Soil variability and quantification based on Sentinel-2 and Landsat-8 bare soil images: A comparison

• Published in: Remote sensing of environment Vol. 252; p. 112117
• Main Authors: Silvero, Nélida Elizabet Quiñonez, Demattê, José Alexandre Melo, Amorim, Merilyn Taynara Accorsi, Santos, Natasha Valadares dos, Rizzo, Rodnei, Safanelli, José Lucas, Poppiel, Raul Roberto, Mendes, Wanderson de Sousa, Bonfatti, Benito Roberto
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.01.2021; Elsevier BV
• Subjects: Agriculture; Algorithms; Bare soil pixels; Brazil; clay; Digital soil mapping; environment; Image acquisition; Landsat; Landsat satellites; Machine learning; Mapping; Organic matter; Pixels; Precision agriculture; prediction; Remote sensing; sand; Satellite imagery; Satellites; silt; soil color; soil heterogeneity; Soil mapping; Soil maps; Soil organic matter; Soil properties; Soil surveys; Spatial data; Spatial resolution; Spectra; Spectral resolution; Time series; Topsoil; Tropical environments; Brazil; Bare soil pixels; Digital soil mapping; Spectral resolution; Machine learning; Time series; Spatial resolution; Remote sensing
• DOI: 10.1016/j.rse.2020.112117
• ISSN: 0034-4257

There is a worldwide need for detailed spatial information to support soil mapping, mainly in the tropics, where main agricultural areas are concentrated. In this line, satellite images are useful tools that can assist in obtaining soil information from a synoptic point of view. This study aimed at evaluating how satellite images at different resolutions (spatial, spectral and temporal) can influence the representation of soil variability over time, the percentage of bare soil areas and spatial predictions of soil properties in southeastern Brazil. We used single-date and multi-temporal images (SYSI, Synthetic Soil Images) of bare soil pixels from the Sentinel2-MultiSpectral Instrument (S2-MSI) and the Landsat-8 Operational Land Imager (L8-OLI) to conduct this research. Two SYSIs were obtained from images acquired in four years (2016–2019) for each satellite (SYSI S2-MSI and SYSI L8-OLI) and a third SYSI, named SYSI Combined, was obtained by combining the images from both satellites. The single-date images for each satellite was acquired in September, when the influence of clouds was low and bare soil pixels was predominant. Single-date images and SYSIs were compared by means of their spectral patterns and ability to predict topsoil properties (clay, sand, silt, and organic matter contents and soil color) using the Cubist algorithm. We found that the SYSIs outperformed single-date images and that the SYSI Combined and SYSI L8-OLI provided the best prediction performances. The SYSIs also had the highest percentage of areas with bare soil pixels (~30–50%) when compared to the single-date images (~20%). Our results suggest that bare soil images obtained by combining Landsat-8 and Sentinel-2 images are more important for soil mapping than spatial or spectral resolutions. Soil maps obtained via satellite images are important tools for soil survey, land planning, management and precision agriculture. •Bare soil images proved useful for mapping topsoil properties.•SYSI images had more than twice the bare soil areas of single-date images.•SYSI Combined and SYSI L8-OLI had the best performances in predicting topsoil properties.•The use of RedEdge bands from Sentinel-2 did not provide large model performance gains.