Application of near-infrared reflectance for quantitative assessment of soil properties

Application of near infrared reflectance for quantitative assessment of soil properties The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx Contents lists available at Science[.]

The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx Contents lists available at ScienceDirect The Egyptian Journal of Remote Sensing and Space Sciences journal homepage: www.sciencedirect.com Review Article Application of near-infrared reflectance for quantitative assessment of soil properties E.S Mohamed ⇑, A.M Saleh, A.B Belal, Abd_Allah Gad National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt a r t i c l e i n f o Article history: Received 24 July 2016 Revised 31 January 2017 Accepted February 2017 Available online xxxx Keywords: Near infrared spectroscopy Soil salinity Soil moisture Soil organic carbon Soil surface features and soil contamination a b s t r a c t Beginning with a discussion of reflectance spectroscopy, this article attempts to provide a review on fundamental concepts of reflectance spectroscopic techniques Their applications as well as exploring the role of Near-infrared reflectance spectroscopy that would be used for monitoring and mapping soil characteristics This technique began to be used in the second half of the 20th century for industrial purposes Moreover, this article explores the potentiality of predicting soil properties based on spectroscopic measurements Quantitative prediction of soil properties such as; salinity, organic carbon, soil moisture and heavy metals can be conducted using various calibration models – such models were developed depending on the measured soil laboratory analyses data and soil reflectance spectra thereby resampled to satellite images - to predict soil properties The most common used models are stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), principal component regression (PCR) and artificial neural networks (ANN) Those methods are required to quickly and accurately measure soil characteristics at field to improve soil management and conservation at local and regional scales Visable-Near Infra Red (VIS-NIR) has been recommended as a quick tool for mapping soil properties Furthermore, VIS-NIR reflection spectroscopy reduces the cost and time, therefore has a wonderful ability and potential use as a rapid soil analysis for both precision soil management and assessing soil quality Ó 2017 Production and hosting by Elsevier B.V on behalf of National Authority for Remote Sensing and Space Sciences This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/) Contents Introduction Spectroscopy definitions and history of spectroscopy Near-infrared reflectance spectroscopy Predictive models Applications of NIRS in soil sciences 5.1 Soil salinity 5.2 Soil moisture 5.3 Soil organic carbon 5.4 Clay minerals 5.5 Soil surface features 5.6 Soil contamination Conclusion References Peer review under responsibility of National Authority for Remote Sensing and Space Sciences ⇑ Corresponding author E-mail addresses: Salama55_55@yahoo.com, Salama55@mail.ru (E.S Mohamed) http://dx.doi.org/10.1016/j.ejrs.2017.02.001 1110-9823/Ó 2017 Production and hosting by Elsevier B.V on behalf of National Authority for Remote Sensing and Space Sciences This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 00 00 00 00 00 00 00 00 00 00 00 00 00 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Introduction Soil is described as a heterogeneous system, their mechanisms and processes are complex and difficult to be fully understood Numerous traditional methods are used in an endeavour to describe the relationship between different soil properties such physical, chemical and principal soil components Consequently, simple and accurate soil testing procedures are required in field and laboratory Near-infrared reflectance spectroscopy (NIRS) is a nondestructive systematic strategy for characterizing and identifying soil properties Those techniques have been used since the 1960 to estimate moisture, protein, and oil in agricultural products (Ben-Gera and Norris, 1968) During last three decades, numerous studies illustrated that the spectral reflectance property of soil samples in laboratory conditions, as well as field investigation of soils’ characteristics, can be assessed where remote photography materials occupy an increasingly significant place in the organization of soil cover monitoring (Mohamed, 2013; Mohamed et al., 2015; Saleh et al., 2015; Savin, 1993) Recently, this technique is widely used in several fields as an amazing tool for evaluating such agriculture, food, polymer pharmaceuticals and petrochemicals Moreover, the technique, NIR method can be applied to predict soil properties as additional (to laboratory analysis) or initial assessment of soil quality (Demattê and da Silva Terra, 2014; Mateusz Kania and Piotr gruba, 2016) Near-infrared reflectance spectroscopy (NIRS) has been used