Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes

Rizzo, Rodnei; Demattê, José Alexandre Melo; Lepsch, I. F.; Gallo, Bruna Cristina; Fongaro, Caio Troula

doi:10.1016/j.geoderma.2016.03.019

Cited by 48 publications

(23 citation statements)

References 22 publications

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“…Rizzo et al . () and Teng et al . () have already demonstrated the potential use of vis–NIR spectra in updating soil class maps from a local to national scale.…”

Section: Resultsmentioning

confidence: 99%

“…We are not proposing that this is as yet a replacement for laboratory analysis and expert knowledge, but vis-NIR spectroscopy offers a new possibility for rapid soil classification based on legacy soil data and therefore more detailed soil class maps. Rizzo et al (2016) and Teng et al (2018) have already demonstrated the potential use of vis-NIR spectra in updating soil class maps from a local to national scale. Furthermore, the vis-NIR technique makes it possible to update soil databases rapidly when a more objective soil classification system is developed.…”

Section: Classification Performance At the Suborder Levelmentioning

confidence: 99%

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Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

Chen

Ma³

et al. 2018

European J Soil Science

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Summary Visible‐near infrared (vis–NIR) spectroscopy can reveal various soil properties and facilitate soil classification. However, few studies have attempted to classify vertical soil profiles that contain several genetic horizons. Here, we propose the ‘multiple objectives mixed support vector classification’ (MOM–SVC) method to classify soil profiles. A total of 130 soil profiles were collected from genetic horizons in Zhejiang Province, China. After laboratory analysis, soil profiles were classified according to the Chinese Soil Taxonomy system. Vis–NIR spectra were recorded from each genetic horizon of each soil profile and were then pre‐processed. We performed the MOM–SVC method as follows: (i) created a support vector machine (SVM) model (one‐versus‐one approach) using spectral data from all soil horizons in calibration profiles, (ii) applied the SVM model on each horizon of the profile to be predicted, (iii) extracted ‘votes’ from each horizon and mixed (or summarized) them into the votes of each profile to be predicted and (iv) classified each profile by the majority‐voting method. We also investigated whether the additional input of auxiliary soil information (e.g. moist soil colour, soil organic matter and soil texture), which could be measured easily or be well predicted by vis–NIR spectroscopy, could improve the accuracy of soil classification when combined with it. Independent validation results showed that the MOM–SVC method performed better at the soil order level than at the suborder level. Adding auxiliary soil information to the classification model improved the overall accuracy of classification at the soil order level. The proposed MOM–SVC method provides a fast objective diagnostic of soil classes for use in soil surveys and can help to update soil databases when a more objective soil classification system is developed. Highlights The MOM–SVC method can be used to classify soil profiles objectively with a variety of soil horizons. Stratified random sampling was used to quantify prediction uncertainty in classification MOM–SVC can predict soil orders with greater accuracy than suborders. Adding auxiliary soil information into the classification model improved prediction accuracy.

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“…Rizzo et al . () and Teng et al . () have already demonstrated the potential use of vis–NIR spectra in updating soil class maps from a local to national scale.…”

Section: Resultsmentioning

confidence: 99%

Section: Classification Performance At the Suborder Levelmentioning

confidence: 99%

Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

Chen

Ma³

et al. 2018

European J Soil Science

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

Section: Introductionmentioning

confidence: 99%

“…In addition, despite the development of spectral libraries, it is important to show how to use them for this purpose [24]. Therefore, we must merge the punctual information as described by Digital Soil Morphometrics [7] discipline with the spatial domain on DSM.…”

Section: Introductionmentioning

confidence: 99%

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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“…Dessa forma, para avançar na caracterização quantitativa da área foi necessário utilizar um modelo digital de elevação (MDE), assim como nos diversos estudos de mapeamento digital de solos (Rizzo et al, 2016;Demattê et al, 2017. Para produzir o MDE foi utilizada a rede de drenagem, pontos de elevação coletados em campo e um mapa de curvas altimétricas com equidistância vertical de 5 m, proveniente da digitalização de cartas altimétricas do Instituto Geográfico de Campinas (IGC).…”

Section: Parâmetros Morfométricos De Relevo E Rede De Drenagemunclassified

Compartimentação da paisagem via relevo e rede de drenagem e sua relação com atributos e classes de solos

Mello¹,

Demattê²

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Rede de drenagem 2. Compartimentação automática da paisagem 3. Mapeamento digital de solos 4. Estereoscopia digital I. Título AGRADECIMENTOS Agradeço primeiramente à Deus pelo sustento e por sempre me surpreender com sua bondade. Sem isso seria impossível caminhar. Agradeço imensamente à minha família, pois sempre me apoiaram e deram suporte em todos os momentos desse caminho. Agradeço em especial ao meu Pai, Nilton. Todo o seu esforço valeu a pena. Reconheço que se não fossem os seus finais de semana perdidos eu não estaria aqui. Serei eternamente grato e sei que está orgulhoso. Agradeço à minha mãe pelo amor e carinho. A condição de fazer esse curso era você estar bem e hoje está curada. O tempo passou mas a saudade ainda aperta. Obrigado por me incentivar sempre. Agradeço à meu irmão Luccas. Nunca se contente com pouco, seja o melhor que puder em tudo. Você vai mais longe do que pode imaginar. Agradeço à Ana, minha noiva, meu amor. Você deixou a caminhada mais leve e mais bela. Obrigado pelo apoio e compreensão nos tempos difíceis. Você é uma mulher incrível! Te amo! Agradeço à minha segunda família, Nivaldo e Hilda. Obrigado por me tratarem como filho e não pouparem esforços para ajudar quando preciso. Vocês também tornaram a caminhada muito mais facil. Viva o vinho! Agradeço aos novos irmãosTaís, Pedro e Sara. A caminhada está só começando. Agradeço aos amigos e a Igreja no Jupiá.

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Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes

Cited by 48 publications

References 22 publications

Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

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

Compartimentação da paisagem via relevo e rede de drenagem e sua relação com atributos e classes de solos

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