Altmetric Original Articles Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain
Sarkar, Shraban
Altmetric Original Articles Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain created Shraban Sarkar , Archana K. Roy & Tapas R. Martha - Volume , number , .
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.
Keywords:
Darjeeling Himalayasdigital elevation modelregression krigingsoil depth
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Acknowledgements
The first author is thankful to University Grants Commission (UGC), New Delhi, India for providing the fellowship to carry out the research work. He is also thankful to Dr. Edwin and his family for their support during field work.
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Soil-landscape modelling and spatial prediction of soil attributes
P. E. GESSLER et al.
International journal of geographical information systems
Published online: 5 Feb 2007
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.
Darjeeling Himalayas--
digital evaluationmodel
gression kriging
Altmetric Original Articles Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain created Shraban Sarkar , Archana K. Roy & Tapas R. Martha - Volume , number , .
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.
Keywords:
Darjeeling Himalayasdigital elevation modelregression krigingsoil depth
Previous article
View issue table of contents
Next article
Acknowledgements
The first author is thankful to University Grants Commission (UGC), New Delhi, India for providing the fellowship to carry out the research work. He is also thankful to Dr. Edwin and his family for their support during field work.
More Share Options
Related research
People also read
Recommended articles
Cited by
20
Soil-landscape modelling and spatial prediction of soil attributes
P. E. GESSLER et al.
International journal of geographical information systems
Published online: 5 Feb 2007
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (–0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model.
Darjeeling Himalayas--
digital evaluationmodel
gression kriging