Electromagnetic energy, the spectrum and spectral signatures

Detail

1. Electromagnetic radiation (EMR): the carrier of information

• Electromagnetic radiation (EMR): energy that travels as waves at the speed of light, which is 300,000 km per second.
• In remote sensing, EMR carries information from the object to the sensor (the instrument that records energy, such as a camera or scanner on a satellite).
• Wavelength and frequency go in opposite directions:
• Wavelength is the distance from one wave crest to the next.
• Frequency is the number of waves that pass a point in one second.

• All EMR moves at the same speed, so short waves have high frequency and long waves have low frequency.

• Unit: remote sensing bands are measured in micrometres (µm). 1 µm = one-millionth of a metre.

2. The electromagnetic spectrum

• Electromagnetic spectrum: the full range of EMR, from short, high-frequency waves to long, low-frequency waves.
• Order, shortest to longest: gamma rays → X-rays → ultraviolet → visible → infrared → microwaves → radio waves.
• Remote sensing mainly uses three regions: visible, infrared and microwave. X-rays are not used. This is a classic NCERT trap (Class 11, Ch 6, exercise 1(ii)).
• Visible light: about 0.4–0.7 µm (400–700 nm). This is the only part the human eye can see [4].
• Infrared (IR): about 0.7–100 µm, just beyond visible red [4]. It is divided into near infrared (NIR), middle infrared and thermal infrared.
• Microwave: most microwave energy passes through the atmosphere without being blocked, so radar waves reach the surface [4]. This is why microwave (radar) satellites can image through cloud. It matters in India during the monsoon, when clouds cover the land.

Region	Approx. wavelength	What the sensor records	Typical use
Visible (blue, green, red)	0.4–0.7 µm [4]	Reflected sunlight	True-colour images, water and land-use mapping
Near infrared (NIR)	0.7–1.1 µm	Reflected sunlight	Vegetation health, separating land from water
Thermal infrared	~8–14 µm	Heat given off by the surface	Surface temperature, fires, urban heat islands
Microwave	mm to cm range	Energy sent out and bounced back (radar), or given off naturally	Imaging in all weather, day or night

3. Spectral bands

• Spectral band: one chosen wavelength range in which a sensor records energy. A multispectral sensor records several bands at once.
• Key NCERT bands:
• Green: 0.5–0.6 µm
• Red: 0.6–0.7 µm

• Near infrared (NIR): 0.7–1.1 µm

• Near infrared (NIR): the band just beyond visible red. The eye cannot see it. Healthy leaves reflect it strongly and clear water absorbs it. So NIR is the best band for separating vegetation, land and water.

• Multispectral sensors record reflected energy in the red, green, blue and infrared bands. Resource managers use this data to monitor fragile lands, wetlands and forests [4].

4. Thermal infrared remote sensing

• Thermal infrared remote sensing: sensing in roughly the 8–14 µm range. It records heat given off (emitted) by surfaces, not sunlight bounced back. So it also works at night.
• Uses: land and sea surface temperature, urban heat islands (cities that stay hotter than the countryside around them), water stress in crops, and forest fires.
• TRISHNA (Thermal Infra-Red Imaging Satellite for High-resolution Natural Resource Assessment) is a joint ISRO–CNES (France's space agency) mission [2]:
• Payloads: a 4-channel thermal infrared (TIR) sensor from CNES, and a 7-band VNIR-SWIR (visible, near infrared and short-wave infrared) sensor from ISRO [2].
• Orbit: sun-synchronous, at 761 km, crossing the equator at 12:30 pm local time [2].
• Resolution: 57 m over land and coasts, and 1 km over oceans and polar regions. Planned life: 5 years [2].
• Main aims: measure surface temperature and the energy and water budgets of land, find water stress in crops, track water use, and check water quality in coastal and inland waters [2].
• Other aims: urban heat islands, volcanic and geothermal hot spots, snow-melt runoff and glacier changes [2].

5. The atmosphere changes the energy on its way

• Cause: energy reflected or emitted by Earth must pass back up through the atmosphere to reach the sensor.
• Process:
• Absorption: carbon dioxide, hydrogen and water vapour absorb middle infrared energy. Atmospheric gases such as water vapour, carbon dioxide and ozone absorb certain wavelengths. These wavelength ranges are called absorption bands [5].

• Scattering: dust and other particles scatter blue light, sending it off in all directions.

• Result: that energy never reaches the sensor, so those wavelengths carry little useful information about the ground.

• Atmospheric windows: wavelength ranges where the atmosphere is transparent, with little or no absorption, so energy passes through easily [4][5].
• Sensors are tuned to these windows so they can "see" the surface [5].
• Examples: the visible range; two short-wave infrared windows at about 1.4–1.8 µm and 2–2.5 µm [5]; the thermal window (about 8–14 µm); and the microwave region, where radiation passes through almost freely [4].

