The effectiveness of digital soil mapping to predict soil properties over low-relief areas

Abstract

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.

Patterns and Predictors of Recent Forest Conversion in New England

“…BRTs generally avoid overfitting [35], can be applied to a variety of spatial analyses, such as the distribution of species and vegetation types [36][37][38][39][40][41][42], hydrology [43,44], soil and landform properties [45][46][47] and natural disturbance [48], as well as quantification of land cover and land use change through human activities [34,49,50]. Across a wide variety of contexts, model comparisons have shown BRTs to perform much better than traditional models and comparably well to other machine-learning models [36,37,43,45,48,51], with some variability in comparative performance with other machine-learning methods depending on context [33,38,44,47].…”

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

“…From this DEM, 9 terrain variables commonly used for predictions and mapping of soil classes and properties [10,[50][51][52][53][54][55] were selected using both ArcGIS 10.1 and SAGA GIS [56], including: slope, topographic wetness index (TWI), SAGA wetness index (SWI), cross-sectional and longitudinal curvatures, vertical distance to channel network and valley depth, in addition to elevation and Geomorphons [57]. Geomorphons consist of an algorithm that classifies the landscape into 10 possible landforms, and thus, it is expected to contribute to distinguishing geomorphology patterns that may be related to varying soil classes and properties.…”

Selecting appropriate machine learning methods for digital soil mapping

“…Even though sample size is not the only factor affecting R 2 when comparing different studies, a notable pattern can be observed. For example, comparing two studies predicting calcium carbonate equivalent (CCE) using MLR, the R 2 of independent validation improved from 0.06 with 120 observations [55] to 0.51 with 137 observation [56]. In those same studies, the R 2 for independent validation increased from 0.05 to 0.40 for predicting sand content.…”

“…The same pattern is seen for other soil properties. Mosleh et al [55] predicted soil organic carbon (SOC) with an R 2 of 0.26 for independent validation. Bonfatti et al [26] predicted SOC with 43 more samples than Mosleh et al.…”