This document discusses using artificial neural networks (ANNs) to help complete digital soil maps of Portugal at a scale of 1:100,000. Currently only about 45% of Portugal has been mapped at this scale, as soil surveys are expensive and time-consuming. The study investigates using two types of ANNs - GeoSom and Multi-layer Perceptron - along with five different sampling strategies to predict soil types in two catchments in Northern Portugal. Landscape variables related to soil formation, like those derived from digital elevation models, land use, and lithology, are combined with existing soil map data to train the ANNs. Results show that the sampling strategy has a greater impact on the accuracy of predictions than the specific

1. Artificial Intelligence – Bridging the Gaps in Soil Mapping
Fonseca, I.L.1
, Freire, S.2
, Brasil, R. 1
, Rocha, J. 1
&Tenedório, J.A.2
1
Centro de Estudos Geográficos, IGOT Universidade de Lisboa, Edf. Fac. Letras, ,
Alameda da Universidade, 1600-214 Lisboa, Portugal. i.fonseca@campus.ul.pt
2
Centro de Estudos de Geografia e Planeamento Regional, Faculdade de Ciências
Sociais e Humanas da Universidade Nova de Lisboa, Av. de Berna 26-C, 1069-061
Lisboa, Portugal.
Scientific Area: 1. Landscape and Land Management
Keywords: Soil Digital Maps, Artificial Neural Networks, Geo-Self-Organizing Map,
Multi-layer Perceptron Model, Sampling, Spatial Autocorrelation.
There is a growing need for digital soil maps at scales suitable for land management
and regional planning. Soils modulate hydrological fluxes, regulate ecosystems and
play a very important role in mediating the impact of climate change. However, most
European countries still lack complete soil map coverage at medium to large scales
because soil surveys are very expensive and time consuming. Artificial Neural
Networks (ANNs) have started to be applied as a means of fast, cheaply and
accurately predicting soils by learning rules that can be extended to unmapped areas.
In this study two different types of ANNs (GeoSom and Multi-layer Perceptron) and five
sampling strategies are investigated in order to predict soil types across two
catchments in Northern Portugal and implement, at a later stage, the best model to
complete the soil map coverage of Portugal at 1:100000, which currently stands at c.
45% of the country. Landscape variables that are also factors of soil formation, such as
parameters derived from DEMs (altitude, slope, profile and plan curvatures, upslope
area, dispersal area, wetness index and potential solar radiation), land use and
lithology were combined with existing soil map data to train the ANNs. Results indicate
that sampling has a higher impact on model accuracy and performance than the
parameterisation of the models themselves because the landscape variables most
commonly used to predict soils are spatially autocorrelated. Thus, ANNs learn and
converge faster to a better solution if the sampling strategy takes into account that
close neighbours are more likely to have similar soil types.