André Carnieletto Dotto

Estimating Soil Texture from a Limited Region of the Visible/Near-Infrared Spectrum

Soil particle size is an attribute of fundamental importance when defining soil horizons. Proximal soil sensors can facilitate the acquisition of a larger amount of soil data using a faster and less laborious technique. Thus, the objective of this study is to evaluate the capacity of a limited spectral acquisition region (325–1075 nm) for estimating soil texture. Soil samples were collected in the southwest part of Marombas river watershed located near the center of Santa Catarina State, south of Brazil. A total of 42 soil profiles were sampled according to the GlobalSoilMap specification. A dataset of 166 samples was used for model calibration and another set of 71 samples was used for model validation. Diffuse reflectance spectroscopy of sieved samples (2 mm) was collected with a spectrometer FieldSpecHandHeld II (ASD Inc.). Savitzky–Golay second derivatives were calculated and used in partial least-squares regression modeling. Calibration and validation datasets showed statistically similar mean and variance. The root-mean-square error of prediction for sand, silt, and clay content is 5.47, 5.18, and 5.39 g 100 g−1, respectively. The R2 for validation is 0.30, 0.59, and 0.69 for the same attributes. Partitioning the model by depth did not improve the predictions significantly. The results show that estimating soil texture from a limited spectral region is promising and can contribute toward the development of cheaper spectrometers or infrared cameras that can be used for digital soil morphometrics.

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

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.

Digital Soil Morphometrics via a Low-Cost Radiometer for Estimating Soil Organic Carbon and Texture

There is scientific evidence toward the incorporation, in a near feature, of diffuse reflectance spectroscopy (DRS) as an everyday laboratory tool for soil attribute determination. Nevertheless, research still has to be conducted toward the capabilities of limited ranges of the spectra (i.e., 325–1075 nm), as well as the use of more affordable spectrometers. This study aimed at evaluating the capacity of a 15,000 USD spectrometer for estimating soil organic carbon (SOC) and texture. Soil samples were collected in 10 Ferralsol profiles of basaltic parental material in Serra Geral Formation in southern of Brazil. Spectral signatures were collected in 45 air-dried soil samples previously sieved through 2-mm mesh and 45 soil samples grounded in an agate mortar. Sample preparation through pestle grounding showed a slight gain in modeling accuracy. The best results of partial least squares regression (PLSR) were achieved for SOC with an error of prediction of 2.44 g kg−1, R 2 of 0.88, and RPD of 2.85. These results are an indication of the applicability of a low-cost spectrometer for soil attribute determination through DRS. This approach could lead to a wider adoption of the technique, especially in laboratories were there are budget limitations and are in need of this important soil attribute determination.

Mapeamento digital de atributos: granulometria e matéria orgânica do solo utilizando espectroscopia de reflectância difusa

Diffuse reflectance spectroscopy (DRS) can be used as an alternative in identifying and quantifying some soil properties such as particle size and soil organic matter (SOM). This technique may be an alternative to quantifying those properties in a large volume of soil samples since it is faster and less costly and does not produce chemical residues. The aim of this study was to develop models using multiple linear regression analysis to predict the content of clay, sand, silt, and SOM using DRS data in an area of complex topography and geology located in the central region of Rio Grande do Sul, Brazil. In the study, 303 samples were collected at a depth of 0.00-0.20 m for determination of clay, sand, silt, and SOM by laboratory and spectral reflectance analysis. The predictive models produced high-quality results, explaining 77 and 72 % of the variance for sand and clay, respectively. The interpolation maps of the observed and predicted properties revealed that the spatial patterns are mainly associated with the topography and geology of the area. Even with the geological and pedological complexity, the results indicated that the technique is promising and it may be applied for prediction of particle size and SOM.