Abstract
This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). A soil survey was carried out and 74 soil profiles were analyzed. The tested models were: Multinomial logistic regression (MLR), C5 decision tree (C5-DT) and Random forest (RF). In order to reduce the effects of an imbalanced dataset on the accuracy of the tested models, additional sampling retrieved by photointerpretation was necessary. Accuracy assessment was based on aggregated data from a proportional 5-fold cross-validation procedure. Extrapolation assessment was based on the multivariate environmental similarity surface (MESS). The RF model including additional sampling (RF*) showed the best performance among the tested models (overall accuracy = 49%, kappa index = 0.33). The RF* allowed to link soil mapping units (SMU) and, in the case of less-common soil classes in the watershed, to set specific conditions of occurrence on the space of terrain-attributes. MESS analysis showed reliable outputs for 82.5% of the watershed. SMU distribution across the watershed was: Typic Rhodudult (56%), Typic Hapludult* (13%), Typic Dystrudept (10%), Typic Endoaquent + Fluventic Dystrudept (10%), Typic Hapludult (9.5%) and Rhodic Hapludox + Typic Hapludox (2%).
Highlights
This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha)
Additional point sampling by photointerpretation enabled to capture specific conditions of occurrence of less-common soils and, in consequence, it was showed a substantial improvement in classification accuracy of the minority soil classes
A comparison among different models and the effects of additional point sampling for digital mapping of less-common soil classes, based on the spatial association with soilscape covariates, was performed
Summary
This study aimed to evaluate the performance of three spatial association models used in digital soil mapping and the effects of additional point sampling in a steep-slope watershed (1,200 ha). Pedometric and knowledge-driven approaches differ in philosophy and technical emphasis[7], they are not mutually exclusive[20] In this sense, pedologists’ knowledge about the study area (mental model) could be incorporated into the digital mapping process, by identifying characteristic sites in aerial imagery associated to soil profiles previously surveyed[21], which might be useful to account for specific conditions of less-common soil classes. Pedologists’ knowledge about the study area (mental model) could be incorporated into the digital mapping process, by identifying characteristic sites in aerial imagery associated to soil profiles previously surveyed[21], which might be useful to account for specific conditions of less-common soil classes In this way, a qualitative soil-landscape model would be translated into quantitative predictions supported by the spatial association between soil classes occurrence and environmental covariates[22]
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