to predict several soil properties such soil organic carbon, soil moisture content, soil contemenation, soil salinity, etc Soil electrical conductivity can be detected using visible, near infrared, or short-wave infrared spectral bands from optical sensors to be promising for the detection of surface soil salinity The intensive of reflectance is related to concentration of soluble salts in salt-affected soils (Dwivedi and Rao, 1992; Khan et al., 2005; Nield et al., 2007; Abdi et al., 2016) Many authors suggested that, infrared and red channels are applicable methods to monitor soil characteristics such iron oxides and soil moisture are considered (Samsonova and Meshalkina, 2011; Sonia et al., 2012 and Niederberger et al., 2015) Near infrared (NIR) and midinfrared (MIR) ranges are promising technologies considered as a quantitative ones that gives good results for heavy metals concentration as there is a high correlation between pollutants and their spectral indicators Reflectance spectroscopy techniques have been used for retrieving and mapping the distribution of heavy metals such as Pb at high accuracy (Samsonova and Meshalkina, 2011) Many of regression models are used to estimate quantitative and qualitative analyses of the various soil elements, based on investigating the correlation between each element properties and the observance for each selected wavelength However, the most widespread regression models are partial least square regression (PLSR), multivariate adaptive regression splines (MARS), ordinal logistic regression, stepwise multiple linear regression (SIMR), artificial neural networks (ANN), locally weighted regression (LWR) and principal components regression (PCR) (Chang et al., 2001; Ciurczack, 2001; Nawar et al., 2014; Fikrat et al., 2016; Zheng et al., 2016) Spectroscopy definitions and history of spectroscopy Spectroscopy is the science that studies the interaction between matter and its electromagnetic radiation (Crouch and Skoog, 2007) Reflectance spectroscopy is the study of light as a function of wavelength that has been reflected or scattered from a solid, liquid, or gas This concept was expanded greatly to include any interaction with radiative energy as a function of its wavelength or frequency Spectroscopic data is often represented by aspectrum, a plot of the response of interest as a function of wavelength or frequency (Herrmann and Onkelinx, 1986; Clark, 1999) The history of spectroscopy began in the 17th century with Isaac Newton’s discovery of the with Isaac Newton’s discovery of the light nature and color basics He introduced the word ‘‘spectrum” at first application to describe the rainbow of colors combination to form white light During the early 1800s, Joseph von Fraunhofer made experimental advances with dispersive spectrometers that enabled spectroscopy to become a more precise and quantitative scientific technique Since then, spectroscopy has played and continues to play a significant role in chemistry, physics and astronomy (Brand, 1995) As far as the development of instrumentation and its breakthrough for industrial applications in the second half of the 20th century were concerned, NIR proceeded in technology jumps (Fig 1) In this respect, credit has largely to be given to researchers in the field of agricultural science At the same time, with few exceptions, comparatively low priority has been given to NIR spectroscopy in the chemical industry (Siesler et al., 2002) This technique recently has been developed into essential methods for scientific research and industrial quality control in a different applications such chemistry, environmental analysis, agriculture and as well as life sciences Near-infrared reflectance spectroscopy The fundamental principle of VisNIR is based on the differences in molecular characteristics, where spectral signatures of different materials are categorized based on their reflectance and absorbance spectra The change in signatures is referred to vibrational extending and bending of atoms that arrange molecules and crystals Most soil components are usually observed in the midinfrared region vibrations (2500–25,000 nm), with overtones and combinations found in the near-infrared region (400–2500 nm) (Clark, 1999; Shepherd and Walsh, 2002) The electromagnetic (EM) spectrum ranges from gamma (c) rays, at the shortest wavelengths, to radio-waves, at the longest wavelengths (Fig 2) Most common sensing systems operate in one or several of the visible, infrared (IR) and microwave portions of the spectrum Sensor data covering those wavelengths are readily available from both satellite and airborne platforms (NASA, 2014) The energy of infrared light corresponds to the energy required to cause molecular vibrations Moreover, the far-IR region (A =
l04  106 nm) harmonize to molecular variations and the mid-IR (A = 2500 
