Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface?

• Published in: Revista Brasileira de Ciência do Solo 2016, Vol.40
• Main Authors: Demattê, José Alexandre Melo, Alves, Marcelo Rodrigo, Terra, Fabricio da Silva, Bosquilia, Raoni Wainer Duarte, Fongaro, Caio Troula, Barros, Pedro Paulo da Silva
• Format: Article
• Language: English
• Subjects: bare soils; digital soil mapping; multi-temporal images; satellite images; soil remote sensing; SOIL SCIENCE; spectral sensing
• ISSN: 0100-0683; 1806-9657; 0100-0683
• DOI: 10.1590/18069657rbcs20150335

ABSTRACT It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.