{"id":48,"date":"2019-03-08T05:36:51","date_gmt":"2019-03-08T05:36:51","guid":{"rendered":"http:\/\/esp06.epgpbooks.inflibnet.ac.in\/?post_type=chapter&p=48"},"modified":"2019-03-08T05:59:29","modified_gmt":"2019-03-08T05:59:29","slug":"spectral-reflectance","status":"publish","type":"chapter","link":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/chapter\/spectral-reflectance\/","title":{"rendered":"Spectral Reflectance"},"content":{"raw":"
\r\n\r\n1. Learning Objectives<\/strong>\r\n\r\n \r\n

This module will help us conceptualize the principle of spectral reflectance and understand its significance in studying various earth surface features.<\/p>\r\n \r\n\r\n2. Energy Interactions with Earth Surface Features<\/strong>\r\n\r\n \r\n

When electromagnetic energy is incident on any feature of the earth\u2019s surface feature, any of the three interactions, viz. reflection, absorption or transmission, may occur. Thus, at any given wavelength for incident energy, the relationship among these interactions can be represented by the following equation (1) based on the principle of conservation of energy:<\/p>\r\n \r\n\r\nE1 (\u03bb) =ER (\u03bb)+EA (\u03bb)+ET (\u03bb)\u00a0 \u00a0\u2026\u2026.\u00a0\u00a0\u00a0\u00a0 (Equation 1)\r\n\r\n \r\n\r\nWhere,\r\n\r\n \r\n\r\nE1 = incident energy\r\n\r\n \r\n\r\nER= reflected energy\r\n\r\n \r\n\r\nEA = absorbed energy\r\n\r\n \r\n\r\nET = transmitted energy\r\n\r\n \r\n

Figure 1 reveals the interaction between these energy components at a given wavelength.<\/p>\r\n\"\"\r\n\r\n \r\n

Figure 1: Interactions between electromagnetic energy and Earth surface feature<\/p>\r\n\r\n<\/div>\r\n

<\/div>\r\n
From equation 1, it becomes imperative to understand that for different earth features, the proportion of energy reflected, absorbed and transmitted varies, depending on the nature of the material. Further, even for a given feature type, the proportion of reflected, absorbed and transmitted energy will vary at different wavelengths.<\/span><\/div>\r\n
\r\n\r\n \r\n

Since most of the remote sensing systems operate based on the reflected energy, the reflectance properties of the feature become very important for studying earth\u2019s surface features. Thus, the energy balance relationship expressed in Equation 1 can be represented as:<\/p>\r\n \r\n\r\nER(\u03bb)=E1(\u03bb)-[EA (\u03bb)+ET (\u03bb)]-----------------(2)\r\n\r\n \r\n

This implies that the reflected energy at any given wavelength is equal to the energy incident on a given feature reduced by the energy that is either absorbed or transmitted by that feature.<\/p>\r\n \r\n\r\n3. Spectral Reflectance<\/strong>\r\n\r\n \r\n

The quantitative measure of the reflectance characteristics of the Earth surface features is the portion of incident energy that is reflected, measured as a function of wavelength; and is called spectral reflectance, \u03c1\u03bb. It is mathematically defined as<\/p>\r\n \r\n\r\n\u03c1\u03bb =ER (\u03bb)\/E1 (\u03bb)\r\n\r\n=<\/span>?????? ?? ?????????\u210e \u03bb reflected from the object\/<\/span>?????? ?? ?????????\u210e \u03bb incident upon the object<\/span>\u00d7 100\u00a0<\/span>\r\n\r\n \r\n\r\nWhere, \u03c1\u03bb is expressed as a percentage.\r\n\r\n \r\n

A graph of the spectral reflectance of an object as a function of wavelength is termed as spectral reflectance curve. This varies with the variation in the chemical composition and physical conditions of the feature. The spectral response patterns for an object are averaged to get a generalized form, which is called as generalized spectral response pattern. If a spectral response pattern is unique to an object or an earth\u2019s feature, it is termed as \u2018Spectral signature\u2019. Because spectral responses measured by remote sensors over various features often permit an assessment of the type and\/or condition of the features, these responses have often been referred to as spectral signatures. However, the term has now\u00a0become obsolete, and spectral reflectance is only used to characterize a feature. The importance of spectral reflectance curve lies in the fact that it gives us an insight into the spectral characteristics of the object under consideration. This can be illustrated by the examples of spectral reflectance curves of the following earth\u2019s surface features.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n \r\n\r\n3.1 Vegetation<\/strong>\r\n\r\n \r\n

All the green plants contain the pigment chlorophyll, which absorbs electromagnetic radiations greatly in the visible region. As a result, for healthy green vegetation, spectral reflectance curve exhibits the \"peak-and-valley\" configuration (Fig. 2). The peaks indicate predominant reflection and the valleys indicate dip in reflectance due to strong absorption of the energy in the corresponding wavelength bands.<\/p>\r\n \r\n

Figure 2: Spectral reflectance curve for vegetation, soil and water (http:\/\/www.seosproject.eu\/modules\/remotesensing\/remotesensing-c01-p05.html)<\/em><\/p>\r\n\"\" \"\"\r\n\r\n \r\n

