Vis-NIR-SWIR Remote Sensing Products as New Soil Data for Digital Soil Mapping

Since the early ages of soil surveys, air photographs have been intensively used by soil surveyors for depicting the soil variations across landscapes. The variations of soil surfaces, specifically color and ratio of vegetation cover, that were revealed by this early remote sensing product were a great help for interpolating the scarce soil observations and for delineating the soil class boundaries. This was further transposed in digital soil mapping (McBratney et al. 2003), thanks to the large availability of remote sensing images provided by the emerging spatial data infrastructures. Up to now, digital soil mappers have mainly used remote sensing images as spatial data inputs for representing the landscape variables that are related with soil, such as vegetation and parent material (the soil covariates). Boettinger et al. (2008) reviewed the main indicators that could be retrieved for estimating these soil covariates, using multispectral data acquired in the visible near-infrared and short-wave infrared (VIS, 400–700 nm; NIR, 700–1100 nm; SWIR, 1100–2500 nm) spectral domain. After a spatial overlay with the sparse sets of observed and measured sites collected in a given area, the indicators derived from remote sensing have been used as independent variables in regression-like models or as external drift in geostatistic models (McBratney et al. 2003, Chap. 12 of this book).

[1]  B. Wesemael,et al.  Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy , 2013 .

[2]  Ronald D. Snee,et al.  Validation of Regression Models: Methods and Examples , 1977 .

[3]  Budiman Minasny,et al.  Evaluating near infrared spectroscopy for field prediction of soil properties. , 2009 .

[4]  M. F. Baumgardner,et al.  Effects of organic matter on the multispectral properties of soils , 1969 .

[5]  Yufeng Ge,et al.  VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact , 2014 .

[6]  P. Lagacherie,et al.  Detecting, correcting and interpreting the biases of measured soil profile data: A case study in the Cap Bon Region (Tunisia) , 2013 .

[7]  J. Roger,et al.  EPO–PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits , 2003 .

[8]  P. Lagacherie,et al.  Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data , 2012 .

[9]  Philippe Lagacherie,et al.  Using scattered hyperspectral imagery data to map the soil properties of a region , 2012 .

[10]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

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

[12]  Sabine Chabrillat,et al.  Imaging Spectrometry for Soil Applications , 2008 .

[13]  J. Hill,et al.  Using Imaging Spectroscopy to study soil properties , 2009 .

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

[15]  T. Jarmer,et al.  Linking spatial patterns of soil organic carbon to topography — A case study from south-eastern Spain , 2011 .

[16]  R. D. Ramsey,et al.  Landsat Spectral Data for Digital Soil Mapping , 2008 .

[17]  Lammert Kooistra,et al.  Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Sabine Chabrillat,et al.  Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution , 2002 .

[19]  Edzer J. Pebesma,et al.  A geostatistical approach to data harmonization - Application to radioactivity exposure data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[20]  R. J. Hanks,et al.  REFLECTION OF RADIANT ENERGY FROM SOILS , 1965 .

[21]  P. Lagacherie,et al.  Using legacy data for correction of soil surface clay content predicted from VNIR/SWIR hyperspectral airborne images , 2016 .

[22]  B. Minasny,et al.  Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbon , 2011 .

[23]  Philippe Lagacherie,et al.  Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements , 2008 .

[24]  Rodolphe Marion,et al.  HYPXIM: A second generation high spatial resolution hyperspectral satellite for dual applications , 2013, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[25]  José Madeira Netto,et al.  Caractéristiques spectrales des surfaces sableuses de la région côtière Nord-Ouest de l'Egypte : application aux données satellitaires SPOT , 1991 .

[26]  E. R. Stoner,et al.  Characteristic variations in reflectance of surface soils , 1981 .

[27]  P. Lagacherie,et al.  Combining Vis–NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia) , 2013 .

[28]  Budiman Minasny,et al.  Analysis and prediction of soil properties using local regression-kriging , 2012 .

[29]  Ross S. Bricklemyer,et al.  On-the-go VisNIR: Potential and limitations for mapping soil clay and organic carbon , 2010 .

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

[31]  M. Schaepman,et al.  Spectral reflectance based indices for soil organic carbon quantification , 2008 .

[32]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[33]  Michael E. Schaepman,et al.  Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging spectroscopy , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Sabine Grunwald,et al.  Multi-criteria characterization of recent digital soil mapping and modeling approaches , 2009 .

[35]  Cristine L. S. Morgan,et al.  Predicting clay content on field-moist intact tropical soils using a dried, ground VisNIR library with external parameter orthogonalization , 2015 .

[36]  N. Flay,et al.  Visible spectrometric indices of hematite (Hm) and goethite (Gt) content in lateritic soils: The application of a Thematic Mapper (TM) image for soil-mapping in Brasilia, Brazil , 1997 .

[37]  Philippe Lagacherie,et al.  Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces , 2011 .

[38]  J. Roger,et al.  Analysis of the uncertainties affecting predictions of clay contents from VNIR/SWIR hyperspectral data , 2015 .

[39]  H. Herrmann,et al.  Dune whitening and inter-dune freshwater ponds in NE Brazil , 2007 .

[40]  S. Gaffey,et al.  Spectral reflectance of carbonate minerals in the visible and near infrared (O.35-2.55 microns); calcite, aragonite, and dolomite , 1986 .

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

[42]  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 .

[43]  Andre G. Journel,et al.  Constrained interpolation and qualitative information—The soft kriging approach , 1986 .

[44]  P. Lagacherie,et al.  Semi-blind source separation for the estimation of the clay content over semi-vegetated areas using VNIR/SWIR hyperspectral airborne data , 2016 .

[45]  P. Lagacherie,et al.  Using Vis-NIR hyperspectral data to map topsoil properties over bare soils in the Cap Bon region, Tunisia , 2012 .

[46]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[47]  Eyal Ben-Dor,et al.  Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor , 2006 .

[48]  R. Morris,et al.  Spectral and other physicochemical properties of submicron powders of hematite (alpha-Fe2O3), maghemite (gamma-Fe2O3), magnetite (Fe3O4), goethite (alpha-FeOOH), and lepidocrocite (gamma-FeOOH). , 1985, Journal of geophysical research.

[49]  E. Ben-Dor,et al.  Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils , 2007 .

[50]  Alex B. McBratney,et al.  Modelling soil attribute depth functions with equal-area quadratic smoothing splines , 1999 .

[51]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[52]  A. Huete,et al.  Etude des propriétés spectrales des sols arides appliquée à l'amélioration des indices de végétation obtenus par télédétection , 1991 .

[53]  F. Meer.  Analysis of spectral absorption features in hyperspectral imagery , 2004 .

[54]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[55]  P. Lagacherie,et al.  Co-Kriging of soil properties with Vis-NIR hyperspectral covariates in the Cap Bon region (Tunisia) , 2012 .

[56]  U. Schmidhalter,et al.  High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures , 2006 .

[57]  H. Kaufmann,et al.  Surface soil moisture quantification models from reflectance data under field conditions , 2008 .

[58]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[59]  Budiman Minasny,et al.  On digital soil mapping , 2003 .

[60]  E. Ben-Dor,et al.  NEAR INFRARED ANALYSIS (NIRA) AS A METHOD TO SIMULTANEOUSLY EVALUATE SPECTRAL FEATURELESS CONSTITUENTS IN SOILS , 1995 .