Spectral Reflectance of Land Covers

 Land-cover and land-use maps are required for many applications such as regions planning, landscape ecology and landscape planning, agricultural management, and forestry.

Electromagnetic energy reaching the earth's surface from the Sun is reflected, transmitted or absorbed. A basic assumption made in remote sensing is that specific targets (soils of differed types, water with varying degrees of impurities, rocks of differing lithologies, or vegetation of various species) have an individual and characteristic manner of interacting with incident radiation that is described by the spectral response of that target.

The spectral response of a target also depends upon such factors as the orientation of the Sun, the height of the Sun in the sky (solar elevation angle), direction in which the sensor is pointing relative to nadir (the look angle), the topogaphic position of the target in terms of slope orientation, the state of health of vegetation if that is the target, and the state of the atmosphere. The spectral reflectance curve is affected by factors such as soil nutrient status, the growth stage of the vegetation, the colour of the soil (which may be affected by recent weather conditions).

In some instances, the nature of the interaction between incident radiation and earth's surface materials will vary from time to time during the year, such as might be expected in the case of vegetation as it develops from  the leafing stage, through growth to maturity and, finally to senescence.  The term 'spectral signature' is sometimes used to describe the spectral response curve for a target. 

Fig 1: Typical spectral reflectance curves of common earth surface materials in the visible and near to mid-infrared range. The positions of spectral bands for some remote sensors are also indicated

The earth surface materials that are considered here are vegetation, soil, bare rock and water. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing.

The fundamental approach in remote sensing is to investigate the spectral signature before a correct image interpretation may be achieved.  The variety of earth’s surface materials is enormous, and therefore the recording of their spectral signatures (also known as spectral library) requires substantial financial and time investments. For years, efforts have been made to establish such datasets and pool them for general use through what are known as spectral libraries.  Such spectral libraries are maintained by many organizations including the Johns Hopkins University (JHU), the Jet Propulsion Laboratory (JPL), and the United States Geological Survey (USGS).  Many of these datasets are made available with commercial remote sensing image processing software packages. A typical spectral reflectance curve of the most common earth surface materials viz., water and vegetation is shown in Fig 1.

Spectral reflectance of common land covers

Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. The reflectance is low in both the blue and red regions of the spectrum, due to absorption by chlorophyll for photosynthesis. It has a peak at the green region. In the near infrared (NIR) region, the reflectance is much higher than that in the visible band due to the cellular structure in the leaves. Hence, vegetation can be identified by the high NIR but generally low visible reflectances. This property has been used in early reconnaisance missions during war times for "camouflage detection".

The reflectance of bare soil generally depends on its composition. In the spectral reflectance curves shown in Figs 5 and 6, the reflectance increases with increasing wavelength. The reflectance of clear water is generally low. However, the reflectance is maximum at the blue end of the spectrum and decreases as wavelength increases. Hence, clear water appears dark-bluish. Turbid water has some sediment suspension which increases the reflectance in the red end of the spectrum, accounting for its brownish appearance.

Spectral Characteristics of Vegetation

The spectral reflectance of vegetation can be detected in three major EMS regions:

·        Visible region (400-700 nm) – Low reflectance, high absorption, and minimum Transmittance.  The fundamental control of energy-matter interactions with vegetation in this part of the spectrum is plant pigmentation

·        NIR (700-1350 nm)High reflectance and transmittance, very low absorption.  The physical control is internal leaf structures.

Fig 2:  Partitioning of Vegetation Spectral Reflectance in the VIS, NIR and MIR regions of the electromagnetic spectrum.

·        MIR (1350-2500 nm)As wavelength increases, both reflectance and transmittance generally decrease from medium to low, while absorption increases from low to high.   The primary physical control in these middle-infrared wavelengths for vegetation is in vivo water content.  Figs 2, 3 and 4 show the spectral reflectance characteristics of vegetation in different conditions.  

Fig 3:  Spectral Reflectance of Vegetation – Foliar Reflectance.


Fig 4:  Variation in the spectral reflectance characteristics of vegetation according to leaf moisture content.

Spectral Characteristics of Soil

The spectral reflectance of soil is controlled, for the most part, by six variables

 a) Moisture content
 b) Organic matter content
 c) Particle size distribution
 d) Iron oxide content
 e) Soil mineralogy
 f) Soil structure

Of these variables, moisture content is the most important due to its dynamic nature and large overall impact on soil reflectance. 

Fig 5:  Variation in the spectral reflectance characteristics of soil according to moisture content.

Figs 5, 6 and 7 show the spectral reflectance curves of different types of soils with different moisture contents.

Fig 6:  Variation in the spectral reflectance characteristics of soil according to particle size.


Fig 7:  Variation in the spectral reflectance characteristics of soil according to soil texture.

Urban Land Cover

In urban areas the spectral reflectance of an individual pixel will generally not resemble the reflectance of a single land cover class but rather a mixture of the reflectances of two or more classes.  Because they are combinations of spectrally distinct land cover types, mixed pixels in urban areas are frequently misclassified as other classes.  Conversely, the definition of an urban spectral class will often incorporate pixels of other non-urban classes as well.

Spectral Characteristics of Water

There are three types of possible reflectance from a water body

a) Surface (specular) reflectance
b) Bottom reflectance
c) Volume reflectance

Only volume reflectance contains information relating to water quality. For deep (> 2 m) clear water bodies, volume reflectance is very low (6-8 percent) and is confined to the visible wavelengths.

Fig 8:  Spectral reflectance characteristics of deep, clear water.

Clear water reflects very little solar irradiance, but turbid water is capable of reflecting significant amounts of sunlight.  As the chlorophyll content of a water bodyincreases (resulting from an increase in algae, phytoplankton, etc.) its blue-lightreflectance decreases while its green-light reflectance increases 

Fig 9:  Variation in the spectral reflectance characteristics of turbid water according to the content of suspended solids.

Figs 8 and 9 show the spectral reflectances of clear and turbid water with different amounts of suspended solids.

Spectral Characteristics of Snow and Clouds

Snow and clouds can be easily differentiated only in the middle-infrared portion of the spectrum.  Snow reflectance is very high in the visible and NIR wavelengths, but drops to near zero in the water absorption bands.  Most clouds act as non-selective scatterers and reflect significant amounts of solar irradiance across the 400-2500 nm spectrum.  In general, snow reflects more visible and NIR radiation than ice does. 

Fig 10:  Spectral reflectance characteristics of clouds and snow.  Snow shows variation in spectral reflectance according to the size of crystals.

Fig 10 shows the spectral reflectance characteristics of clouds and snow.  Snow shows variation in spectral reflectance according to the size of crystals.

Shapes of Spectral Reflectance Curves

The shape of the reflectance spectrum can be used for identification of vegetation type. For example, the reflectance spectra of green and dry vegetation in Fig 4 can be easily distinguished, although they exhibit the same general characteristics of high NIR but low visible reflectances. Dry vegetation has a somewhat higher reflectance in the visible and NIR regions as compared to green vegetation. For the same vegetation type, the reflectance spectrum also depends on other factors such as the leaf moisture content and health of the plants. These properties enable vegetation condition to be monitored using satellite remote sensing images.

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