A regional scale soil mapping approach using integrated AVHRR and DEM data

Dobos, Endre; Montanarella, Luca; Thierry, Negre; Michéli, Erika

doi:10.1016/s0303-2434(01)85019-4

Cited by 43 publications

(20 citation statements)

References 23 publications

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“…Dobos et al [12] estimated a taxonomic purity of 49.1% for the soil and terrain information in Hungary. The database was created using the traditional, expert-knowledge-based method [13].…”

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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“…Dobos et al [12] estimated a taxonomic purity of 49.1% for the soil and terrain information in Hungary. The database was created using the traditional, expert-knowledge-based method [13].…”

Section: Introductionmentioning

confidence: 99%

“…Relief parameters were also used for pedological mapping as indicated by [28]. In fact, prior to the use of hyperspectral imaging, there have been a number of studies using multispectral images either by visual interpretation or by digital processing throughout 1970-2000s [13,[29][30][31].…”

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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“…The recently adapted hierarchical approach to defi ne soilscapes follows the World Soils and Terrain Digital Database (SOTER) methodology (ISRIC, 1993). SOTER has become widely evaluated in European and broader context (Dobos, E. et al 2001(Dobos, E. et al , 2005(Dobos, E. et al , 2010. However, these terrain-based approaches are more appropriate for fi ner scales as they mainly focus on deriving terrain facets instead of deriving larger homogeneous geomorphological or pedological regions (Schmidt, K. et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Method development to extract spatial association structure from soil polygon maps

et al. 2015

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“…The precision of the generated maps ranges from 50 to 88 % (Lagacherie & Holmes, 1997;Dobos et al, 2001;Moran & Bui, 2002;Hengl & Rossiter, 2003;Peng et al, 2003;Qi & Zhu, 2003;Schmidt & Hewitt, 2004;Scull et al, 2005;Giasson et al, 2006;Smith et al, 2006;Qi et al, 2006;Ziadat, 2007;Figueiredo et al, 2008;Schmidt et al, 2008;Hansen et al, 2009;Ballabio, 2010;Behrens et al, 2010). These maps produced by DSM are based on scientific data by the extrapolation of soil properties, but do not take local soil knowledge into consideration.…”

Section: Introductionmentioning

confidence: 99%

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

Cruz-Cárdenas

Solorio

Trejo

et al. 2011

Rev. Bras. Ciênc. Solo

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SUMMARYMap units directly related to properties of soil-landscape are generated by local soil classes. Therefore to take into consideration the knowledge of farmers is essential to automate the procedure. The aim of this study was to map local soil classes by computer-assisted cartography (CAC), using several combinations of topographic properties produced by GIS (digital elevation model, aspect, slope, and profile curvature). A decision tree was used to find the number of topographic properties required for digital cartography of the local soil classes. The maps produced were evaluated based on the attributes of map quality defined as precision and accuracy of the CAC-based maps. The evaluation was carried out in Central Mexico using three maps of local soil classes with contrasting landscape and climatic conditions (desert, temperate, and tropical). In the three areas the precision (56 %) of the CAC maps based on elevation as topographical feature was higher than when based on slope, aspect and profile curvature. The accuracy of the maps (boundary locations) was however low (33 %), in other words, further research is required to improve this indicator.Index terms: decision tree, digital elevation model, map quality.(1) Received for publication in March 2010 and approved in January 2011.

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A regional scale soil mapping approach using integrated AVHRR and DEM data

Cited by 43 publications

References 23 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

Method development to extract spatial association structure from soil polygon maps

Computer-assisted cartography using topographic properties: precision and accuracy of local soil maps in central Mexico

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