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In soil surveys, the number of collected samples is commonly reduced by factors that hamper field activities, such as rugged terrain and lack of roads. Conditioned Latin hypercube (CLH) sampling has been used to properly capture soil variability across the landscape, whereas cost-constrained conditioned Latin hypercube (CCLH) sampling limits the sampling to areas of easy access. The objectives of this work were to: (i) compare the efficiency of CLH and CCLH sampling systems to create soil maps, considering the number of soil classes covered per system, (ii) compare both systems to map soil A horizon thickness, and (iii) generate a detailed soil map of the study area to assist in decision makings. The study was performed in Minas Gerais, Brazil. A digital elevation model (DEM) and its terrain derivatives were the basis for CLH and CCLH to determine the sampling points. The CCLH system also required a cost map that represented the difficulty of reaching every place in the area. At the sampling locations, soil information was observed, allowing for the creation of those maps that were further validated in the field. Kappa index, global index (Gl), RMSE, 1:1 ratio graphic, and R² were the comparison parameters. Conditioned Latin hypercube presented higher accuracy than CCLH to represent both soil classes and soil attributes, although the samples were spread out in the area. Cost-constrained conditioned Latin hypercube was less representative than CLH, but it may contribute to soil sampling in areas of difficult access, common in developing countries, such as Brazil. [ABSTRACT FROM AUTHOR]

The use of satellite images is a widespread technique for estimating soil attributes. However, what is the potential of satellite data on the prediction of soil weathering indices? How much in their performance is affected by spectral mixing and data resolution? There is no consensus for answering these questions. Hence, the main objectives of this work were: i) to show the difference of spectral signature between prepared and field-condition samples; ii) to compare the convoluted data from laboratory spectroradiometer with the spectral signatures from Sentinel-2 and Landsat-8 satellites; and iii) to build and evaluate predictive models for soil weathering indices using diffuse reflectance spectroscopy from samples under natural conditions, air-dried-sieved and from satellite images. Soil samples were collected from the first 1-cm depth in 27 locations along seven catenas located in a 163-ha area in São Paulo State, Brazil. The samples were analyzed by a Vis-NIR-SWIR spectrometer without sample preparation (field conditions) and after being air-dried and sieved (prepared). The air-dried-sieved samples were also analyzed by an X-ray fluorescence equipment. Reflectance data were obtained from Sentinel and Landsat for the same 27 locations. The spectral data from the lab were convoluted to respective bands of the real satellite data. Predictive models for soil weathering indices using the convoluted laboratory data and the satellite data were also built and compared. The reflectance of samples under field conditions contrasted significantly between soils derived from basalt and sedimentary rocks. In general, the reflectance intensity of prepared samples was 80% higher than that observed for field-conditions, which can be explained by the roughness and the structure effect. However, some cases showed an opposite trend, with a smaller relative reflectance from the air-dried-sieved samples. These trends support the hypothesis of aggregates heterogeneity and clay occlusion. The sample preparation can increase or decrease the reflectance of the soil surface material, depending, mainly, on roughness, soil structure and particles occlusion by the structure. The Landsat spectral signatures were more similar to those from convoluted laboratory than to the Sentinel ones. The satellite performance on the estimation of the soil weathering indices is comparable to those from convoluted laboratory data, but it is limited by spectral resolution. Sentinel and Landsat achieved ratio of performance to interquartile distance (RPIQ) from 1.53 to 2.38 and from 1.56 to 2.42 on soil weathering indices estimation, respectively. The models for Plagioclase Index of Alteration (PIA) and Si/Ti [Si and (Ti and Al) ratio] indices were considered the best ones (RPIQ from 2.17 to 3.6; and RPIQ from 1.90 to 5.56; respectively) for both laboratory and satellite data; in turn, Si/Al, Si (Al + Fe) and Chemical Index of Alteration (CIA) indices performed poorly/reasonably (RPIQ <1.92). • The satellite performance was similar to those from convoluted laboratory data. • Sample preparation can change the reflectance of the soil surface. • The L samples showed a higher reflectance. • Landsat curves are more similar to those obtained in laboratory. • Spectral curves from Sentinel showed pronounced bands on the visible range. [ABSTRACT FROM AUTHOR]