Soil Drainage Class Probability Mapping Using a Soil‐Landscape Model

Bell, James; Cunningham, Robert; Havens, Matthew W.

doi:10.2136/sssaj1994.03615995005800020031x

Cited by 82 publications

(27 citation statements)

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“…McKenzie and Ryan, 1999) because the downslope gradient (DG) replaces the local slope gradient (S). Other topographic attributes include the altitude of data points (A); the specific monodirectional catchment area (AMO, Moore and Grayson, 1991); the stream power index (SPI, Bell et al, 1994; SPI D AMU ð S); and the profile, plan and tangential curvatures (Curv pr , Curv pl , Curv ta , Zevenbergen and Thorne, 1987). Variables D, A and E have been estimated from theodolite measurements.…”

Section: Topographic Analysismentioning

confidence: 99%

“…The following attributes are considered: distance to the stream bank (D); soil depth (SD); altitude (A); elevation difference to the stream bank (E); the 'downslope gradient' (DG), which represents the ratio between E and the distance to the stream bank; the revised compound topographic index (CTI); the slope gradient (S), i.e. the specific monodirectional catchment area (AMO); the specific multidirectional catchment area (AMU); the stream power index (SPI) (Bell et al, 1994 Hydrol. Process.…”

Section: Predicting Volumetric Soil Wetness Using Topographical Attrimentioning

confidence: 99%

“…Correlation coefficients between the volumetric soil wetness of the topsoil (0-10 cm) on 11 December 1996 and 9 April 1997 and soil and topographical factors estimated using the 10-m digital elevation model (DEM) of the upper boundary of the saprolite and of the soil surface. Distance to the stream bank (D); soil depth (SD); altitude (A); elevation difference to the stream bank (E); the 'downslope gradient' (DG), which represents the ratio between E and the distance to the stream bank; the revised compound topographic index (CTI); the slope gradient (S), the specific monodirectional catchment area (AMO); the specific multidirectional catchment area (AMU); the stream power index (SPI) (Bell et al, 1994, SPI D 89%, whereas values for Â 30 , Â 60 and Â 110 were between 10 and 90%. No significant differences were observed between topographies.…”

Section: Predicting Volumetric Soil Wetness Using Topographical Attrimentioning

confidence: 99%

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Subsurface topography to enhance the prediction of the spatial distribution of soil wetness

Chaplot

Walter

2003

Hydrological Processes

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Abstract:The estimation of the spatial distribution of soil wetness within a catchment is one of the most important issues in hydrological and erosion modelling. So far, such models have been based on soil surface topographic information only. However, soil hydrology is also controlled by subsurface flow pathways that may not be explained only by surface terrain features. This study examined how the topography of the lower limit of the soil cover could improve quantitative modelling for the spatial prediction of soil wetness. The study was conducted in an agricultural catchment of the Armorican Massif (western France) characterized by impermeable granitic saprolites. Two digital elevation models (DEMs) with a 10-m grid mesh and with a 0Ð3 m vertical resolution were generated from field investigations throughout the catchment. One DEM was a numerical representation of the soil surface and the other described the topography of the boundary between the soil cover and the underlying impermeable saprolite. Soil wetness (Â) was surveyed systematically from 1996 to 1997 along a hillslope. The sampling scheme consisted of 149 nodes of a 10-m grid where Â at 0-10 cm was estimated using time-domain reflectometry. The value of Â at depths of 20-30, 50-60 and 110-120 cm was estimated for a subset of 112 data points using a gravimetric method. For both surface and subsurface DEMs, soil wetness at all depths significantly correlated with the topographic attributes, namely the distance to the stream bank, the elevation above the stream bank (E), the downslope gradient, the revised compound topographic index (CTI), and the specific monodirectional and multidirectional catchment areas. The best correlations were observed between Â 10 of winter 1996 and the physically based attributes E and CTI estimated by using the subsurface DEM (r D 0Ð83 and 0Ð86, respectively). Two multiple non-linear regression models for Â 10 spatial prediction were generated using non-autocorrelated topographic attributes estimated from both surface and subsurface topography. Model validation using a new set of 41 data points showed root mean square errors (RMSE) lower than 10% of the Â 10 range. The model based on subsurface topography decreased RMSE by 43%. Prediction errors were not spatially distributed. Finally, theses results are discussed in respect of processes involved in hillslope hydrology.

