Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley
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 environment...
| Autores principales: | , , , , , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.12955/2537 http://dx.doi.org/10.2139/ssrn.4777607 |
| _version_ | 1855490592140689408 |
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| author | Pizarro Carcausto, Samuel Vera Vilchez, Jesús Emilio Huamani, Joseph Cruz, Juancarlos Lastra Paucar, Sphyros Roomel Solórzano Acosta, Richard Andi Verástegui Martínez, Patricia |
| author_browse | Cruz, Juancarlos Huamani, Joseph Lastra Paucar, Sphyros Roomel Pizarro Carcausto, Samuel Solórzano Acosta, Richard Andi Vera Vilchez, Jesús Emilio Verástegui Martínez, Patricia |
| author_facet | Pizarro Carcausto, Samuel Vera Vilchez, Jesús Emilio Huamani, Joseph Cruz, Juancarlos Lastra Paucar, Sphyros Roomel Solórzano Acosta, Richard Andi Verástegui Martínez, Patricia |
| author_sort | Pizarro Carcausto, Samuel |
| collection | Repositorio INIA |
| description | 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. |
| format | Artículo preliminar |
| id | INIA2537 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INIA25372025-09-18T20:33:14Z Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley Pizarro Carcausto, Samuel Vera Vilchez, Jesús Emilio Huamani, Joseph Cruz, Juancarlos Lastra Paucar, Sphyros Roomel Solórzano Acosta, Richard Andi Verástegui Martínez, Patricia 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 Machine learning 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. 2024-07-12T04:47:13Z 2024-07-12T04:47:13Z 2024-03-29 info:eu-repo/semantics/workingPaper Pizarro-Carcausto, S.; Vera-Vilchez, J.; Huamani, J.; Cruz, J.; Lastra, S.; Solórzano-Acosta, R.; Verástegui-Martínez, P. (2024). Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley. SSRN. doi: 10.2139/ssrn.4777607 1556-5068 https://hdl.handle.net/20.500.12955/2537 http://dx.doi.org/10.2139/ssrn.4777607 eng urn:issn: 1556-5068 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf application/pdf Elsevier US Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | 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 Machine learning Aprendizaje automático Pizarro Carcausto, Samuel Vera Vilchez, Jesús Emilio Huamani, Joseph Cruz, Juancarlos Lastra Paucar, Sphyros Roomel Solórzano Acosta, Richard Andi Verástegui Martínez, Patricia Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title | Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title_full | Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title_fullStr | Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title_full_unstemmed | Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title_short | Digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning, implemented in GEE, for the Peruvian Mantaro Valley |
| title_sort | digital soil mapping of metals and metalloids in croplands using multiple geospatial data and machine learning implemented in gee for the peruvian mantaro valley |
| topic | 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 Machine learning Aprendizaje automático |
| url | https://hdl.handle.net/20.500.12955/2537 http://dx.doi.org/10.2139/ssrn.4777607 |
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