Mapping soil thickness using a mechanistic model and machine learning approaches

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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.

• Tipo de documento: Artigo Científico
• Fonte: CATENA; v. 249, p. 17-pg., 2024-12-10.

Resumo

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)

• Processo FAPESP: 14/22262-0 - Geotecnologias no mapeamento digital pedológico detalhado e biblioteca espectral de solos do Brasil: desenvolvimento e aplicações
• Beneficiário: José Alexandre Melo Demattê
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 21/10063-6 - Mapeamento espaço-temporal 2.5d do estoque de carbono orgânico dos solos do brasil
• Beneficiário: Nícolas Augusto Rosin
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 21/05129-8 - Qualidade dos solos do Brasil via geotecnologias: mapeamento, interpretação e aplicações agrícolas/ambientais: um legado para a sociedade
• Beneficiário: José Alexandre Melo Demattê
• Modalidade de apoio: Auxílio à Pesquisa - Temático