Variation of Routine Soil Analysis When Compared with Hyperspectral Narrow Band Sensing Method

Abstract

Abstract:The objectives of this research were to: (i) develop hyperspectral narrow-band models to determine soil variables such as organic matter content (OM), sum of cations (SC = Ca + Mg + K), aluminum saturation (m%), cations saturation (V%), cations exchangeable capacity (CEC), silt, sand and clay content using visible-near infrared (Vis-NIR) diffuse reflectance spectra; (ii) compare the variations of the chemical and the spectroradiometric soil analysis (Vis-NIR). The study area is located in Sã o Paulo State, Brazil. The soils were sampled over an area of 473 ha divided into grids (100 × 100 m) with a total of 948 soil samples georeferenced. The laboratory RS data were obtained using an IRIS (Infrared Intelligent Spectroradiometer) sensor (400-2,500 nm) with a 2-nm spectral resolution between 450 and 1,000 nm and 4-nm between 1,000 and 2,500 nm. Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Each pixel in the image was evaluated as its vegetation index, color compositions and soil line concepts regarding certain locations of the field in the image. Chemical and physical analysis (organic matter content, sand, silt, clay, sum of cations, cations saturation, aluminum saturation and cations exchange capacity) 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, for TM-Landsat six bands were used in analysis (1, 2, 3, 4, 5, and 7). OPEN ACCESSRemote Sensing 2010, 2 1999Estimations of some tropical soil attributes were possible using laboratory spectral analysis. Laboratory spectral reflectance (SR) presented high correlations with traditional laboratory analyses for the soil attributes such as clay (R 2 = 0.84, RMSE = 3.75) and sand (R 2 = 0.85, RMSE = 3.74). The most sensitive narrow-bands in modeling (using 474 observations) these attributes were B8 (1,350-1,417 nm), B10 (1,417-1,449 nm), B11 (1,449-1,793 nm), B15 (1,927-2,102 nm), B16 (2,101-2,139 nm), and B17 (2,139-2,206 nm); B7 (975-1,350 nm), B10, B11, B16, B19 (2,206-2,258 nm) and B21 (2,258-2,389 nm) for clay and sand, respectively. The bands selected to model sand and clay, by orbital data, were 3, 5 and 7 of TM-Landsat-5 and 2, 5 and 7 sand and clay, respectively. The use of soil analysis methodology by ground remote sensing constitutes an alternative to traditional routine laboratory analysis.

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

“…Nevertheless, the achieved model underlined the high correlation between sand particles and aisaDUAL soil spectra. The predictive model for silt can be graded as relatively robust in comparison to laboratory studies providing lower model accuracies [13,14]. However, Hively et al [20] presented results based on image data, which led to similar results.…”

“…The hyperspectral image data were acquired with a 2.5 m ground sampling distance (GSD) in 178 spectral bands. Demattê et al [14] applied their laboratory data approach to multispectral Landsat 5 images with a 30 m spatial resolution and six spectral bands, resulting in a drastic performance drop for sand (R 2 = 0.64) and clay (R 2 = 0.61) compared to the models built under laboratory conditions. Due to the aforementioned constraints, soil texture modeling from image spectra performs worse compared to laboratory spectra, but still shows high potential for further investigations.…”

Determination of soil physicochemical attributes in farming sites through visible, near-infrared diffuse reflectance spectroscopy and PLSR modeling

“…The VNIR-DRS is productive, rapid, non-wasting, non-destructive, and more accurate as compared to customary ways. The in situ method provides information about the SPA without perilous compounds (Awiti et al 2008;Bilgili et al 2010;Brown et al 2006;Demattê et al 2010;Rossel et al 2006Rossel et al , 2016Srivastava et al 2017;Udelhoven et al 2003;Zornoza et al 2008). In addition, a single spectrum of soils provides information about the various SPA and the methods are adaptable for on-the-go at the field (Rossel et al 2006).…”

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

“…Several authors have studied the relationships between soil properties and spectral reflectance data, for example soil salinity [24][25][26][27][28], OM [29][30][31][32][33] and clay content [34][35][36][37][38]. According to Ben-Dor et al [39] changes in the spectral response occur due to changes in soil albedo and soil chromophores.…”