Surface Spectroscopy of Oxisols, Entisols and Inceptisol and Relationships with Selected Soil Properties

Abstract

Traditional method of soil survey is expensive, slow, and must be carried out by experienced researchers. Thus, advances in soil observation technologies and the need to obtain information quickly by modern techniques have intensified the use of proximal sensing. This study characterized surface reflectance spectra (A horizon) and related them to traditional soil classification, based on morphological, physical, and chemical properties of representative pedogenetic profiles, developed in two toposequences of the Distrito Federal, Brazil. In the toposequences, 15 soil profiles were selected for a complete morphological description and sampling for laboratory analyses. Soil-landscape relationships were established, and profiles were classified to the fourth level of the Brazilian Soil Classification System (SiBCS). Classes of similar soils were grouped based on their surface spectra, resulting in spectral curves of 10 representative soils in the studied area. The Morphological Interpretation of Reflectance Spectrum (MIRS) and second derivative of the Kubelka-Munk (KM) function were applied to the soil spectra. The clustered soils were similar, mainly in terms of color, textural class, and organic matter content. Groups based on soil physical and chemical properties and on surface and subsurface colors were similar to those determined by surface reflectance. The identification of soil-landscape relationships was fundamental to understand the genesis and distribution of the soils, which had similar chemical and physical properties to their parent materials. The analysis of clusters based on soil surface reflectance proved efficient in determining groups of soil classes with similar properties. Surface reflectance data were related to the soil surface and subsurface properties determined by traditional soil sample analyses, since the two approaches formed similar groups. The simultaneous interaction of soil properties was assessed by MIRS analysis, while the second derivative of the KM function adequately quantified the mineralogy of the spectra.

Relationships between spectral features, iron oxides and colours of surface soils in northern Jordan

“…Accordingly, all MSRSs represent types of spectral soil curves that are affected by Fe oxides (Bowker, Davis, Myrick, Stacy, & Jones, 1985; Condit, 1970; Escadafal & Huete, 1992; Stoner & Baumgardner, 1981). The effects of Fe oxides on spectra can be illustrated using four morphological distinctions (Demattę, Bellinaso, Romero, & Fongaro, 2014; Poppiel et al, 2018), as in Figure 3: reflectance intensity value (RIV); distance between concave sigmoid and y ‐axis (DCS); steepness of convex sigmoid (SCS); and iron absorption feature (IAF). …”

“…The morphological conditions DCS, SCS and IAF show the following trends: the DCS decreases from red soils to yellow soils, and the steepness (SCS) changes from relatively low (red soils) to high (yellow soils). Taking into consideration the negligible effects of SOC, the low SCS is related to an increased proportion of haematite in comparison to goethite (Demattę et al, 2014; Poppiel et al, 2018). In our study, this finding is considerable because of the relationship between the morphological condition SCS and RI LAB‐BT , which can be used as an indicator of the proportion of haematite/goethite (Barrón & Torrent, 1986).…”

“…Furthermore, spectroscopy offers an alternative method for soil monitoring, surveying and land‐use planning in combination with remote sensing data (Brown, Shepherd, Walsh, Dewayne Mays, & Reinsch, 2006; Chabrillat et al, 2019; Escadafal, Girard, & Courault, 1989; Hill & Schütt, 2000; Irons, Weismiller, & Peterson, 1989). These methods are currently undergoing further development for predictions of soil properties, and improved soil classifications using cluster‐based statistical calculations (Ogen, Zaluda, Francos, Goldshleger, & Ben‐Dor, 2019; Poppiel et al, 2018; Viscarra Rossel, Rizzo, Demattê, & Behrens, 2010). Several studies highlighted the relationships between the shapes and features of spectra and soil properties such as moisture, minerals, organic matter contents, colours, etc.…”

Soil Color and Mineralogy Mapping Using Proximal and Remote Sensing in Midwest Brazil

“…Soils of tropical Midwest Brazil usually present high weathering degree and tend to have relatively homogenous profiles [5,7]. Some studies has been shown that topsoil spectral patterns are related to the subsoil pattern variations and dynamic processes which occur within the soil profile [35][36][37]. In addition, bare topsoil reflectance composites produced from Landsat time series [38][39][40] were considered as reliable proxies of topsoil spatial patterns, which can be integrated with other datasets by machine learning to better capture information from deeper layers of the Earth's crust [30].…”

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

“…A partir dos estudos de (Neto, 2017) Era esperado que o teor de argila fosse determinante nessa diferenciação, decorrente dos processos da evolução da vertente já trabalhado e explicado por outros estudos (Obi Redy et al, 2004;Pissarra et al, 2004;Demattê et al, 2011). Da mesma maneira a cor também era esperada ter uma relação com os compartimentos, sendo totalmente influenciada pela drenagem dos solos que é diferente em cada compartimento (Campos e Demattê, 2004 Assim como em outros estudos, a argila nos horizontes superficiais tem sido utilizada para o mapeamento digital de solos, tendo em vista sua alta correlação com os parâmetros do relevo (Fongaro et al, 2018;Poppiel et al, 2018). Assim, com a identificação da variação do e 11 continuou representada pelo atributo em questão, apresentando baixa variação de solos nas áreas (Tabela 5).…”