Abstract
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.
Highlights
The characteristics of the topsoil layer are important because they provide fundamental information for food production
When these minerals are absent in the soil, higher quartz content causes a strong increase in reflectance from B3 to B4, with a peak at B5
The spectral signature obtained from the Synthetic Soil Image (SYSI) presented very similar patterns to the equivalent spectral signature of the laboratory data
Summary
The characteristics of the topsoil layer are important because they provide fundamental information for food production. An important soil property related to physical structure is the particle size distribution, which is divided into three main fractions: clay (0.02 mm). Particle size is directly related to soil compaction, plasticity, consistency, mechanical resistance, air capacity, pollutants, and herbicide interactions [2]. Several other studies have confirmed the potential of PS levels for quantifying soil attributes, such as clay content [4,5] and textural classes [6]. Based on this strong spectroscopy background, research has moved towards remote satellite information, as in Coleman et al [7]. Mouazen et al [8] and Morellos et al [9], in order to offer operational solutions for systematic coverage of fields, applied on-line sensors at accuracies comparable to laboratory analyses
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