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

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...