The geochemistry of soils on a catena on basalt at Khon Buri, northeast Thailand

Thanachit, Suphicha; Suddhiprakarn, A.; Kheoruenromne, Irb; Gilkes, Robert

doi:10.1016/j.geoderma.2005.10.010

Cited by 45 publications

(22 citation statements)

References 43 publications

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“…A close relationship of soil attributes maps with elevation was observed ( Table 5). This is consistent with several studies where soils derived from basalt, where the spatial variability of their attributes is partly controlled by topographic position, especially in the tropics [62]. …”

Section: Spatial Modeling Of Soil Attributes In Undersurface Layersupporting

confidence: 93%

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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Section: Spatial Modeling Of Soil Attributes In Undersurface Layersupporting

confidence: 93%

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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“…The higher feldspar peaks in the X-ray results in the rainfed paddies can be linked to the higher potassium concentrations found in these paddies, suggesting that these were most likely potassium feldspars, which points to recent sediment deposition (Thanachit et al, 2006;Prakongkep et al, 2008).…”

Section: Sediment Induced Spatial Variability In Soil Fertility Depenmentioning

confidence: 95%

Sediment induced soil spatial variation in paddy fields of Northwest Vietnam

et al. 2010

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“…Soil geochemistry is important because it offers a means by which the natural (lithogenic and pedogenic) levels of heavy metals in soils could be defined and distinguished from those of anthropogenic sources, which is an essential first step in the analysis of soil pollution (Kabata-Pendias, 2000;Thanachit et al, 2006). Furthermore, geochemical background of heavy metals is necessary for the determination of pollution degree of upper horizons in soil profiles used for plant cultivation.…”

Section: Introductionmentioning

confidence: 99%

Distribution and potential source evaluation of heavy metals in prominent soils of Accra Plains, Ghana

2010

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The geochemistry of soils on a catena on basalt at Khon Buri, northeast Thailand

Cited by 45 publications

References 43 publications

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

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

Sediment induced soil spatial variation in paddy fields of Northwest Vietnam

Distribution and potential source evaluation of heavy metals in prominent soils of Accra Plains, Ghana

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