Improvement of clay and sand quantification based on a novel approach with a focus on multispectral satellite images

Caio T. Fongaro, José A.M. Demattê*, Rodnei Rizzo, José Lucas Safanelli, Wanderson de Sousa Mendes, André Carnieletto Dotto, Luiz Eduardo Vicente, Marston H.D. Franceschini, Susan L. Ustin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Scopus)


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.

Original languageEnglish
Article number1555
JournalRemote Sensing
Issue number10
Publication statusPublished - 27 Sep 2018


  • Bare soil
  • Digital soil mapping
  • Precision agriculture
  • Reflectance spectroscopy
  • Satellite imagery
  • Soil degradation


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