Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis

Abstract

Traditional soil analyses are expensive, time‐consuming, and may also result in environmental pollutants. The objective of this study was to develop and evaluate a methodology to measure soil attributes using spectral reflectance (SR) as an alternative to traditional methods. Tropical Brazilian soils were sampled over a 196‐ha area divided into grids. Samples (n = 184) were obtained from the 0‐ to 20‐ and 80‐ to 100‐cm depths and georeferenced. The laboratory SR data were obtained using a Spectroradiometer (400–2 500 nm). Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Particle‐size distribution and chemical analysis (organic matter [OM], cation‐exchange capacity [CEC], total SiO2, Fe2O3, TiO2, sum of cations, cation, and Al saturation) were performed in the laboratory. Statistical analysis and multiple regression equations for soil attribute predictions using radiometric data were developed. Laboratory data used 22 bands and 13 “Reflectance Inflexion Differences, RID” from different wavelength intervals of the optical spectrum. However, the satellite data used only the reflectance of the 1, 2, 3, 4, 5, and 7 TM‐Landsat bands. Multiple regression equations were derived from surface and subsurface soil layers. Estimations of some tropical soil attributes were possible using laboratory spectral analysis. Laboratory SR yielded high correlations with traditional laboratory analyses (R2 > 0.79) for the soil attributes such as clay, sand, TiO2, and Fe2O3 Satellite spectral data correlated well with most of the soil attributes such as clay, Fe2O3, and TiO2 (reaching R2 = 0.72). The use of soil analysis methodology by satellite and/or ground remote sensing constitutes an alternative to traditional routine laboratory analysis.

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

“…Coleman et al (1993) started with 0.4 R² for clay using Landsat. Nanni and Demattê (2006) reached 67 % accuracy for the clayey class when evaluated an area with full exposure of non-vegetated areas (bare soils). Demattê et al (2009) reached 90 % accuracy evaluating 224 pixels in a 300,000 ha area, but in this case, it was all done in a single image.…”

“…In particular, many studies have found that texture can be quantified by spectral reflectance under laboratory conditions (Nanni and Demattê, 2006). Nanni et al (2012) have also observed the importance of orbital images for clay estimates.…”

Comparison between detailed digital and conventional soil maps of an area with complex geology

“…This 182-ha area, at altitudes between 475 and 567 m asl, is bordered by low lands along the course of the Rio Capivari; it had already been studied by Nanni & Demattê (2006) and is used for sugar cane production. The regional climate is Cwa (mesothermic with dry winters), according to the Köppen classification.…”

“…The local geology is dominated by the Itararé Formation, where siltstones are common, alternated with diabase dikes and sills from the Serra Geral Formation (Mezzalira, 1966), pleistocenic and holocenic fluvial sediments along the Rio Capivari terraces (Nanni & Demattê, 2006), and hardened siltstone areas near the diabase, due to contact metamorphism.…”

Soil spectral library and its use in soil classification

“…In many recent studies, the application of remote sensing techniques has gained much attention, mainly because these techniques have been shown to generate faster and cheaper characterizations, earning them credibility in the scientific community. The application of remote sensing techniques in soil studies began in the 1960s (Bowers & Hanks, 1965) and expanded to various applications, including quick and nondestructive quantification of soil attributes (Janik et al, 1998;Shepherd & Walsh, 2002, Dunn et al, 2002ViscarraRossel, et al, 2006a;Demattê & Nanni, 2006;Brown, et al,. 2006), soil survey and classification (Demattê et al, 2004, Ben-Dor et al, 2008, mineralogical measurements (Madeira-Netto, 1996;Viscarra-Rossel et al, 2006b;Sellito et al, 2009), digital soil mapping (Viscarra-Rossel & McBractney, 2008), precision agriculture (Thomasson et al, 2001Mouazen et al, 2007) and quantification of heavy metals (Wu Zhao et al, 2005).…”