l04 nm) corresponds to fundamental molecular vibrations, such as stretching, bending, wagging, and scissoring The energy of near-IR light corresponds to overtones and combination bands of fundamental molecular vibrations from the mid-IR (Drago, 1992; Workman, 1996) Vibrational spectroscopy is depending on interactions between the molecules and electronic field components of incident light in the mid- and near-IR region Such interactions result in absorption of light by molecules when the energy of incident light (Ep) is equal to the energy difference (AE) between the quantized energy levels of different vibrational states of the molecule (Fig 3) Their relationship can be expressed as: Ep ¼ hv ¼ hc=A: ẳ AE; 1ị where: v is the frequency of incident light, c is velocity of light, A is the wavelength, and h is Plank’s constant The energy difference, AE, is specified by chemical bonds of functional groups in the molecules A molecule must undergo a Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig Development of near-infrared spectroscopy (Source: Siesler et al., 2002) Fig Electromagnetic spectrum (Source; NASA 2014) change in dipole moment in order to absorb IR light Based on a harmonic oscillator, the permitted energy states of a molecule are given by: Ev ẳ hvv ỵ 1=2ị 2ị where:v is the vibrational quantum number (v = 0, l, 2, .) The fundamental vibration means that the transition from v = to v = l, according to the selection rule for a harmonic oscillator Furthermore, if the chemical bond is too weak or the atoms are too heavy, the fundamental vibration will occur at very low frequency As a result, the higher overtones, in the near-IR region, may not be detectable Therefore, the near-IR is dominated by the overtones and the combinations of fundamental vibrations for O–H, C–H, and N–H found in mid-IR (Wetzel, 1983) The amount of light absorbed is a function of the absorber concentration Based on the Beer-Lambert law, the relationship between absorbance (A), transmittance (T), and concentration (c) for monochromatic light can be expressed as follows; A ẳ logl=Tị ẳ logIo =Iị ẳ klc; ð3Þ where: Io is the intensity of the incident light, I is the intensity of the transmitted light, k is the molecular absorption coefficient, and l is the path length of light through the sample The molecular absorption coefficient, k, is the characteristic of each molecule and is dependent on the wavelength of the incident light However, the reflectance of radiation from one type of surface material, such as soil, varies over the range of wavelengths in the electromagnetic spectrum and known as the spectral signature of the material (Fig 4) Predictive models Prediction of different soil characteristics using spectral reflections depends on statistical models that explain the relationship Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig Stretching and bending vibrations Fig Spectral resolution of some materials (Source: Short, 2011) between them, most common used models are stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), multivariate adaptive regression splines (MARS), principal component regression (PCR) and artificial neural networks (ANN) SMLR is a statistical method of regressing multiple variables while simultaneously removing those that aren’t important The choice of predictive variables is carried out by an automatic procedure (Efroymson, 1960; Hocking, 1976; Draper and Smith, 1981; and SAS, 1989) The variable that considered for addition to or subtraction from the set of explanatory variables in each step is based on a form of a sequence of F-tests or t-tests The widely used algorithm was first proposed by Efroymson (1960) The main types of Stepwise multiple linear regression are forward selection, backward elimination, and bidirectional elimination The forward selection involves starting with no variables in the model, testing the addition of each variable using a chosen model fit criterion, adding the variable (if any) whose inclusion gives the most statistically significant improvement of the fit, and repeating this process until none improves the model to a statistically significant extent Backward elimination involves starting with all candidate variables, testing the deletion of each variable using a chosen model fit criterion, deleting the variable (if any) whose loss gives the most statistically insignificant deterioration of the model fit, and repeating this process until no further variables can be deleted without a statistically significant loss of fit The bidirectional elimination is a combination of the forward selection and backward elimination Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig (a) PLSR in 2006; (b) MARS in 2006; (c) PLSR in 2012; and (d) MARS in 2012 (Source: Nawar et al., 2014) types The accuracy of SMLR model is measured as the actual standard error (SE) or the mean error between the predicted value and the actual value in the hold-out sample (Mayers and Forgy, 1963) Fig PLS is a statistical method that finds a linear regression by projecting the predicted variables and the observable variables to a new space (Tenenhaus et al., 2005; Vinzi et al., 2010) PLS regression is today most widely used in chemometrics, sensometrics, and other related areas (Rönkkö et al., 2015) MARS is a form of non-parametric regression analysis (Friedman, 1991) MARS is an extension of linear models that automatically models nonlinearities and interactions between vari- ables MARS is also called EARTH in many implementations MARS consists of two phases: the forward and the backward pass The forward pass starts with a model consists of the mean of the response values and then repeatedly adds basis function in pairs to the model At each step it finds the pair of basis functions that gives the maximum reduction in sum-of-squares residual error The two basis functions in the pair are identical except that a different side of a mirrored hinge function is used for each function Each new basis function consists of a term already in the model multiplied by a new hinge function This process of adding terms continues until the change in residual error is too small to continue or until the maximum number of terms is reached (Friedman, Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx 1993) The backward pass removes terms one by one, deleting the least effective term at each step until it finds the best sub-model MARS models are more flexible than linear regression models PCR is a regression method that considers regressing the dependent variable on a set of independent variables based on a standard linear regression model, but uses PCA for estimating the unknown regression coefficients in the model (Jolliffe, 1982) Instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regressors PCR is some kind of a regularized procedure The principal components with the higher variances are selected as the regressors The major use of PCR lies in overcoming the multicollinearity problem which arises when two or more of the explanatory variables are close to being collinear (Dodge, 2003) PCR can result in dimension reduction through substantially lowering the effective number of parameters characterizing the underlying model PCR can lead to efficient prediction with the appropriate selection of the principal components to be used for regression ANN is a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs (Caudill, 1987) ANNs are processing algorithms that are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales Neural networks are typically organized in layers Layers are made up of a number of interconnected ‘nodes’ which contain an ‘activation function’ Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’ The hidden layers then link to an ‘output layer’ where the answer is the output Most ANNs contain some form of ’learning rule’ which modifies the weights of the connections according to the input patterns ANNs provide an analytical alternative to conventional techniques which are often limited by strict assumptions of normality, linearity, and variable independence Applications of NIRS in soil sciences As mentioned above, Near-infrared reflectance spectroscopy (NIRS) has been used to predict several soil properties There are many authors focuses their works on predicting soil characteristics based on Near-infrared reflectance spectroscopy, some of them selected one parameter of soil with reflectance spectroscopy Furthermore other authors predicted soil parameters as relation with reflectance spectroscopy (Zbíra et al., 2016) Some applications will be discussed as follows: 5.1 Soil salinity Salinization is an overall issue that influences the physical and chemical soil properties that leads to loss in yield efficiency Throughout the previous two decades, remotely detected symbolism has exhibited its capacity to evaluate saltiness changes at different scales (Metternicht and Zinck, 2008; Elnaggar and Noller, 2009) Numerous studies have illustrated the ability of Vis-NIR reflection spectroscopy bands – from the optical sensors – for detecting surface soil salinity Furthermore, hyperspectral data have been used in several approaches for quantitative assessment of soil salinity and different soil properties (Dehaan and Taylor, 2003; Farifteh et al., 2008; Feyziyev et al., 2016) It has been showen that, effective prediction of saltiness is administered by the relationship between other soil properties, such as soil moisture (Ben-Dor et al., 2002) For multispectral image studies, the inclusion of topographic data is sometimes used to mitigate the poor diagnostic power of the sensor and improve the classification For example, a study utilized Landsat Thematic Mapper (TM) data and Digital elevation model (DEM) obtained topographical indices for mapping soil salinity in Western Australian (Caccetta et al., 2000) Furthermore, hyperspectral data increase the capability of remotely sensed information, thereby, can be applied more independently of other data sets The absence of spectral features of salt still makes classification difficult However, several researchers have concluded that soil salinity can be mapped based on other properties of soil as alternatives Ben-Dor et al (2002) reported that, hyperspectral scanner data was used for mapping soil salinity, also there was a correlation between soil moisture and salinity reached (r = 0.58) in cultivated crops and was able to develop reliable prediction equations Moreover, hyperspectral remote sensing data have been utilized to monitor soil salinity under different environmental conditions, as well as other halophyte species such as Sea Blite and Sea Barley Grass (Dehaan and Taylor, 2003) Nawar et al (2014) coupled MARS, PLSR and NIR soil spectra and geostatistics to map spatial variation of soil salinity in El-Tina Plain, north Sinai, Egypt They measured electrical conductivity (ECe) data and eflectance spectra of soil samples resampled to satellite sensor’s resolution (Fig 5) The study reported good results for the prediction of soil salinity; MARS (R2 = 0.73), RMSE = 6.53, and ratio of performance to deviation (RPD) = 1.96), while PLSR model (R2 = 0.70, RMSE = 6.95, and RPD = 1.82).Moreover, the authors emphasized that MARS gives very good results for prediction of soil salinity, especially under high salinity levels Thus, it is important to monitor and map soil salinity at an early stage to enact effective soil reclamation program that helps to lessen or prevent future increase in soil salinity Remote sensing has more informative and professional rapid assessment of soil salinity, compared with traditional methods offering more informative and professional rapid assessment techniques for monitoring and mapping soil salinity Soil salinity can be identified from remote sensing data obtained by different sensors based on visible direct indicators that refer to salt features at soil surface indicators, such as the presence of halophytic plant 5.2 Soil moisture Previous numerous studies have shown the role of reflectance spectroscopy for monitoring soil moisture Many studies illustrated the inverse relationship between soil moisture and spectral reflectance (Post et al., 2000; Galvao et al., 2001) Furthermore, the inverse relationship means the decrease of reflectance with the increase of soil moisture content This relationship is due to two reasons; soil particles covered with thin films of water and water on the lattice sites of some minerals present in the soil (Stoner and Baumgardner, 1981) With the improvement of measurement tools, the change in spectral reflectance with change in soil moisture levels became more pronounced at longer wavelengths (>1450 nm) (Weidong et al., 2002) The same study also showed that, at higher moisture contents the trend is changed and the reflectance increased with the increasing of moisture content They determined this type of reversal to be somewhere around field capacity, while it changed for different soils, and happens before the point where water retention is saturating the reflectance signal Bogrekci and Lee (2006) investigated the possibility of estimating phosphorus by spectral reflectance under the influence of different levels of soil moisture with different phosphorus (P) concentrations (0, 12.5, 62.5, 175, 375, 750, and 1000 mg kg1) using ultraviolet (UV), visible (VIS), and near-infrared (NIR) absorbance spectroscopy (Fig 6) The authors illustrated that the moisture content affected the absorbance spectra, where correlation coefficient between spectra absorbance and P concentrations Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig Average Spectral reflectance at different level of soil moisture (Source: Bogrekci and Lee, 2006) Fig Correlation coefficient spectra between absorbance and P concentration at different moisture contents within the 225–2550 nm range (Source: Bogrekci and Lee, 2006) showed high values within the 1982–2550 nm range In addition, spectral signal processing by removing the moisture content effect enhanced P prediction in soils considerably (Fig 7) The study of any soil property is related to the understanding of sensitive areas at the spectrum due to presence of water The vibrational frequencies of water molecules after 2500 nm affect the water absorption wavelengths (Baumgardner et al., 1985) The 1450 and 1950 nm wavelengths are the absorption bands with sharp peaks (Fig 8) The broad unordered bands are more common in naturally occurring soils in addition, the highest significant variable in determining the reflectance located within a range 2080– 2320 mm (Baumgardner et al., 1985 and Galvao et al., 2001) The broad unordered bands are more common in naturally occurring soils Furthermore, the highest significant variable in determining the reflectance changes in the 2080–2320 mm However, other studies emphasized on the importance role of reflectance Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig Atmospheric water absorption bands spectroscopy and remote sensing to develop spectral models for detecting soil moisture content (Ben-Dor et al., 