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Section: Topographic Analysismentioning

confidence: 99%

Section: Predicting Volumetric Soil Wetness Using Topographical Attrimentioning

confidence: 99%

Section: Predicting Volumetric Soil Wetness Using Topographical Attrimentioning

confidence: 99%

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Subsurface topography to enhance the prediction of the spatial distribution of soil wetness

Chaplot

Walter

2003

Hydrological Processes

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“…For example, Bell et al (1994) used discriminant function analysis to relate soil drainage class to parent material, terrain, and surface drainage in Pennsylvania, USA. Based on this method, soil drainage probability maps were in agreement with published soil drainage maps.…”

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confidence: 99%

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

Zhao¹,

Chow²,

Yang³

et al. 2008

Can. J. Soil. Sci.

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Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps. Can. J. Soil Sci. 88: 787Á799. High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sediment delivery ratio (SDR) and slope steepness as inputs. It was found that 52% of model-predicted drainage classes were exactly the same as field assessment observations and 94% of model-predicted drainage classes were within91 class. In comparison, only 12% of maps indicated drainage classes were the same as field assessment observations based on coarse resolution soil maps and only 55% of points were within 91 class of field assessed drainage classes. Results indicated that the model could be used to produce high-resolution soil drainage maps at relatively low cost.Key words: Soil drainage, artificial neural network model, ANN model, high-resolution soil maps, DEM, hydrology model Zhao, Z., Chow, T. L., Yang, Q., Rees, H. W., Benoy, B., Xing, Z. et Meng, F. R. 2008. Mode`le pre´visionnel des classes de drainage du sol d'apre`s les parame`tres du MAN et les attributs des cartes pe´dologiques a`faible re´solution. Can. J. Soil Sci. 88: 787Á799. Les cartes de drainage du sol a`haute re´solution reveˆtent une grande importance pour la planification des cultures, la gestion des foreˆts et l'e´valuation de l'environnement. Les cartes de classification des sols actuelles ont tendance a`ne fournir de renseignements que sur les principales conditions de drainage et ne conviennent pas a`la planification de la foresterie et de l'agriculture de pre´cision. L'e´tude devait cre´er un mode`le a`re´seau neuronal artificiel (RNA) permettant une classification a`haute re´solution du drainage du sol. Les auteurs ont examine´si on pouvait utiliser a`cette fin les donne´es sur le profil du sol tire´es des cartes a`faible re´solution (e´chelle 1:1 000 000) ainsi que les variables topographiques et hydrologiques du mode`le altime´trique nume´rique (MAN) (e´chelle 1:35 000). Ils ont ensuite utilise´la carte a`haute re´solution de drainage du sol (1:10 000) du bassin...

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“…Up-to-date information is needed to guide watershed and site-specific crop management but conventional soil survey procedures are time-consuming and expensive. New technologies, such as remote and proximal sensing, feature in many key soil science applications and are particularly effective for mapping soil drainage (Niang et al, 2007 andBell et al, 1994 andLee et al, 1988;Levine et al, 1994;Cialella et al, 1997;Campling et al, 2002;Liu et al, 2008). Since soil drainage is often related to other properties, such as soil water content and texture (Kravchenko et al, 2002), it can be mapped using both optical and radar remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Potential of C-Band Multi-polarized and Polarimetric SAR Data for Soil Drainage Classification and Mapping

Niang¹,

Nolin²,

Bernier³

2010

Geoscience and Remote Sensing New Achievements

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Soil Drainage Class Probability Mapping Using a Soil‐Landscape Model

Cited by 82 publications

References 0 publications

Subsurface topography to enhance the prediction of the spatial distribution of soil wetness

Subsurface topography to enhance the prediction of the spatial distribution of soil wetness

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

Potential of C-Band Multi-polarized and Polarimetric SAR Data for Soil Drainage Classification and Mapping

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