Mapping soil thickness using a mechanistic model and machine learning approaches

Author(s):Show less -

Rosin, Nicolas Augusto ; de Mello, Danilo Cesar ; Bonfatti, Benito R. ; Hartemink, Alfred E. ; Ferreira, Tiago O. ; Silvero, Nelida E. Q. ; Poppiel, Raul Roberto ; Mendes, Wanderson de S. ; Veloso, Gustavo Vieira ; Francelino, Marcio Rocha ; Alves, Marcelo Rodrigo ; Falcioni, Renan ; Dematte, Jose A. M.

• Document type: Journal article
• Source: CATENA; v. 249, p. 17-pg., 2024-12-10.

Abstract

Soil thickness is an important property as it influences the landscape dynamics, partakes part in hydrologic and geomorphologic processes, and controls water saturation and soil moisture, which are directly related to agricultural production. However, soil thickness data are difficult to obtain in situ, especially in areas with deep soils (>2 m). In this study, we developed and compared three models to predict soil thickness. First, we developed a mechanistic model which uses physical equations from a landscape evolution model applied to a digital elevation model (DEM) (30 m spatial resolution). We evaluated the inclusion of parameters derived from the soil parent material, including erosion and sediment deposition. Second, we developed an empirical model using terrain derivatives obtained from a 30-m DEM, using a Random Forest algorithm. This model was calibrated using 1,362 soil thickness data collected in field as right censored data. We implemented a hybrid model using the residual from the mechanistic model as a dependent variable in the empirical model. The models were validated with 214 soil observation points collected in field as right censored data and 12 data points with real data. The result was added back to predictions of the mechanistic model. For all models, we verified coherence with a soil map at 1:100,000. The models were also evaluated considering changes in the spatial resolution. The mechanistic model was improved when parent material parameters were added. The mechanistic models performed better in areas with shallow soils (<1 m), whereas the empirical model was better in predicting deeper soils and was more coherent with a soil class map. Model performance could be further improved when updating DEM data to a 5-m resolution. As expected, the hybrid model could combine the model performances and improve the predictions. However, the predictions remained poor for shallow soils. (AU)

• FAPESP's process: 14/22262-0 - Geotechnologies on a detailed digital soil mapping and the Brazilian soil spectral library: development and applications
• Grantee: José Alexandre Melo Demattê
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 21/10063-6 - 2.5D SPATIO-TEMPORAL MAPPING OF SOIL ORGANIC CARBON STOCK IN BRAZIL
• Grantee: Nícolas Augusto Rosin
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 21/05129-8 - The Brazilian soil quality determined by geotechnologies: mapping, interpretation and agricultural/environmental applications: a legacy for society
• Grantee: José Alexandre Melo Demattê
• Support Opportunities: Research Projects - Thematic Grants