Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths

Vasques, Gustavo M.; Demattê, José A. M.; Rossel, Raphael A. Viscarra; López, Luciano Espino; Terra, Fabrício da Silva

doi:10.1016/j.geoderma.2014.01.019

Cited by 99 publications

(60 citation statements)

References 13 publications

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“…In subsurface layers, there is more evidence of absorption bands within 1400 -1900 nm because of the OH -molecule of soil hygroscopic water (Ben-Dor, 2002) and the presence of 2:1 minerals (Demattê et al, 2004a). Thus, our results showed types of curves that differentiate soil samples from surface and subsurface layers, corroborating Demattê et al (2004a), Rizzo et al (2014) and Vasques et al (2014).…”

Section: Spectral Curves As Indicators For Soil Classificationsupporting

confidence: 77%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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Section: Spectral Curves As Indicators For Soil Classificationsupporting

confidence: 77%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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“…However, they did not show, at that time, how to use the information to achieve classification. With the aim of analyzing spectra by shapes and features, Demattê (2002) identified, much later, the differences between tropical soils, that were subsequently used and ratified in case studies by Vasques et al (2014), SorianoDisla et al (2014) and Fiorio et al (2014). But they still did not have a method that integrated quantitative and descriptive analysis.…”

Section: Introductionmentioning

confidence: 99%

Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

Demattê

Bellinaso²,

Romero

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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The search for tools to perform soil surveying faster and cheaper has led to the development of technological innovations such as remote sensing (RS) and the so-called spectral libraries in recent years. However, there are no studies which collate all the RS background to demonstrate how to use this technology for soil classification. The present study aims to describe a simple method of how to classify soils by the morphology of spectra associated with a quantitative view (400-2,500 nm). For this, we constructed three spectral libraries: (i) one for quantitative model performance; (ii) a second to function as the spectral patterns; and (iii) a third to serve as a validation stage. All samples had their chemical and granulometric attributes determined by laboratory analysis and prediction models were created based on soil spectra. The system is based on seven steps summarized as follows: i) interpretation of the spectral curve intensity; ii) observation of the general shape of curves; iii) evaluation of absorption features; iv) comparison of spectral curves between the same profile horizons; v) quantification of soil attributes by spectral library models; vi) comparison of a pre-existent spectral library with unknown profile spectra; vii) most probable soil classification. A soil cannot be classified from one spectral curve alone. The behavior between the horizons of a profile, however, was correlated with its classification. In fact, the validation showed 85 % accuracy between the Morphological Interpretation of Reflectance Spectrum (MIRS) method and the traditional classification, showing the importance and potential of a combination of descriptive and quantitative evaluations.

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“…Indeed, spectroscopy quantifies important soil attributes looking for soil classification [20,21]. This strategy also indicates its usefulness for soil classification as performed by a multi-depth spectral analysis [22]. The use of spectral libraries are an important strategy for attributes quantification, as performed by Shepherd, K.D.…”

Section: Introductionmentioning

confidence: 99%

“…; Walsh [21] and Shi et al [23]. Additionally, spectral libraries have also been used on soil classification [22,24,25].…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

Demattê¹,

López

Rizzo

et al. 2016

Remote Sensing

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There is a consensus about the necessity to achieve a quick soil spatial information with few human resources. Remote/proximal sensing and pedotransference are methods that can be integrated into this approach. On the other hand, there is still a lack of strategies indicating on how to put this in practice, especially in the tropics. Thus, the objective of this work was to suggest a strategy for the spatial prediction of soil classes by using soil spectroscopy from ground laboratory spectra to space images platform, as associated with terrain attributes and spectral libraries. The study area is located in São Paulo State, Brazil, which was covered by a regular grid (one per ha), with 473 boreholes collected at top and undersurface. All soil samples were analyzed in laboratory (granulometry and chemical), and scanned with a VIS-NIR-SWIR (400-2500 nm) spectroradiometer. We developed two traditional pedological maps with different detail levels for comparison: TFS-1 regarding orders and subgroups; and TFS-2 with additional information such as color, iron and fertility. Afterwards, we performed a digital soil map, generated by models, which used the following information: (i) predicted soil attributes from undersurface layer (diagnostic horizon), obtained by using a local spectral library; (ii) spectral reflectance of a bare soil surface obtained by Landsat image; and (iii) derivative of terrain attributes. Thus, the digital map was generated by a combination of three variables: remote sensing (Landsat data), proximal sensing (laboratory spectroscopy) and relief. Landsat image with bare soil was used as a first observation of surface. This strategy assisted on the location of topossequences to achieve soil variation in the area. Afterwards, spectral undersurface information from these locations was used to modelize soil attributes quantification (156 samples). The model was used to quantify samples in the entire area by spectra (other 317 samples). Since the surface was bare soil, it was sampled by image spectroscopy. Indeed, topsoil spectral laboratory information presented great similarity with satellite spectra. We observed angle variation on spectra from clayey to sandy soils as differentiated by intensity. Soil lines between bands 3/4 and 5/7 were helpful on the link between laboratory and satellite data. The spectral models of soil attributes (i.e., clay, sand, and iron) presented a high predictive performance (R 2 0.71 to 0.90) with low error. The spatial prediction of these attributes also presented a high performance (validations with R 2 > 0.78). The models increased spatial resolution of soil weathering information using a known spectral library. Elevation (altitude) improved mapping due to correlation with soil attributes (i.e., clay, iron and chemistry). We observed a close relationship between soil weathering index map and laboratory spectra + image spectra + relief parameters. The color composite of the 5R, 4G and 3B had great performance on the detection of soils along topossequences, since...

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Soil classification using visible/near-infrared diffuse reflectance spectra from multiple depths

Cited by 99 publications

References 13 publications

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Morphological Interpretation of Reflectance Spectrum (MIRS) using libraries looking towards soil classification

Remote Sensing from Ground to Space Platforms Associated with Terrain Attributes as a Hybrid Strategy on the Development of a Pedological Map

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