Further, the spectral response of vegetation depends on the structure of the plant leaves. Fig. 2 shows the cell structure of a green leaf and the interaction with the electromagnetic radiation (Gibson 2000). In a plant leaf, the palisade cells containing chlorophyll pigment strongly absorb energy in the wavelength bands centered at 0.45 and 0.67 \u03bcm corresponding to blue and red wavelengths within\u00a0visible region (Fig. 3). This dip in reflectance is known as \u2018chlorophyll absorption bands\u2019. Also, this reflection is highest for the green colour in the visible region, due to which our eyes perceive healthy vegetation to be green in colour.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n\"\"\r\n\r\nFigure 3: Cell structure of a green leaf and interactions with the electromagnetic radiation (Gibson, 2000)\r\n\r\n \r\n

As we move from visible region of electromagnetic spectrum to infrared region; at 0.7 \u03bcm, the absorption reduces while reflection greatly increases. Then this reflectance is nearly constant from 0.7-1.3 \u03bcm where plant leaf reflects about 50 percent of the energy incident upon it. Most of the remaining energy is transmitted, since absorption in the spectral region is minimal (less than 5%). Plant reflectance in the range of 0.7 to 1.3 \u00b5m results primarily from the internal structure of plant. The infrared radiation penetrates the palisade cells and reaches the irregularly packed mesophyll cells which make up the body of the leaf. Mesophyll cells reflect almost 60% of the NIR radiation reaching this layer. Most of the remaining energy is transmitted, since absorption in this spectral region is minimal. Healthy vegetation therefore shows brighter response in the NIR region compared to the green region.<\/p>\r\n \r\n

Beyond 1.3 \u00b5m, energy incident upon the vegetation is essentially absorbed or reflected, with little to no transmittance of energy. Dips in the reflectance occur at the 1.4, 1.9 and 2.7 \u00b5m because water in the leaf absorbs strongly at these wavelengths. Accordingly, wavelengths in these spectral regions are referred to as \u2018water absorption bands\u2019. Reflectance peaks occur at about 1.6 and 2.2 \u00b5m, between the\u00a0absorption bands. Throughout the range beyond 1.3 \u00b5m, leaf reflectance is approximately inversely related to the total water present in a leaf. This total is function of both the moisture content and the thickness of a leaf.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n \r\n

Due to high degree of variability between plant species, the significance of reflectance measurements lies in the fact that these measurements help to distinguish between visually similar species of visible wavelengths. Reflectance also acts as an indicator for detecting vegetation stress (Figure 4). For example, if a plant is subjected to some form of stress that interrupts its normal growth and productivity; it may decrease chlorophyll production. This results in lesser absorption in the blue and red bands in the palisade layer. As a result, red and blue bands also get reflected along with the green band, giving yellow or brown colour to the stressed vegetation.<\/p>\r\n\"\"\r\n

Figure 4: Spectral reflectance curve of various land features (https:\/\/gis.stackexchange.com\/questions\/101392\/determining-which-color-each-image-band-represents)<\/em><\/p>\r\n \r\n

Further, in stressed vegetation, the NIR bands are no longer reflected by the mesophyll cells, instead they are absorbed by the stressed or dead cells causing dark tones in the image. Also, multiple layers of leaves in a plant canopy provide the opportunity for multiple transmittance and reflectance. Hence, the near-IR reflectance increases with the number of layers of leaves in a canopy, with the reflection maximum achieved at about eight leaf layers (Bauer et., 1986).<\/p>\r\n \r\n

Another example illustrating the importance of spectral reflectance curve is the application in forestry to distinguish between deciduous versus coniferous trees (Figure 5). It is pertinent to mention that the reflectance curve for these trees is plotted as a ribbon of values, not as a single line to include a broad range of reflectance values, rather than a discrete value for each tree.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n\"\"\r\n

Figure 5: Generalized spectral reflectance envelopes for deciduous and coniferous trees (sar.kangwon.ac.kr<\/em>)<\/p>\r\n \r\n

From F igure 5, it is clear that if we try to discriminate the two types of tress in visible bands, it would be a bit difficult since the spectral reflectance curves for the two tree types overlap in the visible region. As a result, both the tree types would appear green in colour and would exhibit similar reflectance. Although this difficulty can be overcome partially by using clues such as size and shape of canopy; however, it would not be possible to give absolute results. However, if we have a sensor that can detect the reflectance of the vegetation in infrared bands, this would solve our purpose. Since, deciduous trees have broad leaves compared to needle shaped leaves of conifers; they show much higher reflectance in infrared wavelength and thus, appear much lighter in tone than conifers. Thus, based on spectral characteristics, various Earth surface features can be identified and mapped.<\/p>\r\n \r\n\r\n3.2 Soil<\/strong>\r\n\r\n \r\n

The reflectance curve for soil in figure 4 shows considerably less peak and valley variation. The factors that affect soil reflectance are moisture content, organic matter content, soil texture (proportion of sand, silt and clay), surface roughness and presence of iron oxide. The presence of moisture in soil\u00a0decreases its reflectance due to the presence of water absorption bands at 1.4, 1.9 and 2.7 \u00b5m. Clay soil also has hydroxyl absorption bands at 1.4 and 2.2 \u00b5m. There also exists close relation with soil moisture content and soil texture; for example, coarse, sandy soils are usually well drained, resulting in low moisture content and relatively high reflectance; while, poorly drained fine-textured soils will generally have lower reflectance. Besides, soil reflectance is also reduced by surface roughness, presence of organic matter content and iron oxide. It is pertinent to mention here that soil reflectance comes from the uppermost layer of the soil, and is not be indicative of the properties of the bulk of the soil.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n \r\n\r\n3.3 Water<\/strong>\r\n\r\n \r\n

