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Abstract:The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400-2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R 2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

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.

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.