Espectroscopia de infravermelho na determinação da textura do solo

Ferraresi, Tatiana Maris; Silva, Wilson Tadeu Lopes da; Martin‐Neto, Ladislau; Silveira, Pedro Marques da; Madari, B. E.

doi:10.1590/s0100-06832012000600010

Cited by 8 publications

(4 citation statements)

References 13 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Viscarra-Rossel and Behrens [50] used several statistical algorithms and obtained R 2 between 0.88 and 0.77 for clay. The PCA dispersion presented sequential distribution of spectra of clayey and sandy classes, as reported by Sørensen and Dalsgaard [47], Ferraresi et al [45] and Franceschini et al [36]. Some points overlap between classes due to other soil attributes that influence the spectrum, such as OM and Fe oxides ( Figure 6).…”

Section: Prediction Models Of Soil Attributessupporting

confidence: 58%

“…Individualization of soil textural classes by means of spectroscopy at VIS-NIR-SWIR has been observed and reported by various authors and in Brazilian soils, such as Demattê and Garcia [6], Nanni and Demattê [14], Ferraresi et al [45], Vendrame et al [46] and Franceschini et al [36], among others. This individualization was corroborated in our study.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 84%

“…Nanni and Demattê [14] obtained R 2 0.92 and 0.80 for clay and sand contents, respectively. Ferraresi et al [45] reached R 2 0.85 for clay content quantification and 0.87 for sand in São Paulo and Goiás States. In regional sampling of Latosols (Ferralsols) of the Brazilian Cerrado, Vendrame et al [46] found 0.83 and 0.74 of R 2 for calibration and validation to determine clay content.…”

Section: Prediction Models Of Soil Attributesmentioning

confidence: 94%

See 2 more Smart Citations

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

et al. 2016

View full text Add to dashboard Cite

Abstract:The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400-2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R 2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

show abstract

Section: Prediction Models Of Soil Attributessupporting

confidence: 58%

Section: Principal Component Analysis (Pca)mentioning

confidence: 84%

Section: Prediction Models Of Soil Attributesmentioning

confidence: 94%

See 1 more Smart Citation

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Among the potential methods for predicting clay and SOM contents in soil analysis laboratories, is the near-infrared spectroscopy (NIRS). This technique brings several advantages, such as speed in the analysis execution, easier sample preparation, and requires only a small soil sample to perform readings (FERRARESI et al, 2012). Furthermore, the NIRS is not harmful to the environment, since it uses no chemical extractors (VANDRAME et al, 2015) and the technique is easy to perform.…”

Section: Soil Sciencementioning

confidence: 99%

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

et al. 2020

View full text Add to dashboard Cite

Among the soil constituents, special attention is given to soil organic matter (SOM) and clay contents, since, among other aspects, they are key factors to nutrient retention and soil aggregates formation, which directly affect the crop production potential. The methods commonly used for the quantification of these constituents have some disadvantages, such as the use of chemical reactants and waste generation. An alternative to these methods is the near-infrared spectroscopy (NIRS) technique. The aim of this research is to evaluate models for SOM and clay quantification in soil samples using spectral data by NIRS. A set (n = 400) of soil samples previously analyzed by traditional methods were used to generate a NIRS calibration curve. The clay content was determined by the hydrometer method while SOM content was determined by sulfochromic solution. For calibration, we used the original spectra (absorbance) and spectral pretreatment (Savitzky-Golay smoothing derivative) in the following models: multiple linear regression (MLR), partial last squares regression (PLSR), support vector machine (SVM) and Gaussian process regression (GPR). The curve validation was performed with the SVM model (best performance in the calibration based on R² and RMSE) in two ways: with 40 random samples from the calibration set and another set with 200 new unknown samples. The soil clay content affects the predictive ability of the calibration curve to estimate SOM content by NIRS. Validation curves showed poorer performance (lower R² and higher RMSE) when generated from unknown samples, where the model tends to overestimate the lower levels and to underestimate the higher levels of clay and SOM. Despite the potential of NIRS technique to predict these attributes, further calibration studies are still needed to use this technique in soil analysis laboratories.

show abstract

Carbon functional groups of leaf litter in cacao and rubber agroforestry systems in southern Bahia, Brazil

et al. 2023

View full text Add to dashboard Cite

Espectroscopia de infravermelho na determinação da textura do solo

Cited by 8 publications

References 13 publications

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Prediction of soil organic matter and clay contents by near-infrared spectroscopy - NIRS

Carbon functional groups of leaf litter in cacao and rubber agroforestry systems in southern Bahia, Brazil

Contact Info

Product

Resources

About