Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data

Terra, Fabrício da Silva; Demattê, José A. M.; Rossel, Raphael A. Viscarra

doi:10.1016/j.geoderma.2015.04.017

Cited by 187 publications

(120 citation statements)

References 57 publications

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“…Terra et al [32] observed that the order of prediction modeling for other non-spectrally detectable soil properties is dependent on their correlation with the detectable ones.…”

Section: Correlations Between Soil Propertiesmentioning

confidence: 99%

“…According to Terra et al [32] soil properties directly detectable by VIS-NIR spectroscopy such as clay, iron and aluminum oxides, hydroxide contents and SOC can be modeled by first-order predictions; however, the order of prediction modeling for other non-spectrally detectable soil properties will be dependent on their correlation with the detectable ones. For example, other properties indicating soil acidity, fertility and mineralogy can be modeled by second-order or even third-order predictions due to the significant correlation with clay and organic carbon content [33].…”

Section: Correlations Between Soil Propertiesmentioning

confidence: 99%

“…Although MIR spectra generally produced more accurate results, especially for SOC, due to its stronger direct relation with MIR spectral data produced by fundamental vibrations, the technology is more complex and expensive than that used for VIS and NIR measurements [27]. Recently, Terra et al [32] observed a R 2 of 0.65 and a RMSE of 0.16 for SOC predictions in the VIS-NIR region, for four states in the Brazilian tropical soils.…”

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

“…All the outliers, reducing SOC prediction, were observed in the O and A-horizon (Figure 3). The presence of non-degraded or partly degraded organic material in the topsoil can increase the prediction uncertainty [30][31][32][33][34][35]. The model was more efficient to predict in the subsurface horizons due to more humified (humic acids) organic matter that has absorption in the VIS-NIR region.…”

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

“…According to the six level interpretations of RPD in [27], these models/predictions were classified as fair and may be used for assessment and correlation. Terra et al [32] predicted physical, chemical and mineralogical soil properties using VIS-NIR and MIR spectroscopy, on 1259 soil samples from four Brazilian states. The authors observed fair predictions only for potential acidity(R 2 : 0.54, RPIQ: 2.01), while other predictions of acidity were unreliable in the VIS-NIR region.…”

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

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Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.

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“…Terra et al [32] observed that the order of prediction modeling for other non-spectrally detectable soil properties is dependent on their correlation with the detectable ones.…”

Section: Correlations Between Soil Propertiesmentioning

confidence: 99%

Section: Correlations Between Soil Propertiesmentioning

confidence: 99%

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

Section: Predictions Of Soil Chemical Propertiesmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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Soil texture and organic matter prediction using Mehlich‐3 extractable nutrients

Drescher,

Slaton,

Roberts

et al. 2024

Agrosystems Geosci & Env

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Soil organic matter (SOM) and texture are key properties influencing soil nutrient and water dynamics but are time‐consuming procedures for analytical laboratories. Our objective was to evaluate SOM and soil texture predictions using Mehlich‐3 nutrients and pH in Arkansas soils. Particle size was determined by the hydrometer method (2‐ and 8‐h readings) and SOM by loss on ignition. Two datasets were used to calibrate clay and sand (n = 409) and SOM (n = 1019) prediction models using simple and multiple regression. Estimated cation exchange capacity was highly correlated with clay, resulting in significant prediction models alone or combined with phosphorus (P); pH and copper (Cu); or pH, sodium (Na), and Cu (R2 = 0.84, 0.88, 0.89, and 0.90; p < 0.0001, respectively). Soil nutrients were weakly correlated with sand, resulting in a prediction model with moderate accuracy when using Mehlich‐3 P, calcium (Ca), Na, iron (Fe), and manganese (Mn) (R2 = 0.49; p < 0.0001). Clay and sand prediction models presented comparable accuracy when validated on a new dataset (n = 103). Predicted sand and clay showed good accuracy in grouping soils into medium (65%) and fine (96%) textural categories but had limited ability to define the coarse‐textural group (9%). SOM had moderate goodness‐of‐fit statistics for calibration and validation datasets using pH, P, Ca, Na, Mn, and zinc (R2 = 0.65 and 0.70, respectively; p < 0.0001). Mehlich‐3 nutrients can be used to estimate soil texture and assist with crop management decisions, but further research is needed to improve SOM prediction.

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Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Chao

Qiao

et al. 2021

Agronomy Journal

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Visible and near-infrared reflectance (Vis-NIR) spectroscopy is considered a promising tool for the estimation of soil properties. Soil clay content and soil organic matter (SOM) are main components affecting soil spectra. Accurate assessment of clay content and SOM is essential before achieving accurate prediction for other soil properties. Selecting the proper spectral transformation technique and optimal calibration method are important processes to improve model performance. In this study, a total of 240 soil samples were collected from the main area of winter wheat (Triticum aestivum L.) fields in the Southwest region of Shanxi province, northern China. Six spectral pre-treatments and three multivariate methods were utilized to realize the estimation of clay content and SOM. Finally, the important spectral wavelengths were identified

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Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data

Cited by 187 publications

References 57 publications

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Soil texture and organic matter prediction using Mehlich‐3 extractable nutrients

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

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