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

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 Sao 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).Estimations 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 (R2 = 0.84, RMSE = 3.75) and sand (R2 = 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.

[1]  Alex B. McBratney,et al.  Soil chemical analytical accuracy and costs: implications from precision agriculture , 1998 .

[2]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[3]  A. Rencz,et al.  Remote sensing for the earth sciences , 1999 .

[4]  E. R. Stoner,et al.  REFLECTANCE PROPERTIES OF SOILS , 1986 .

[5]  T. L. Coleman,et al.  SPECTRAL DIFFERENTIATION OF SURFACE SOILS AND SOIL PROPERTIES: IS IT POSSIBLE FROM SPACE PLATFORMS? , 1993 .

[6]  David C. Jones,et al.  Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents , 2008 .

[7]  G. McCarty,et al.  Mid-Infrared and Near-Infrared Diffuse Reflectance Spectroscopy for Soil Carbon Measurement , 2002 .

[8]  R. Plant Site-specific management: the application of information technology to crop production , 2001 .

[9]  Jurandir Zullo Junior,et al.  Correção atmosferica de imagens de satelite e aplicações , 1994 .

[10]  E. Ben-Dor The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process , 1997 .

[11]  Ewald Schnug,et al.  Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements , 2008 .

[12]  J. Demattê,et al.  Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis , 2006 .

[13]  P. Lagacherie,et al.  Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements , 2008 .

[14]  Antônio Roberto Formaggio,et al.  Variações espectrais em solos submetidos à aplicação de torta de filtro , 2005 .

[15]  Joji Iisaka,et al.  Extraction of Soil Information from Vegetated Area , 1979 .

[16]  Gilberto J. Garcia,et al.  Alteration of Soil Properties through a Weathering Sequence as Evaluated by Spectral Reflectance , 1999 .

[17]  J. M. Netto,et al.  Spectral reflectance properties of soils , 1996 .

[18]  Francis X. M. Casey,et al.  Improved design for an automated tension infiltrometer , 2002 .

[19]  Daniel C. Coster,et al.  High dimensional reflectance analysis of soil organic matter , 1992 .

[20]  Scott Edwards,et al.  Application of near‐infrared spectroscopy in analysis of soil mineral nutrients , 1999 .

[21]  B. Markham,et al.  Radiometric Calibration of Landsat , 1997 .

[22]  Mats Söderström,et al.  The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale , 2008, Precision Agriculture.

[23]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[24]  Eyal Ben-Dor,et al.  Near-Infrared Analysis as a Rapid Method to Simultaneously Evaluate Several Soil Properties , 1995 .

[25]  E. Ben-Dor Quantitative remote sensing of soil properties , 2002 .

[26]  Gilberto Câmara,et al.  Spring: integrating remote sensing and gis by object-oriented data modelling , 1996, Comput. Graph..

[27]  Rasmus Fensholt,et al.  Remote Sensing , 2008, Encyclopedia of GIS.

[28]  R. V. Rossel,et al.  Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy , 2006 .

[29]  L. Janik,et al.  Can mid infrared diffuse reflectance analysis replace soil extractions , 1998 .

[30]  Garey A. Fox,et al.  Estimation of Soil Organic Matter from Red and Near‐Infrared Remotely Sensed Data Using a Soil Line Euclidean Distance Technique , 2002 .

[31]  Marvin H. Hall,et al.  Carbon and Nitrogen Analysis of Soil Fractions Using Near-Infrared Reflectance Spectroscopy , 1991 .

[32]  R. Sahoo,et al.  Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS-NIR-SWIR) region , 2009 .

[33]  Wouter Saeys,et al.  Potential for Onsite and Online Analysis of Pig Manure using Visible and Near Infrared Reflectance Spectroscopy , 2005 .

[34]  David A. Landgrebe,et al.  Spectral band selection for classification of soil organic matter content , 1989 .

[35]  Marcos Rafael Nanni,et al.  Comportamento da linha do solo obtida por espectrorradiometria laboratorial para diferentes classes de solo , 2006 .

[36]  R. Jackson Spectral indices in N-Space , 1983 .

[37]  A. Huete,et al.  Assessment of biophysical soil properties through spectral decomposition techniques , 1991 .

[38]  W. S. Lee,et al.  REFLECTANCE SPECTROSCOPY FOR ROUTINE AGRONOMIC SOIL ANALYSES , 2007 .

[39]  MARCOS RAFAEL NANNI,et al.  Quantification and discrimination of Soils Developed from Basalt as Evaluated by Terrestrial , Airborne and Orbital Sensors , 2001 .

[40]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[41]  L. H. C. Anjos,et al.  Sistema Brasileiro de Classificação de Solos. , 2006 .

[42]  Yoram J. Kaufman,et al.  Atmospheric correction against algorithm for NOAA-AVHRR products: theory and application , 1992, IEEE Trans. Geosci. Remote. Sens..

[43]  A. J. Turgeon,et al.  Determination of Soil Separates with near Infrared Reflectance Spectroscopy , 1996 .

[44]  T. L. Coleman,et al.  SPECTRAL BAND SELECTION FOR QUANTIFYING SELECTED PROPERTIES IN HIGHLY WEATHERED SOILS , 1991 .

[45]  F. E. Nicodemus,et al.  Geometrical considerations and nomenclature for reflectance , 1977 .

[46]  H. Beecher,et al.  The potential of near-infrared reflectance spectroscopy for soil analysis — a case study from the Riverine Plain of south-eastern Australia , 2002 .

[47]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[48]  Heitor Cantarella,et al.  Variability of Soil Analysis in Commercial Laboratories: Implications for Lime and Fertilizer Recommendations , 2006 .

[49]  R. V. Rossel,et al.  In situ measurements of soil colour, mineral composition and clay content by vis–NIR spectroscopy , 2009 .

[50]  Ewald Schnug,et al.  Sampling and nutrient recommendations ‐ the future , 1998 .

[51]  S. Searcy,et al.  An Automated Soil Line Identification Routine for Remotely Sensed Images , 2004 .

[52]  J. Demattê,et al.  Effect of fermentation residue on the spectral reflectance properties of soils , 2004 .

[53]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .