Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain

Autor: Shraban Sarkar, Tapas R. Martha, Archana K. Roy
Rok vydání: 2013
Předmět:
Zdroj: International Journal of Geographical Information Science. 27:2436-2454
ISSN: 1362-3087
1365-8816
DOI: 10.1080/13658816.2013.814780
Popis: 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.
Databáze: OpenAIRE
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