Clear water absorbs relatively little energy having wavelengths less than about 0.6 \u00b5m. As a result of high transmittance, water generally appears blue. An important characteristic of spectral reflectance of water is its complete absorption at near-IR wavelengths and beyond. As a result, a water body always appears dark when studied in infrared wavelengths. This is the reason that locating and delineating water bodies with remote sensing data is done mostly in near IR wavelengths.<\/p>\r\n \r\n

However, various conditions of water bodies manifest themselves primarily in visible wavelengths such as the presence of organic as well as inorganic materials in water. For example, highly turbid water containing large quantities of suspended sediments has much higher visible reflectance. Similarly, the presence of chlorophyll pigment as a result of algal growth decreases the reflectance, a fact used for studying the eutrophication status of water body using remote sensing techniques. Similarly, spectral reflectance is aslo used for determining the presence of tannin dyes, industrial wastes discharges and various pollutants that alter the reflectance.<\/p>\r\n \r\n\r\n3.4 Snow<\/strong>\r\n\r\n \r\n

Snow reflects strongly in the visible and near infrared and absorbs more energy at mid-IR wavelengths that is characterized by a dip in spectral reflectance. However, the reflectance of snow is affected by its grain size, liquid water content and presence or absence of other materials (Dozier and Painter, 2004). Larger grains of snow absorb more energy, particularly at wavelengths longer than 0.8 \u00b5m. At temperature near 0\u00b0C, liquid water within the snow pack can cause grains to stick together in clusters,\u00a0thus increasing the effective grain size and decreasing the reflectance at near IR and longer wavelengths. When particles of contaminants such as dust or soot are deposited on snow, they can significantly reduce the surface\u2019s reflectance in the visible spectrum.<\/span><\/p>\r\n\r\n<\/div>\r\n

\r\n\r\n\"\"\r\n

Figure 6: Spectral reflectance of snow and clouds (http:\/\/www.geol-amu.org\/notes\/m1r-1-8.htm)<\/p>\r\n \r\n

From Figure 6, it can be inferred that the absorption of mid-IR wavelengths by snow can permit the differentiation between snow and clouds. While both feature types appear bright in the visible and near IR, clouds have significantly higher reflectance than snow at wavelengths longer than 1.4 \u00b5m.<\/p>\r\n \r\n\r\n3.5 Asphalt<\/strong>\r\n\r\n \r\n\r\nSand can have a wide variation in its spectral reflectance pattern depending on its parent material.\r\n\r\nBesides, presence or absence of water and organic matter also affect the spectral response of sand.\r\n\r\n<\/div>\r\n \r\n\r\n\"\"\r\n

\r\n

Figure 7: Spectral reflectance curve of various land features (http:\/\/slideplayer.com\/slide\/8259758\/)<\/p>\r\n \r\n

As shown in the figure 7, the spectral reflectance curves for asphalt is much flatter than those of other land cover features; however, its reflectance may be modified by the presence of paint, soot, or water Besides, ageing also has an effect on spectral reflectance o asphalt. The reflectance of asphaltic concrete increases on ageing, particularly, in the visible spectrum.<\/p>\r\n \r\n\r\n4. Summary<\/strong>\r\n\r\n \r\n

Having studied the spectral reflectance curve of various land features, it would not be wrong to conclude that these features can be separated spectrally based on their properties of absorption, reflectance and transmittance. This spectral variability study can also be affected by temporal and spatial effects. Temporal effects are those factors that change the spectral characteristics of a feature over time. For example, the spectral characteristics of many vegetation species are in a nearly continual state of change throughout a growing season. Spatial effects refer to the factors that cause the same types of features at a given point in time to have different characteristics at different geographic locations. For example, analysing a crop pattern varies if the analysis is carried out on a small scale or a large regional scale where entirely different soils, climates and cultivation practices might exist for the same crop. Another example could be analysing spectral variability for a diseased\u00a0versus healthy vegetation. However, these effects are extremely helpful while carrying out change detection studies over a given area based on changes in temporal effects.<\/span><\/p>\r\n\r\n<\/div>\r\n \r\n

In addition to being influenced by temporal and spatial effects, spectral response patterns are influenced by the atmosphere between sensor and the ground. Thus, it is advisable to carry out ground truth studies as a helping aid while studying spectral reflectance pattern for various earth features.<\/p>\r\n\r\n