2002; Whiting et al., 2004) 5.3 Soil organic carbon Soil organic carbon (SOC) is a key characteristic of soil quality which impacts the assortment of organic compounds and physical properties of soils (Carter, 2002) The evaluation of greenhouse gas emissions from soils requires a precise information on the fate of carbon and nitrogen in soils Near-infrared reflectance spectroscopy (NIRS) is a quick and non-damaging explanatory procedure that includes diffuse-reflectance estimation in the near infrared region (1000–2500 nm) Visible–NIR spectroscopy with decision-tree modeling can fairly and accurately, with small to moderate uncertainty, predict soil organic carbon (ViscarraRossel and Hicks, 2015 and Hu et al., 2015) Soil organic matter (SOM) decrease the vis-NIR spectral reflectance range (520– 800 nm), especially if the SOM content is bigger than 2% (Stoner and Baumgardner, 1981; Henderson et al., 1992) Humic acid considered the most dark pigment of SOM and reduces the spectral reflectance over the visible to short-wave spectral range Otherwise, fulvic acid has no influence on soil reflectance (Henderson et al., 1992) A study of soils in Thailand using artificial neural networks found that vis-NIR VNIR spectrum (400–1100 nm) as a precise detector of SOM (R2 = 0.86) (Daniel et al., 2003) Furthermore, other study (Ben-Dor et al., 2002) has used hyperspectral images for mapping SOM based on the reflectance spectra of heavy clay soils in Israel where the root mean square of the prediction equations was (R2 m > 0.82) A support vector machine regression (SVMR) and a successive projections algorithm (SPA) model (SPASVMR model) have been used for improving the accuracy of soil organic carbon (SOC) which has resulted from integrating the laboratory-based visible and near-infrared (VIS/NIR, 350– 2500 nm) spectroscopy of soils (Xiaoting et al., 2014) Another image study used digitized color aerial photography to successfully map SOM based on two approaches The first attempt was to study the individual pixels thereby describe the spatial distribution; the second attempt was applying the relationship on image classification to determine the classes units (Fig 9) 5.4 Clay minerals Soil chemistry affects clay minerals, thereby the soil development and their fertility Many of clay minerals have unique spectral reflectance at visible wavelengths and NIR-SWIR (Hunt, 1980) Silva et al (2016) illustrated that near-infrared region can be used to predicate soil attribute with PLSR using a limited spectral region (325–1075 nm) performed poorly for sand while more promising when considering the capabilities to predict silt and clay The application of visible and part of the (400–980 nm) for clay prediction in Oxisols achieved relative good results where regression coefficients showed good relation to the spectral behavior of weathered soils in visible and near-infrared region The components of soil minerals affect the spectral reflectance of the soil through the absorption bands and overall spectral brightness Quartz is the biggest and most regular part of soils; it shows no unique absorption feature over Vis-NIR-SWIR range although it does increase the overall brightness Clay minerals have unique absorption bands that are effected by distinctive vibrational overtones, electronic and charge transfers, and conduction processes (Clark, 1999) The wavelengths around 2200 nm for the spectral characteristics of clay minerals were extracted from AVIRIS data for the identification of smectite, kaolinite and illite clay minerals (Chabrillat et al., 2002) The alteration phases were mapped based on absorption band position, depth and asymmetry from AVIRIS data (van-der-Meer, 2004) As vegetation obscure the target material partially by the large distinctive absorption features, the Mineralogical identification achieved features (Chabrillat et al., 2002) Similarly, absorption band position, depth and asymmetry have been used to map alteration phases with AVIRIS imagery (van-der-Meer, 2004) Mineralogical identification has been achieved when the target material is partially obscured by vegetation due the largely distinctive absorption features (Chabrillat et al., 2002) In the spectrum of hematite (an iron-oxide mineral), the strong absorption in the visible light range is caused by ferric iron (Fe+3) In calcite, the major component of limestone, the carbonate ion (CO=3) is responsible for a series of absorption bands between 1.8 and 2.4 mm (mm) The most common clay minerals in soil are kaolinite and montmorillonite, these minerals are distinct from others depending on the absorption spectroscopy bands where the highest absorption band around 1.4 mm Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig Spatial distribution of soil organic carbon (Source: Chen et al., 2000) in wavelength, along with the weak 1.9 mm band in kaolinite, referring to hydroxide ions (OH-), while the stronger 1.9 mm band in montmorillonite is affected by bound water molecules in