F. A. Avalos, M. D. de Menezes, Fausto Weimar Acerbi Júnior, N. Curi, J. C. Avanzi, M. L. Silva
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引用次数: 0
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.
期刊介绍:
ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric.
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