\r\n\r\n \r\n\r\nBibliography \/ Further Reading<\/strong>\r\n\r\n \r\n
    \r\n \t
  • American Society of Photogrammetry (1975) \u201cManual of Remote Sensing\u201d, Falls Church, Va.<\/li>\r\n \t
  • Avery, T.E., and G.L. Berlin, Fundamentals of Remote Sensing and Airphoto Interpretation, Macmillan, New York, 1992.<\/li>\r\n \t
  • Bauer,\u00a0 M.E.,\u00a0 et\u00a0 al.,\u00a0 \u201cField\u00a0 Spectroscopy\u00a0 of\u00a0 Agricultural\u00a0 Crops,\u201d\u00a0 IEEE\u00a0 Transactions\u00a0 on<\/li>\r\n \t
  • Geosciences and Remote Sensing, vol. GE-24, no. 1, 1986, pp. 65-75.<\/li>\r\n \t
  • Bowker, D.E., et al., Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, National Aeronautics and Space Administration, Washington, 1985.<\/li>\r\n \t
  • Campbell, G.S., and J.M. Norman, An Introduction to Environmental Biophysics, 2nd ed., Springer, New York, 1997.<\/li>\r\n \t
  • Campbell, J.B., Introduction to Remote Sensing, 3rd ed., Guilford Press, New York, 2002. Colwell, R.N. (Ed.) 1983. Manual of Remote Sensing. Second Edition. Vol I: Theory,<\/li>\r\n \t
  • Curran, P.J. 1985. Principles of Remote Sensing. Longman Group Limited, London.<\/li>\r\n \t
  • Dozier, J., and T.H. Painter, \u201cMultispectral and Hyperspectral Remote Sensing of Alpine Snow Properties,\u201d Annual Review of Earth and Planetary Sciences, vol. 32, 2004, pp. 465-494.<\/li>\r\n \t
  • Elachi, C. 1987. Introduction to the Physics and Techniques of Remote Sensing. Wiley Series in Remote Sensing, New York.<\/li>\r\n \t
  • Elachi, C., Introduction to the Physics and Techniques of Remote Sensing, Wiley, New York, 1987.<\/li>\r\n \t
  • Gibson P.J (2000) \u201cIntroductory Remote Sensing- Principles and Concepts\u201d Routledge, London.<\/li>\r\n \t
  • http:\/\/civil.iisc.ernet.in\/~nagesh\/rs_docs\/02_SpectralReflectanceCurves_bw.pdf http:\/\/coldregionsresearch.tpub.com\/rsmnl\/rsmnl0034.htm<\/li>\r\n \t
  • http:\/\/www.ccrs.nrcan.gc.ca\/ccrs\/learn\/tutorials\/fundam\/chapter1\/chapter1_1_e.html http:\/\/www.geol-amu.org\/notes\/m1r-1-8.htm<\/li>\r\n \t
  • http:\/\/www.seos-project.eu\/modules\/remotesensing\/remotesensing-c01-p05.html http:\/\/www.wamis.org\/agm\/pubs\/agm8\/Paper-2.pdf<\/li>\r\n \t
  • https:\/\/gis.stackexchange.com\/questions\/101392\/determining-which-color-each-image-band-represents<\/li>\r\n \t
  • https:\/\/www.intechopen.com\/books\/biomass-and-remote-sensing-of-biomass\/introduction-to-remote-sensing-of-biomass<\/li>\r\n \t
  • Instruments and Techniques. American Society of Photogrammetry and Remote Sensing. ASPRS, Falls Church.<\/li>\r\n \t
  • Jet Propulsion Laboratory, ASTER Spectral Library, California Institute of Technology, Pasadena, 1999.<\/li>\r\n \t
  • Joseph, G. 1996. Imaging Sensors. Remote Sensing Reviews, 13: 257-342.<\/li>\r\n \t
  • Jupp, D.L.B., and A.H. Strahler, \u201cA Hotspot\u00a0 Model for Leaf Canopies, \u201cRemote Sensing of<\/li>\r\n \t
  • Environment, vol.38, 1991, pp. 193-210.<\/li>\r\n \t
  • Kalensky,\u00a0 Z.,\u00a0 and\u00a0 D.A.\u00a0 Wilson,\u00a0 \u201cSpectral\u00a0 Signatures\u00a0 of\u00a0 Forest\u00a0 Trees,\u201dProceedings:\u00a0 Third<\/li>\r\n \t
  • Canadian Symposium on Remote Sensing, 1975, pp. 155-171.<\/li>\r\n \t
  • Lillesand, T. M., Kiefer, R. W., Chipman, J. W. (2004). \u201cRemote sensing and image interpretation\u201d, Wiley India (P). Ltd., New Delhi.<\/span><\/li>\r\n \t
  • Manual of Remote Sensing. IIIrd Edition. American Society of Photogrammtery and Remote Sensing.<\/span><\/li>\r\n \t
  • Nagesh Kumar D and Reshmidevi TV (2013). \u201cRemote sensing applications in water resources\u201d J.<\/span>Indian Institute of Science, 93(2), 163-188.<\/span><\/li>\r\n \t
  • Sabins, F.F. 1997. Remote Sensing and Principles and Image Interpretation. WH Freeman, New York.<\/span><\/li>\r\n \t
  • Schott, J.R., Remote Sensing: The Image Chain Approach, Oxford University Press, New York, 1997.<\/span><\/li>\r\n \t
  • Short N.M (1999). \u201cRemote Sensing Tutorial - Online Handbook\u201d, Goddard Space Flight Center,<\/span>NASA, USA.<\/span><\/li>\r\n \t
  • Swain, P.H. and S.M. Davis (eds). (1978) \u201cRemote sensing: The Quantitaive Approach\u201d, McGraw-Hill, New York.<\/span><\/li>\r\n<\/ul>\r\n<\/div>","rendered":"
    \n

    1. Learning Objectives<\/strong><\/p>\n

     <\/p>\n

    This module will help us conceptualize the principle of spectral reflectance and understand its significance in studying various earth surface features.<\/p>\n

