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040			_aMSU _cMSU _erda
100			_aSarkar, Shraban _eathour
245			_a Altmetric Original Articles Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain _ccreated Shraban Sarkar , Archana K. Roy & Tapas R. Martha
264			_aIndia: _bTaylor and Fancis, _c2013.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aSoil 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.
650			_a Darjeeling Himalayas _v
650			_adigital evaluationmodel
650			_agression kriging
856			_u DOI:10.1080/13658816.2013.814780Corpus ID: 493068
942			_2lcc _cJA
999			_c160728 _d160728