this hydrous clay (Fig 10) On the other hand, feldspar, the dominant mineral in granite – shows no significant absorption features in the vis-NIR-SWIR (Hauff et al., 1991; Masinter and Lyon, 1991) The combination of spectroscopy reflectance data and hyperspectral satellite images give remarkable results for deriving dominant clay mineral The results from modeling dominant clay minerals by random forests and mapping of hyperion data using Spectral Angular Mapper (SAM) illustrated the dominance of kaolinite clay mineral followed by montmorillonite in Madhya Pradesh India (Fig 11) (Janaki et al., 2014) 5.5 Soil surface features The identification of surface soil features and land resources are very important for precise management in different scales The spectral signature of each soil property influenced by spatial and temporal variability of surface processes however, it is difficult to measure directly from their reflectance spectra even under controlled laboratory conditions (Silva and ten Caten, 2016) Soil Vis-NIR (350–2500 nm) reflectance spectra contain valuable information for predicting soil textural fractions (sand, silt, and clay content) Chemometrics techniques and multivariate calibration (PLSR) allowed researchers to extract the relevant information from the reflectance spectra and to correlate this with the soil Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 10 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig 10 Spectral reflectance of different clay minerals (Source: Clark, 1999) Fig 11 Dominant clay mineral in Madhya Pradesh India (Source: Janaki et al., 2014) texture fractions The same author illustrated that soil texture can be predicted where sand content (R2 = 0.81) and clay content (R2 = 0.80) and less satisfactory for silt content (R2 = 0.70).The spectral signature from an image pixel is a mixture ofsurface materials affected by their chemical components The spectral properties of a single image pixel is the representation of the surface components Each pixel retains the characteristic features of the individual spectra from each of the component reflective materials When the ground material- such as soil types – occupies the whole pixel, the pixel spectra is the signatures of the ground material (Roberts et al., 1993) Saleh et al (2013) used a linear spectral unmixing analyses to discriminate different surface soil types in north sinia – Egypt by Near-infrared reflectance spectroscopy techniques (Fig 12) The spectra of the soil types were significantly influenced by the different surface features presented in the area The same author concluded that linear spectral unmixing is very helpful tool for identifying and mapping the different surface soil types from ETM + by discriminating the different mixture spectra Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx 11 Fig 12 Map of surface soil types using unmixing spectra model with field spectral data (Source: Saleh et al., 2013) of the image into distinct soil types spectra Furthermore, linear spectral unmixing model allowed all the bands in the ETM+ image to be used, it has been proven to be reasonable in mapping soil surface features The spectral signature from an image pixel is a mixture of surface materials and it affected by their chemical components the spectral properties of a single image pixel is the representation of the mixed spectra of the surface components Each pixel retains the characteristic features of the individual spectra from each of the component reflective materials When the ground material- such as soil types – occupies the whole pixel, the pixel spectra is the signatures of the ground material (Roberts et al., 1993) Saleh et al (2013) used a linear spectral unmixing analyses to discriminate the different surface soil types in north sinia – Egypt by Near-infrared reflectance spectroscopy techniques The spectra of the soil types were significantly influenced by the different surface features presented in the area The author concluded that linear spectral unmixing is very helpful tool for identifying and mapping the different surface soil types from ETM+ by discriminating the different mixture spectra of the image into distinct soil types spectra Furthermore, linear spectral unmix- ing model allowed all the bands in the ETM+ image to be used, it has been proven to be reasonable in mapping soil surface features The authors recommended that further research is needed to evaluate the spectral unmixing technique for different soil types and different image types 5.6 Soil contamination Soil contamination by heavy metals is considered the main environmental problem, most of them have toxic effects on plant and microorganisms in soil when allowing increase of their concentration levels (Mohamed et al., 2016) Conventional methods for measuring heavy metal such as inductively coupled plasma (ICP), atomic absorption spectrometry (AAS) or operationally defined sequential extraction can estimate physico-chemical data