     <\/p>\n

    2. Energy Interactions with Earth Surface Features<\/strong><\/p>\n

     <\/p>\n

    When electromagnetic energy is incident on any feature of the earth\u2019s surface feature, any of the three interactions, viz. reflection, absorption or transmission, may occur. Thus, at any given wavelength for incident energy, the relationship among these interactions can be represented by the following equation (1) based on the principle of conservation of energy:<\/p>\n

     <\/p>\n

    E1 (\u03bb) =ER (\u03bb)+EA (\u03bb)+ET (\u03bb)\u00a0 \u00a0\u2026\u2026.\u00a0\u00a0\u00a0\u00a0 (Equation 1)<\/p>\n

     <\/p>\n

    Where,<\/p>\n

     <\/p>\n

    E1 = incident energy<\/p>\n

     <\/p>\n

    ER= reflected energy<\/p>\n

     <\/p>\n

    EA = absorbed energy<\/p>\n

     <\/p>\n

    ET = transmitted energy<\/p>\n

     <\/p>\n

    Figure 1 reveals the interaction between these energy components at a given wavelength.<\/p>\n

    \"\"<\/p>\n

     <\/p>\n

    Figure 1: Interactions between electromagnetic energy and Earth surface feature<\/p>\n<\/div>\n

    <\/div>\n
    From equation 1, it becomes imperative to understand that for different earth features, the proportion of energy reflected, absorbed and transmitted varies, depending on the nature of the material. Further, even for a given feature type, the proportion of reflected, absorbed and transmitted energy will vary at different wavelengths.<\/span><\/div>\n
    \n

     <\/p>\n

    Since most of the remote sensing systems operate based on the reflected energy, the reflectance properties of the feature become very important for studying earth\u2019s surface features. Thus, the energy balance relationship expressed in Equation 1 can be represented as:<\/p>\n

     <\/p>\n

    ER(\u03bb)=E1(\u03bb)-[EA (\u03bb)+ET (\u03bb)]—————–(2)<\/p>\n

     <\/p>\n

    This implies that the reflected energy at any given wavelength is equal to the energy incident on a given feature reduced by the energy that is either absorbed or transmitted by that feature.<\/p>\n

     <\/p>\n

    3. Spectral Reflectance<\/strong><\/p>\n

     <\/p>\n

    The quantitative measure of the reflectance characteristics of the Earth surface features is the portion of incident energy that is reflected, measured as a function of wavelength; and is called spectral reflectance, \u03c1\u03bb. It is mathematically defined as<\/p>\n

     <\/p>\n

    \u03c1\u03bb =ER (\u03bb)\/E1 (\u03bb)<\/p>\n

    =<\/span>?????? ?? ?????????\u210e \u03bb reflected from the object\/<\/span>?????? ?? ?????????\u210e \u03bb incident upon the object<\/span>\u00d7 100\u00a0<\/span><\/p>\n

     <\/p>\n

    Where, \u03c1\u03bb is expressed as a percentage.<\/p>\n

     <\/p>\n

    A graph of the spectral reflectance of an object as a function of wavelength is termed as spectral reflectance curve. This varies with the variation in the chemical composition and physical conditions of the feature. The spectral response patterns for an object are averaged to get a generalized form, which is called as generalized spectral response pattern. If a spectral response pattern is unique to an object or an earth\u2019s feature, it is termed as \u2018Spectral signature\u2019. Because spectral responses measured by remote sensors over various features often permit an assessment of the type and\/or condition of the features, these responses have often been referred to as spectral signatures. However, the term has now\u00a0become obsolete, and spectral reflectance is only used to characterize a feature. The importance of spectral reflectance curve lies in the fact that it gives us an insight into the spectral characteristics of the object under consideration. This can be illustrated by the examples of spectral reflectance curves of the following earth\u2019s surface features.<\/span><\/p>\n<\/div>\n

    \n

     <\/p>\n

    3.1 Vegetation<\/strong><\/p>\n

     <\/p>\n

    All the green plants contain the pigment chlorophyll, which absorbs electromagnetic radiations greatly in the visible region. As a result, for healthy green vegetation, spectral reflectance curve exhibits the “peak-and-valley” configuration (Fig. 2). The peaks indicate predominant reflection and the valleys indicate dip in reflectance due to strong absorption of the energy in the corresponding wavelength bands.<\/p>\n

     <\/p>\n

    Figure 2: Spectral reflectance curve for vegetation, soil and water (http:\/\/www.seosproject.eu\/modules\/remotesensing\/remotesensing-c01-p05.html)<\/em><\/p>\n

    \"\" \"\"<\/p>\n

     <\/p>\n

    Further, the spectral response of vegetation depends on the structure of the plant leaves. Fig. 2 shows the cell structure of a green leaf and the interaction with the electromagnetic radiation (Gibson 2000). In a plant leaf, the palisade cells containing chlorophyll pigment strongly absorb energy in the wavelength bands centered at 0.45 and 0.67 \u03bcm corresponding to blue and red wavelengths within\u00a0visible region (Fig. 3). This dip in reflectance is known as \u2018chlorophyll absorption bands\u2019. Also, this reflection is highest for the green colour in the visible region, due to which our eyes perceive healthy vegetation to be green in colour.<\/span><\/p>\n<\/div>\n

