Soil depth modelling using terrain analysis and satellite imagery: the case study of Qeshlaq mountainous watershed (Kurdistan, Iran)

Abstract

Soil depth is a major soil characteristic, which is commonly used in distributed hydrological modelling in order to present watershed subsurface attributes. This study aims at developing a statistical model for predicting the spatial pattern of soil depth over the mountainous watershed from environmental variables derived from a digital elevation model (DEM) and remote sensing data. Among the explanatory variables used in the models, seven are derived from a 10 m resolution DEM, namely specific catchment area, wetness index, aspect, slope, plan curvature, elevation and sediment transport index. Three variables landuse, NDVI and pca1 are derived from Landsat8 imagery, and are used for predicting soil depth by the models. Soil attributes, soil moisture, topographic curvature, training samples for each landuse and major vegetation types are considered at 429 profiles within four subwatersheds. Random forests (RF), support vector machine (SVM) and artificial neural network (ANN) are used to predict soil depth using the explanatory variables. The models are run using 336 data points in the calibration dataset with all 31 explanatory variables, and soil depth as the response of the models. Mean decrease permutation accuracy is performed on Variable selection. Testing dataset is done with the model soil depth values at testing locations (93 points) using different efficiency criteria. Prediction error is computed for both the calibration and testing datasets. Results show that the variables landuse, specific surface area, slope, pca1, NDVI and aspect are the most important explanatory variables in predicting soil depth. RF and SVM models are appropriate for the mountainous watershed areas that have been limited in the depth of the soil and ANN model is more suitable for watershed with the fields of agricultural and deep soil depth.

Greening and Browning of Urban Lawns in Geneva (Switzerland) as Influenced by Soil Properties

“…In urban contexts, NDVI has been used to monitor vegetation and identify stress factors (Nouri et al, 2014;Wang et al, 2018;Cârlan et al, 2020). Also in soil science, EO information is increasingly used for various purposes, such as estimating soil depth, soil moisture, and the depth of the ground water table (Tesfa et al, 2009;Zahedi et al, 2017;Walker et al, 2017;Peng et al, 2017). Recent improvements in spatial and temporal resolution of EO data make their application also interesting for small-scaled urban settings and enable the observation of natural processes such as the greening and browning of vegetation within single vegetation periods.…”