directly, but take a long time and are very expensive Comparing furthermore, the spectral analyses techniques can be used in a wide range Diffuse reflectance spectroscopy (DRS) in visible-near infrared (VNIR) region (400–2500 nm) has been used to quickly analyse soil characteristics both appropriately and precisely Fig 13 The areas affected by concentration of heavy elements (Source; Mohamed et al., 2016) Please cite this article in press as: Mohamed, E.S., et al Egypt J Remote Sensing Space Sci (2017), http://dx.doi.org/10.1016/j.ejrs.2017.02.001 12 E.S Mohamed et al / Egypt J Remote Sensing Space Sci xxx (2017) xxx–xxx Fig 14 Spatial distribution of selected heavy metals (Source; Mohamed et al., 2016) through VNIR DRS, soil constituents such as iron oxides (Madeira et al., 1997; Ji et al., 2002) Heavy metals, often absorbed or bounded, are characterized by spectrally active constituents based on the environmental condition which make it possible to investigate their characteristics using VNIR- DRS Previous works showed the availability for predicting of heavy metals in soil by spectroscopic reflectance (Wu et al., 2005; Xia et al., 2007; Moros et al., 2009) On the other hand, heavy metals such Cr, Cu, Zn and As have negative correlation coefficients with the spectral bands which attributable to the absorption features of iron oxides, clay, and organic matter, suggesting they are strongly bound to these soil constitutes Significant relationship between Pb, Cd, and Hg and TOC spectral bands was observed That indicates the important organic matter binding for these elements (Junfeng et al., 2010) Moreover, the best relationship between Pb-Zn-Mn and ratio of 610/500 nm range while Ni-Cr have the highest correlation that associated with slope in range of 980 nm, however the authors indicated that spectral parameters and reflectance values for Mn, Pb and Zn within 400–2500 nm range have a better prediction ability for contaminated soil other than for Cr and Ni but not suitable for Fe, Cu, Cd, EC and pH (McCarty et al., 2002; Vodyanitskii, 2013) Mohamed et al (2016) investigated the capability of vis-NIR (350– 2500 nm) for calibration procedures and predicted contaminated soil in the area closed to Bahr El-Baqar, east of Nile Delta The authors used stepwise multiple linear regression (SMLR) to develop calibration models for Cr, Mn and Cu The most affected regions on spectral reflectance were 2010, and 2149 nm for copper also, 2139 and 2072 nm for manganese as shown in (Fig 13) The concentrations of heavy metals were estimated with high accuracy where, R2 was recorded 0.82, 0.75 and 0.65 for Cr, Mn and Cu, respectively In addition, the authors highlighted the environmental hazards occurred along with Bahr El-Baqar drain where the spatial distribution maps of heavy metals were produced based on reflectance spectroscopy data Moreover, the results showed an increase in both Mn and Cu concentration towards the north of the studied area while, an increase in concentration of Cr in the areas located near Fakous city as showen in (Fig 14) Conclusion Under the current situations, the conventional methods of soil analyses takes a long time, in addition to their expensive costs Near-infrared reflectance spectroscopy, as advanced tools may manage to overcome previous obstacles This article addresses the fundamental concepts of spectroscopic reflectance techniques and some applications on soil sciences It also explores the capability of Near-infrared reflectance spectroscopy for detecting and mapping soils characteristics The main limiting factor in assessment of the soil properties is finding certain data pre-treatment and calibration procedures, where, correlation between soil reflectance data and values of each soil properties could be achieved Quantitative prediction of soil properties (e.r salinity, organic carbon, soil moisture and heavy metals) can be conducted using various calibration models which were developed depending on the measured soil analysis and soil reflectance spectra then resampled to satellite images to predict soil properties moreover good predictions for some chemical, physical and biological properties can be achieved based on laboratory analysis and NIRS data The integration of different remote sensing data and NIRS can be maximized the ability to cover and investigate large surfaces in a single flight campaign and thus 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professional rapid assessment of soil salinity, compared with traditional methods offering more informative and professional rapid assessment. .. Meshalkina, Y.L., 2011 Quantitative method of comparing soil maps and schematic maps Eurasian Soil Sci 3, 3–5 Savin, I., 1993 Formation of reflectance properties of tilled chernozem soil? ??s surface //... factor in assessment of the soil properties is finding certain data pre-treatment and calibration procedures, where, correlation between soil reflectance data and values of each soil properties