    \n

    \"\"<\/p>\n

    Figure 3: Cell structure of a green leaf and interactions with the electromagnetic radiation (Gibson, 2000)<\/p>\n

     <\/p>\n

    As we move from visible region of electromagnetic spectrum to infrared region; at 0.7 \u03bcm, the absorption reduces while reflection greatly increases. Then this reflectance is nearly constant from 0.7-1.3 \u03bcm where plant leaf reflects about 50 percent of the energy incident upon it. Most of the remaining energy is transmitted, since absorption in the spectral region is minimal (less than 5%). Plant reflectance in the range of 0.7 to 1.3 \u00b5m results primarily from the internal structure of plant. The infrared radiation penetrates the palisade cells and reaches the irregularly packed mesophyll cells which make up the body of the leaf. Mesophyll cells reflect almost 60% of the NIR radiation reaching this layer. Most of the remaining energy is transmitted, since absorption in this spectral region is minimal. Healthy vegetation therefore shows brighter response in the NIR region compared to the green region.<\/p>\n

     <\/p>\n

    Beyond 1.3 \u00b5m, energy incident upon the vegetation is essentially absorbed or reflected, with little to no transmittance of energy. Dips in the reflectance occur at the 1.4, 1.9 and 2.7 \u00b5m because water in the leaf absorbs strongly at these wavelengths. Accordingly, wavelengths in these spectral regions are referred to as \u2018water absorption bands\u2019. Reflectance peaks occur at about 1.6 and 2.2 \u00b5m, between the\u00a0absorption bands. Throughout the range beyond 1.3 \u00b5m, leaf reflectance is approximately inversely related to the total water present in a leaf. This total is function of both the moisture content and the thickness of a leaf.<\/span><\/p>\n<\/div>\n

    \n

     <\/p>\n

    Due to high degree of variability between plant species, the significance of reflectance measurements lies in the fact that these measurements help to distinguish between visually similar species of visible wavelengths. Reflectance also acts as an indicator for detecting vegetation stress (Figure 4). For example, if a plant is subjected to some form of stress that interrupts its normal growth and productivity; it may decrease chlorophyll production. This results in lesser absorption in the blue and red bands in the palisade layer. As a result, red and blue bands also get reflected along with the green band, giving yellow or brown colour to the stressed vegetation.<\/p>\n

    \"\"<\/p>\n

    Figure 4: Spectral reflectance curve of various land features (https:\/\/gis.stackexchange.com\/questions\/101392\/determining-which-color-each-image-band-represents)<\/em><\/p>\n

     <\/p>\n

    Further, in stressed vegetation, the NIR bands are no longer reflected by the mesophyll cells, instead they are absorbed by the stressed or dead cells causing dark tones in the image. Also, multiple layers of leaves in a plant canopy provide the opportunity for multiple transmittance and reflectance. Hence, the near-IR reflectance increases with the number of layers of leaves in a canopy, with the reflection maximum achieved at about eight leaf layers (Bauer et., 1986).<\/p>\n

     <\/p>\n

    Another example illustrating the importance of spectral reflectance curve is the application in forestry to distinguish between deciduous versus coniferous trees (Figure 5). It is pertinent to mention that the reflectance curve for these trees is plotted as a ribbon of values, not as a single line to include a broad range of reflectance values, rather than a discrete value for each tree.<\/span><\/p>\n<\/div>\n

    \n

    \"\"<\/p>\n

    Figure 5: Generalized spectral reflectance envelopes for deciduous and coniferous trees (sar.kangwon.ac.kr<\/em>)<\/p>\n

     <\/p>\n

    From F igure 5, it is clear that if we try to discriminate the two types of tress in visible bands, it would be a bit difficult since the spectral reflectance curves for the two tree types overlap in the visible region. As a result, both the tree types would appear green in colour and would exhibit similar reflectance. Although this difficulty can be overcome partially by using clues such as size and shape of canopy; however, it would not be possible to give absolute results. However, if we have a sensor that can detect the reflectance of the vegetation in infrared bands, this would solve our purpose. Since, deciduous trees have broad leaves compared to needle shaped leaves of conifers; they show much higher reflectance in infrared wavelength and thus, appear much lighter in tone than conifers. Thus, based on spectral characteristics, various Earth surface features can be identified and mapped.<\/p>\n

     <\/p>\n

    3.2 Soil<\/strong><\/p>\n

     <\/p>\n

    The reflectance curve for soil in figure 4 shows considerably less peak and valley variation. The factors that affect soil reflectance are moisture content, organic matter content, soil texture (proportion of sand, silt and clay), surface roughness and presence of iron oxide. The presence of moisture in soil\u00a0decreases its reflectance due to the presence of water absorption bands at 1.4, 1.9 and 2.7 \u00b5m. Clay soil also has hydroxyl absorption bands at 1.4 and 2.2 \u00b5m. There also exists close relation with soil moisture content and soil texture; for example, coarse, sandy soils are usually well drained, resulting in low moisture content and relatively high reflectance; while, poorly drained fine-textured soils will generally have lower reflectance. Besides, soil reflectance is also reduced by surface roughness, presence of organic matter content and iron oxide. It is pertinent to mention here that soil reflectance comes from the uppermost layer of the soil, and is not be indicative of the properties of the bulk of the soil.<\/span><\/p>\n<\/div>\n

