Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

Abstract

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

Mapping at 30 m Resolution of Soil Attributes at Multiple Depths in Midwest Brazil

“…For our study, SySI represents the soils surface of agriculture areas and other natural surfaces with low vegetation cover and rock outcrops, when the vegetation was absent or almost absent, typical for savanas. The GEOS3 has also been implemented in different regions in Brazil for mapping soil variables [11,12,47]. Similar approaches were developed to produce bare soil composites based on Landsat data and accurately employed for soil mapping and management in Germany [9] and the Swiss Plateau and Europe [8].…”

Geostatistics or machine learning for mapping soil attributes and agricultural practices

“…Since then, these application of random theory functions are extremely used for spatialisation of georeferenced data and as it has been performed for agricultural purposes (Dowd, 1991;Shannon et al, 2018;Wackernagel, 2014). The machine learning approach estimates soil spatial arrangements using ancillary variables such as digital elevation models (DEM) and its covariates, and remote sensing data such as satellite images from Landsat 5 Thematic Mapper (McBratney et al, 2003;Fongaro et al, 2018;Gallo et al, 2018;Castro-Franco et al, 2018).…”

Compartimentação da paisagem via relevo e rede de drenagem e sua relação com atributos e classes de solos

“…A partir dos estudos de (Neto, 2017) Era esperado que o teor de argila fosse determinante nessa diferenciação, decorrente dos processos da evolução da vertente já trabalhado e explicado por outros estudos (Obi Redy et al, 2004;Pissarra et al, 2004;Demattê et al, 2011). Da mesma maneira a cor também era esperada ter uma relação com os compartimentos, sendo totalmente influenciada pela drenagem dos solos que é diferente em cada compartimento (Campos e Demattê, 2004 Assim como em outros estudos, a argila nos horizontes superficiais tem sido utilizada para o mapeamento digital de solos, tendo em vista sua alta correlação com os parâmetros do relevo (Fongaro et al, 2018;Poppiel et al, 2018). Assim, com a identificação da variação do e 11 continuou representada pelo atributo em questão, apresentando baixa variação de solos nas áreas (Tabela 5).…”