Evaluation of Synthetic-Temporal Imagery as an Environmental Covariate for Digital Soil Mapping: A Case Study in Soils under Tropical Pastures

Fabio Arnaldo Pomar Avalos (), Michele Duarte de Menezes, Fausto Weimar Acerbi Júnior, Nilton Curi, Junior Cesar Avanzi and Marx Leandro Naves Silva ()
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Fabio Arnaldo Pomar Avalos: Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura, Universidade Estadual de Campinas, Campinas 13083-886, SP, Brazil
Michele Duarte de Menezes: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil
Fausto Weimar Acerbi Júnior: Departamento de Ciências Florestais, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil
Nilton Curi: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil
Junior Cesar Avanzi: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil
Marx Leandro Naves Silva: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil

Resources, 2024, vol. 13, issue 2, 1-14

Abstract: Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM.

Keywords: vegetation greenness; NDVI; random forest; land surface phenology (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2024
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