    \n

     <\/p>\n

    3.3 Water<\/strong><\/p>\n

     <\/p>\n

    Clear water absorbs relatively little energy having wavelengths less than about 0.6 \u00b5m. As a result of high transmittance, water generally appears blue. An important characteristic of spectral reflectance of water is its complete absorption at near-IR wavelengths and beyond. As a result, a water body always appears dark when studied in infrared wavelengths. This is the reason that locating and delineating water bodies with remote sensing data is done mostly in near IR wavelengths.<\/p>\n

     <\/p>\n

    However, various conditions of water bodies manifest themselves primarily in visible wavelengths such as the presence of organic as well as inorganic materials in water. For example, highly turbid water containing large quantities of suspended sediments has much higher visible reflectance. Similarly, the presence of chlorophyll pigment as a result of algal growth decreases the reflectance, a fact used for studying the eutrophication status of water body using remote sensing techniques. Similarly, spectral reflectance is aslo used for determining the presence of tannin dyes, industrial wastes discharges and various pollutants that alter the reflectance.<\/p>\n

     <\/p>\n

    3.4 Snow<\/strong><\/p>\n

     <\/p>\n

    Snow reflects strongly in the visible and near infrared and absorbs more energy at mid-IR wavelengths that is characterized by a dip in spectral reflectance. However, the reflectance of snow is affected by its grain size, liquid water content and presence or absence of other materials (Dozier and Painter, 2004). Larger grains of snow absorb more energy, particularly at wavelengths longer than 0.8 \u00b5m. At temperature near 0\u00b0C, liquid water within the snow pack can cause grains to stick together in clusters,\u00a0thus increasing the effective grain size and decreasing the reflectance at near IR and longer wavelengths. When particles of contaminants such as dust or soot are deposited on snow, they can significantly reduce the surface\u2019s reflectance in the visible spectrum.<\/span><\/p>\n<\/div>\n

    \n

    \"\"<\/p>\n

    Figure 6: Spectral reflectance of snow and clouds (http:\/\/www.geol-amu.org\/notes\/m1r-1-8.htm)<\/p>\n

     <\/p>\n

    From Figure 6, it can be inferred that the absorption of mid-IR wavelengths by snow can permit the differentiation between snow and clouds. While both feature types appear bright in the visible and near IR, clouds have significantly higher reflectance than snow at wavelengths longer than 1.4 \u00b5m.<\/p>\n

     <\/p>\n

    3.5 Asphalt<\/strong><\/p>\n

     <\/p>\n

    Sand can have a wide variation in its spectral reflectance pattern depending on its parent material.<\/p>\n

    Besides, presence or absence of water and organic matter also affect the spectral response of sand.<\/p>\n<\/div>\n

     <\/p>\n

    \"\"<\/p>\n

    \n

    Figure 7: Spectral reflectance curve of various land features (http:\/\/slideplayer.com\/slide\/8259758\/)<\/p>\n

     <\/p>\n

    As shown in the figure 7, the spectral reflectance curves for asphalt is much flatter than those of other land cover features; however, its reflectance may be modified by the presence of paint, soot, or water Besides, ageing also has an effect on spectral reflectance o asphalt. The reflectance of asphaltic concrete increases on ageing, particularly, in the visible spectrum.<\/p>\n

     <\/p>\n

    4. Summary<\/strong><\/p>\n

     <\/p>\n

    Having studied the spectral reflectance curve of various land features, it would not be wrong to conclude that these features can be separated spectrally based on their properties of absorption, reflectance and transmittance. This spectral variability study can also be affected by temporal and spatial effects. Temporal effects are those factors that change the spectral characteristics of a feature over time. For example, the spectral characteristics of many vegetation species are in a nearly continual state of change throughout a growing season. Spatial effects refer to the factors that cause the same types of features at a given point in time to have different characteristics at different geographic locations. For example, analysing a crop pattern varies if the analysis is carried out on a small scale or a large regional scale where entirely different soils, climates and cultivation practices might exist for the same crop. Another example could be analysing spectral variability for a diseased\u00a0versus healthy vegetation. However, these effects are extremely helpful while carrying out change detection studies over a given area based on changes in temporal effects.<\/span><\/p>\n<\/div>\n

     <\/p>\n

    In addition to being influenced by temporal and spatial effects, spectral response patterns are influenced by the atmosphere between sensor and the ground. Thus, it is advisable to carry out ground truth studies as a helping aid while studying spectral reflectance pattern for various earth features.<\/p>\n

