Soil pedogeochemical attributes prediction by interpolators in ice-free areas of Antarctica

• Published in: Research, Society and Development
• Main Authors: Schünemann, Adriano Luis, Thomazini, André, Almeida, Pedro Henrique Araújo, Francelino, Márcio Rocha, Fernandes Filho, Elpídio Inácio, Santos, Gérson Rodrigues dos, Paula, Mayara Daher de, Schaefer, Carlos Ernesto Gonçalves Reynaud, Pereira, Antonio Batista
• Format: Article in Journal/Newspaper
• Language: English
• Published: Research, Society and Development 2022
• Subjects: Predictive covariates; Interpolation; Digital mapping; Covariables predictivas; Interpolación; Mapeo digital; Covariáveis preditivas; Interpolação; Mapeamento digital; Antarctic; Hielo; Isla Rey Jorge; Keller; Keller peninsula; King George Island; península Keller; Rey Jorge; Antarc*; Antarctica; Antártica; Antártida
• Online Access: https://rsdjournal.org/index.php/rsd/article/view/27542; https://doi.org/10.33448/rsd-v11i4.27542

The main objective of this paper is to predict soil attributes in unsampled areas using geostatistical models, By improving the prediction parameters of selected data, using environmental covariates characteristic of Antarctic ice free areas. In this study, 58 soil samples from a grid were collected at 0-10 cm depth in Keller Peninsula, King George Island, Antarctica. The soil chemical analysis was performed, and the values of potassium, calcium and magnesium were determined for each soil sampled. Simple kriging (SK) and Random Forest interpolator were used in this work to predict the values of the studied soil attributes in non-sampled areas. We used a Terrestrial Laser Scanner (TLS) to generate a cloud of points, to obtain digital elevation models (DEMs) of 1, 5, 10, 20 and 30 meters cell size. The use of covariates did not improve the parameters of soil bases prediction in the studied area. The final maps did not show great differences based on RMSEs, mainly related to the great spatial variability of soil attributes in this region, despite soil thematic maps evidencing visual difference. El objetivo principal de este trabajo es predecir los atributos del suelo en áreas no muestreadas utilizando modelos geoestadísticos, mejorando los parámetros de predicción de los dados seleccionados, utilizando covariables ambientales características de las áreas de hielo antárticas. En este estudio, se recolectaron 58 muestras de suelo de una cuadrícula a una profundidad de 0 a 10 cm en la Península Keller, Isla Rey Jorge, Antártida. Se realizó el análisis químico del suelo y se determinaron los valores de potasio, calcio y magnesio para cada suelo muestreado. En este trabajo se utilizaron interpoladores de kriging simple (SK) y Random Forest para predecir los valores de los atributos del suelo estudiados en áreas no muestreadas. Utilizamos un escáner láser terrestre (TLS) para generar una nube de puntos, para obtener modelos digitales de elevación (DEM) de tamaño de celda de 1, 5, 10, 20 y 30 metros. El uso de ...