6. How objects interact with energy

• When energy hits an object, the object can absorb, transmit (let it pass through), reflect or emit it. How much of each happens depends on the wavelength and on the object.
• Reflectance: the share of energy received that a surface reflects.
• Absorptance: the share of energy received that a surface absorbs.
• For the same light, a surface may reflect a lot at one wavelength and very little at another. For example, sand reflects more than green vegetation at some wavelengths and less at others [4]. This difference lets us tell materials apart.

7. Spectral signatures

• Spectral signature: an object's pattern of reflectance across different wavelengths. It is unique, like a fingerprint (NCERT Fig. 6.4). Grasslands, pinewoods, red sand and silty water can each be told apart by their reflectance curves [4].

Surface	Blue / green	Red	NIR	How it looks
Healthy vegetation	Moderate (green peak)	Absorbed (used for photosynthesis)	Strongly reflected (many-layered leaf structure)	Bright in NIR; red in standard false-colour images
Fresh, clear water	Some reflection	Absorbed	Absorbed	Dark
Turbid (muddy) water	Reflects more	—	—	Light, because suspended silt reflects light

• Why vegetation reflects NIR:
• A healthy leaf has many internal layers of cells and air spaces.
• NIR light bounces many times inside these layers and is sent back out.

• A stressed or dying leaf loses this structure, so its NIR reflectance drops. Satellites can see this before the leaf looks brown to the eye.

• Sambhar Lake, Rajasthan (India's largest inland salt lake) (NCERT Fig. 6.5):

• Its clear, deep parts look dark, because water absorbs red and NIR.
• Its shallow, muddy or salty parts look light, because they reflect more blue and green.

8. NDVI (Normalised Difference Vegetation Index)

• NDVI: a number that shows how green and healthy vegetation is, using the red and NIR bands.
• Formula: NDVI = (NIR − Red) ÷ (NIR + Red)
• Range: −1 to +1.

• Reading it: healthy vegetation reflects high NIR and little red, so NDVI is high (dense forest or crops). Bare soil gives a value near 0. Water and clouds give negative values.

• NDVI in India: drought monitoring

• NADAMS (National Agricultural Drought Assessment and Monitoring System) was started at the end of 1986. The nodal agency was the National Remote Sensing Agency (now NRSC) under the Department of Space, supported by IMD and state agriculture departments [3].
• It became operational in 1990. It reports how widespread, how severe and how long-lasting agricultural drought is, at state, district and sub-district level [3].
• Early method: NDVI combined with IMD rainfall data. Drought is judged by comparing this season's NDVI curve with the normal NDVI for that place [3].
• Current method: NDVI plus NDWI (Normalised Difference Wetness Index, which shows moisture), SASI (Shortwave Angle Slope Index), and ground data on rainfall and crop-sown area. NRSC produces fortnightly NDVI products [3].
• Sensors used: Resourcesat AWiFS, Oceansat-2 OCM, NOAA AVHRR and Terra MODIS [3].

Prelims Hooks

• Spectrum order (short → long wavelength): gamma → X-ray → UV → visible → infrared → microwave → radio. Frequency runs the opposite way.
• X-rays are NOT used in remote sensing. Visible, infrared and microwave are used.
• Band ranges: green 0.5–0.6 µm, red 0.6–0.7 µm, NIR 0.7–1.1 µm, thermal IR ~8–14 µm. Visible light as a whole is 0.4–0.7 µm [4].
• Healthy vegetation: absorbs red, reflects NIR strongly (because of its many-layered leaf structure).
• Clear water looks dark (absorbs red and NIR). Turbid water looks light (reflects more blue and green). Example: Sambhar Lake, Rajasthan.
• Middle infrared is absorbed by CO₂, hydrogen and water vapour. Blue is scattered by dust.
• Microwave (radar) passes through cloud, which makes it an all-weather sensor [4].
• NDVI = (NIR − Red)/(NIR + Red), range −1 to +1. NADAMS: started 1986, operational 1990, run by NRSC [3].
• TRISHNA: ISRO + CNES (France), thermal infrared, 761 km sun-synchronous orbit, 57 m resolution over land [2].
• Trap: thermal IR records emitted heat, not reflected sunlight, so it works at night. Visible and NIR need sunlight.

Mains Points

• Spectral signatures support farm and drought management (GS-III): NDVI from Resourcesat AWiFS and MODIS, read together with IMD rainfall, gives fortnightly drought reports down to sub-district level under NADAMS [3]. These reports guide drought declarations, contingency crop plans and crop insurance checks.
• Thermal infrared for water security and climate adaptation: TRISHNA is designed to measure crop water stress and evapotranspiration (water lost from soil and plants) at 57 m, and to map urban heat islands [2]. This helps irrigation scheduling in water-scarce states and heat action plans for cities.
• Matching the sensor to the atmosphere and the weather: absorption bands and cloud cover limit optical sensors, especially during the Indian monsoon (June–September). Placing sensors in atmospheric windows [5] and using microwave radar [4] together keep monitoring going for floods, cyclones and kharif crops.
• Limits of spectral data: different objects can have similar signatures, for example turbid water and wet soil. So satellite readings must be checked against ground truth (field data), and this is why NADAMS combines satellite indices with sown-area and rainfall data [3].