    \n

     <\/p>\n

    Bibliography \/ Further Reading<\/strong><\/p>\n

     <\/p>\n

      \n
    • American Society of Photogrammetry (1975) \u201cManual of Remote Sensing\u201d, Falls Church, Va.<\/li>\n
    • Avery, T.E., and G.L. Berlin, Fundamentals of Remote Sensing and Airphoto Interpretation, Macmillan, New York, 1992.<\/li>\n
    • Bauer,\u00a0 M.E.,\u00a0 et\u00a0 al.,\u00a0 \u201cField\u00a0 Spectroscopy\u00a0 of\u00a0 Agricultural\u00a0 Crops,\u201d\u00a0 IEEE\u00a0 Transactions\u00a0 on<\/li>\n
    • Geosciences and Remote Sensing, vol. GE-24, no. 1, 1986, pp. 65-75.<\/li>\n
    • Bowker, D.E., et al., Spectral Reflectances of Natural Targets for Use in Remote Sensing Studies, National Aeronautics and Space Administration, Washington, 1985.<\/li>\n
    • Campbell, G.S., and J.M. Norman, An Introduction to Environmental Biophysics, 2nd ed., Springer, New York, 1997.<\/li>\n
    • Campbell, J.B., Introduction to Remote Sensing, 3rd ed., Guilford Press, New York, 2002. Colwell, R.N. (Ed.) 1983. Manual of Remote Sensing. Second Edition. Vol I: Theory,<\/li>\n
    • Curran, P.J. 1985. Principles of Remote Sensing. Longman Group Limited, London.<\/li>\n
    • Dozier, J., and T.H. Painter, \u201cMultispectral and Hyperspectral Remote Sensing of Alpine Snow Properties,\u201d Annual Review of Earth and Planetary Sciences, vol. 32, 2004, pp. 465-494.<\/li>\n
    • Elachi, C. 1987. Introduction to the Physics and Techniques of Remote Sensing. Wiley Series in Remote Sensing, New York.<\/li>\n
    • Elachi, C., Introduction to the Physics and Techniques of Remote Sensing, Wiley, New York, 1987.<\/li>\n
    • Gibson P.J (2000) \u201cIntroductory Remote Sensing- Principles and Concepts\u201d Routledge, London.<\/li>\n
    • http:\/\/civil.iisc.ernet.in\/~nagesh\/rs_docs\/02_SpectralReflectanceCurves_bw.pdf http:\/\/coldregionsresearch.tpub.com\/rsmnl\/rsmnl0034.htm<\/li>\n
    • http:\/\/www.ccrs.nrcan.gc.ca\/ccrs\/learn\/tutorials\/fundam\/chapter1\/chapter1_1_e.html http:\/\/www.geol-amu.org\/notes\/m1r-1-8.htm<\/li>\n
    • http:\/\/www.seos-project.eu\/modules\/remotesensing\/remotesensing-c01-p05.html http:\/\/www.wamis.org\/agm\/pubs\/agm8\/Paper-2.pdf<\/li>\n
    • https:\/\/gis.stackexchange.com\/questions\/101392\/determining-which-color-each-image-band-represents<\/li>\n
    • https:\/\/www.intechopen.com\/books\/biomass-and-remote-sensing-of-biomass\/introduction-to-remote-sensing-of-biomass<\/li>\n
    • Instruments and Techniques. American Society of Photogrammetry and Remote Sensing. ASPRS, Falls Church.<\/li>\n
    • Jet Propulsion Laboratory, ASTER Spectral Library, California Institute of Technology, Pasadena, 1999.<\/li>\n
    • Joseph, G. 1996. Imaging Sensors. Remote Sensing Reviews, 13: 257-342.<\/li>\n
    • Jupp, D.L.B., and A.H. Strahler, \u201cA Hotspot\u00a0 Model for Leaf Canopies, \u201cRemote Sensing of<\/li>\n
    • Environment, vol.38, 1991, pp. 193-210.<\/li>\n
    • Kalensky,\u00a0 Z.,\u00a0 and\u00a0 D.A.\u00a0 Wilson,\u00a0 \u201cSpectral\u00a0 Signatures\u00a0 of\u00a0 Forest\u00a0 Trees,\u201dProceedings:\u00a0 Third<\/li>\n
    • Canadian Symposium on Remote Sensing, 1975, pp. 155-171.<\/li>\n
    • Lillesand, T. M., Kiefer, R. W., Chipman, J. W. (2004). \u201cRemote sensing and image interpretation\u201d, Wiley India (P). Ltd., New Delhi.<\/span><\/li>\n
    • Manual of Remote Sensing. IIIrd Edition. American Society of Photogrammtery and Remote Sensing.<\/span><\/li>\n
    • Nagesh Kumar D and Reshmidevi TV (2013). \u201cRemote sensing applications in water resources\u201d J.<\/span>Indian Institute of Science, 93(2), 163-188.<\/span><\/li>\n
    • Sabins, F.F. 1997. Remote Sensing and Principles and Image Interpretation. WH Freeman, New York.<\/span><\/li>\n
    • Schott, J.R., Remote Sensing: The Image Chain Approach, Oxford University Press, New York, 1997.<\/span><\/li>\n
    • Short N.M (1999). \u201cRemote Sensing Tutorial – Online Handbook\u201d, Goddard Space Flight Center,<\/span>NASA, USA.<\/span><\/li>\n
    • Swain, P.H. and S.M. Davis (eds). (1978) \u201cRemote sensing: The Quantitaive Approach\u201d, McGraw-Hill, New York.<\/span><\/li>\n<\/ul>\n<\/div>\n","protected":false},"author":3,"menu_order":10,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":["dr-puneeta-pandey"],"pb_section_license":""},"chapter-type":[],"contributor":[58],"license":[],"class_list":["post-48","chapter","type-chapter","status-publish","hentry","contributor-dr-puneeta-pandey"],"part":3,"_links":{"self":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/48","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":4,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/48\/revisions"}],"predecessor-version":[{"id":64,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/48\/revisions\/64"}],"part":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapters\/48\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/media?parent=48"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/pressbooks\/v2\/chapter-type?post=48"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/contributor?post=48"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/ebooks.inflibnet.ac.in\/esp06\/wp-json\/wp\/v2\/license?post=48"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}