Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley

• Autores: Pizarro Carcausto, Samuel, Vera Vilchez, Jesús Emilio, Huamani, Joseph, Cruz, Juancarlos, Lastra, Sphyros, Solórzano Acosta, Richard, Verástegui Martínez, Patricia
• Formato: documento de trabajo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/2537
• Enlace del recurso: https://hdl.handle.net/20.500.12955/2537; http://dx.doi.org/10.2139/ssrn.4777607
• Nivel de acceso: acceso abierto
• Materia: Random Forest; Soil mapping; Google Earth Engine; Machine learning; Cloud computing; https://purl.org/pe-repo/ocde/ford#4.01.04; Algorithms; Algoritmo; Soil surveys; Reconocimiento de suelos; Spatial data; Datos espaciales; Aprendizaje automático

Quality and safety of the soil are essential to ensure social and economic development and provides the supply of contaminant free food. With agriculture intensification, expansion of urban zones, construction of roads, and mining, some agricultural soils sites become polluted increasing environmental risks to ecosystems functions and human health. Hence the need know the spatial distribution of elements in soils, we mapped 25 elements, namely Ca, Mg, Sr, Ba, Be, K, Na, As, Sb, Se, Tl, Cd, Zn, Al, Pb, Hg, Cr, Ni, Cu, Mo, Ag, Fe, Co, Mn and V, using various geospatial datasets, such as remote sensing, climate, topography, soil data, and distance, to establish the spatial estimation models of spatial distribution trained trough machine learning model with a supervised dataset of 109 topsoil samples, into Google earth engine platform. Using R2, RMSE and MAE to assess the prediction accuracy. First Random Forest gave satisfactory results in predicting the distribution of analyzed elements in soil, being improved for some elements when adds more trees. Additionally, each element analyzed has a different combination of environmental covariates as predictor, mainly soil, climate, topographic and distance variables especially croplands close to rivers, with less importance for spectral variables. Our results suggest that is possible to identify polluted soils and improved regulations to minimize harm to environmental health and human health, for short-to-medium-term environmental risk control.