, only CEC could be successfully predicted and only using local dataset, since the statistical metrics were compatible, but the geological and global models misrepresented the spatial patterns in the field. Agronomic plausibility of spatial distribution proved to be a key factor for the evaluation of soil attributes prediction at field level. Results suggest that local calibrations are the best recommendation for diffuse reflectance spectroscopy NIR prediction of soil attributes and that statistical metrics alone can mispresent the accuracy of prediction.

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Section: Claysupporting

confidence: 63%

Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy

Filho

Molin

2022

Front. Soil Sci.

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“…Current studies relevant to the quantitative estimation of soil nutrients [35,36], soil heavy metals [37,38] and other soil properties [32,39,40] using hyperspectral techniques are relatively mature. However, research on soil exchangeable cations is relatively minimal.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning

Peng

Wang

Xie³

et al. 2023

Agriculture

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Soil exchange cations are a basic indicator of soil quality and environmental clean-up potential. The accurate and efficient acquisition of information on soil cation content is of great importance for the monitoring of soil quality and pollution prevention. At present, few scholars focus on soil exchangeable cations using remote sensing technology. This study proposes a new method for estimating soil cation content using hyperspectral data. In particular, we introduce Boruta and successive projection (SPA) algorithms to screen feature variables, and we use Guangdong Province, China, as the study area. The backpropagation neural network (BPNN), genetic algorithm–based back propagation neural network (GABP) and random forest (RF) algorithms with 10-fold cross-validation are implemented to determine the most accurate model for soil cation (Ca2+, K+, Mg2+, and Na+) content estimations. The model and hyperspectral images are combined to perform the spatial mapping of soil Mg2+ and to obtain the spatial distribution information of images. The results show that Boruta was the optimal algorithm for determining the characteristic bands of soil Ca2+ and Na+, and SPA was the optimal algorithm for determining the characteristic bands of soil K+ and Mg2+. The most accurate estimation models for soil Ca2+, K+, Mg2+, and Na+ contents were Boruta-RF, SPA-GABP, SPA-RF and Boruta-RF, respectively. The estimation effect of soil Mg2+ (R2 = 0.90, ratio of performance to interquartile range (RPIQ) = 3.84) was significantly better than the other three elements (Ca2+: R2 = 0.83, RPIQ = 2.47; K+: R2 = 0.83, RPIQ = 2.58; Na+: R2 = 0.85, RPIQ = 2.63). Moreover, the SPA-RF method combined with HJ-1A HSI images was selected for the spatial mapping of soil Mg2+ content with an R2 of 0.71 and RPIQ of 2.05. This indicates the ability of the SPA-RF method to retrieve soil Mg2+ content at the regional scale.

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“…Vis-NIR spectrometers with the point scanning method are easily carried in the field or on-site detection for their small volume and high reliability ( Kawamura et al., 2021 ). However, due to the influence of overtone and combined vibration, the absorbance characteristics in the visible near-infrared spectrum are usually low ( Chen et al., 2021 ). HSI combined imaging and spectroscopy techniques to obtain high-resolution spectral-spatial information in visible and near-infrared regions ( Jun et al., 2019 ).…”

Section: Theoretical Basis Of Spectroscopymentioning

confidence: 99%

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

Ren

Cui

et al. 2022

Front. Plant Sci.

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Heavy metal elements, which inhibit plant development by destroying cell structure and wilting leaves, are easily absorbed by plants and eventually threaten human health via the food chain. Recently, with the increasing precision and refinement of optical instruments, optical imaging spectroscopy has gradually been applied to the detection and reaction of heavy metals in plants due to its in-situ, real-time, and simple operation compared with traditional chemical analysis methods. Moreover, the emergence of machine learning helps improve detection accuracy, making optical imaging spectroscopy comparable to conventional chemical analysis methods in some situations. This review (a): summarizes the progress of advanced optical imaging spectroscopy techniques coupled with artificial neural network algorithms for plant heavy metal detection over ten years from 2012-2022; (b) briefly describes and compares the principles and characteristics of spectroscopy and traditional chemical techniques applied to plants heavy metal detection, and the advantages of artificial neural network techniques including machine learning and deep learning techniques in combination with spectroscopy; (c) proposes the solutions such as coupling with other analytical and detection methods, portability, to address the challenges of unsatisfactory sensitivity of optical imaging spectroscopy and expensive instruments.

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Prediction of Soil Clay Content and Cation Exchange Capacity Using Visible Near-Infrared Spectroscopy, Portable X-ray Fluorescence, and X-ray Diffraction Techniques

Cited by 24 publications

References 52 publications

Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy

Spatial distribution as a key factor for evaluation of soil attributes prediction at field level using online near-infrared spectroscopy

Estimation of Soil Cations Based on Visible and Near-Infrared Spectroscopy and Machine Learning

Optical imaging spectroscopy coupled with machine learning for detecting heavy metal of plants: A review

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