Mineralogical analysis of ferralitic soils in Madagascar using NIR spectroscopy

Ramaroson, Volaniaina H.; Becquer, Thierry; Sá, Sandra Oliveira; Razafimahatratra, Hery; Delarivière, Jean Larvy; Breton, Didier; Vendrame, Pedro Rodolfo Siqueira; Rabeharisoa, Lilia; Rakotondrazafy, Amos Fety Michel

doi:10.1016/j.catena.2017.07.016

Cited by 37 publications

(15 citation statements)

References 46 publications

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“…Meanwhile, the wavelength region of 400-490 nm is related to Fe oxides, which are mainly associated with soil color as well as soil organic carbon. Soils in the tropics are rich in Fe and Al (hydr)oxides because of intensive weathering and leaching (Ramaroson et al, 2018), and Fe and Al (hydr)oxides are well known to increase the stability of organic matter in soils through the formation of organo-metal complexes (Van De Vreken et al, 2016). Organic matter is spectrally active in large regions of the Vis-NIR spectrum due to overtones and combinations of NH, CH, and CO groups (Ben-Dor et al, 1997).…”

Section: Selected Wavelength Regions From Dynamic Biplsmentioning

confidence: 99%

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

Kawamura

Nishigaki

Tsujimoto

et al. 2020

Plant Production Science

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Laboratory visible and near-infrared (Vis-NIR) spectroscopy with partial least squares (PLS) regression can be used to determine the soil carbon (C) content, and the waveband selection procedures can refine the predictive ability. However, individually selected wavebands are not always the same depending on the location, scale, and approach. To simplify the variable selection issue, some methods for selecting wavelength regions instead of individual wavebands have been proposed. In this study, we explore relevant wavelength regions for predicting the total carbon (TC) content of lowland and upland soils in Madagascar from Vis-NIR spectroscopy using a dynamic version of backward interval PLS (biPLS) regression. The predictive ability of dynamic biPLS was compared with that of standard full-spectrum PLS (FS-PLS) using the cross-validated coefficient of determination (R 2 ), root mean squared error (RMSE), and ratio of performance to interquartile distance (RPIQ). The biPLS models using reflectance (R 2 = 0.877, RMSE = 0.690) and first derivative reflectance (FDR) (R 2 = 0.940, RMSE = 0.494) data sets showed better predictive accuracy than the FS-PLS models using reflectance (R 2 = 0.826, RMSE = 0.809) and FDR (R 2 = 0.933, RMSE = 0.518) data sets, the spectral efficiency was improved. By using biPLS to predict soil TC, the model was simplified by using only four selected wavelength regions in the

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Section: Selected Wavelength Regions From Dynamic Biplsmentioning

confidence: 99%

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

Kawamura

Nishigaki

Tsujimoto

et al. 2020

Plant Production Science

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“…The commonly selected wavebands in the visible region (454-457, 506-508, 517, 518 and 660 nm) seem to be closely relevant to Fe oxide minerals (Table 3). In addition, the waveband at approximately 2270 nm, which is relevant to gibbsite (Al oxide mineral) [59], had a high regression coefficient and VIP score, although it was not always selected. These results are supported by our previous findings [6], in which oxalate-extractable P was significantly and positively correlated with active Al and Fe, respectively.…”

Section: Selected Wavebands From Ise-pls and Ga-pls Modelsmentioning

confidence: 99%

Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

et al. 2019

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As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.

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“…Thus, it can be concluded that soil P tends to be associated with the visible part of the spectral range, dominated by absorption due to organic matter (OM) and electronic transitions of iron (Fe) in minerals; more specifically, the wavelengths at 454–457, 506–508, 517, 518 and 660 nm seem to be particularly relevant for Fe-oxide minerals [ 43 , 95 , 96 , 97 ]. Additionally, soil P tends to be associated with the higher end of the NIR spectral region where combination bands from intense fundamental vibrations of O-H, C-H bonds and Al metal-OH groups take place; specifically the wavebands at 1720, 2111 and 2300 nm are associated with organic matter (C-H bond) [ 44 , 98 , 99 ] and at around 2270 nm with gibbsite (Al-oxide mineral) [ 100 ].…”

Section: Resultsmentioning

confidence: 99%

Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination

Sánchez-Esteva

Knadel

Kucheryavskiy

et al. 2020

Sensors

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Conventional wet chemical methods for the determination of soil phosphorus (P) pools, relevant for environmental and agronomic purposes, are labor-intensive. Therefore, alternative techniques are needed, and a combination of the spectroscopic techniques—in this case, laser-induced breakdown spectroscopy (LIBS)—and visible near-infrared spectroscopy (vis-NIRS) could be relevant. We aimed at exploring LIBS, vis-NIRS and their combination for soil P estimation. We analyzed 147 Danish agricultural soils with LIBS and vis-NIRS. As reference measurements, we analyzed water-extractable P (Pwater), Olsen P (Polsen), oxalate-extractable P (Pox) and total P (TP) by conventional wet chemical protocols, as proxies for respectively leachable, plant-available, adsorbed inorganic P, and TP in soil. Partial least squares regression (PLSR) models combined with interval partial least squares (iPLS) and competitive adaptive reweighted sampling (CARS) variable selection methods were tested, and the relevant wavelengths for soil P determination were identified. LIBS exhibited better results compared to vis-NIRS for all P models, except for Pwater, for which results were comparable. Model performance for both the LIBS and vis-NIRS techniques as well as the combined LIBS-vis-NIR approach was significantly improved when variable selection was applied. CARS performed better than iPLS in almost all cases. Combined LIBS and vis-NIRS models with variable selection showed the best results for all four P pools, except for Pox where the results were comparable to using the LIBS model with CARS. Merging LIBS and vis-NIRS with variable selection showed potential for improving soil P determinations, but larger and independent validation datasets should be tested in future studies.

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Mineralogical analysis of ferralitic soils in Madagascar using NIR spectroscopy

Cited by 37 publications

References 46 publications

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

Exploring relevant wavelength regions for estimating soil total carbon contents of rice fields in Madagascar from Vis-NIR spectra with sequential application of backward interval PLS

Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar

Combining Laser-Induced Breakdown Spectroscopy (LIBS) and Visible Near-Infrared Spectroscopy (Vis-NIRS) for Soil Phosphorus Determination

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