Remote Sensing, GIS and GNSS

1. What remote sensing is and its stages

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• Human eyes and cameras pick up only a tiny part of the energy that objects give off. Remote sensors record a much wider range. Every object warmer than 0 K (-273°C) reflects, emits, absorbs and transmits radiation.
• Remote sensing means collecting and measuring information about objects with a recording device (sensor) that does not touch them. It uses energy reflected or given off by those objects. The term first appeared in the early 1960s (beyond NCERT: Evelyn Pruitt of the US Office of Naval Research coined it).
• It has three elements: an object surface, a sensor, and energy waves that carry the information.

• Stages in remote sensing (eight steps): 1. Source of energy (the sun, or energy the sensor sends out itself) 2. Energy travels to the earth's surface 3. Energy interacts with the surface 4. Reflected or emitted energy travels back up through the atmosphere 5. The sensor detects it 6. The energy is turned into photographic or digital data 7. Information is taken out of the data 8. That information becomes maps or tables

• Why it beats traditional survey (NCERT exercise): it covers a wide area in one view, it covers the same place again and again, it reaches remote areas, and it records more than visible light.

2. Electromagnetic energy, the spectrum and spectral signatures

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• Electromagnetic radiation (EMR): energy that travels as waves at the speed of light (300,000 km per second).
• Electromagnetic spectrum: the full range of EMR, from short, high-frequency waves to long, low-frequency waves. In order: gamma rays → X-rays → ultraviolet → visible → infrared → microwaves → radio waves. Remote sensing uses mainly the visible, infrared and microwave regions. X-rays are not used.
• Spectral band: one chosen wavelength range. Green is 0.5-0.6 µm, red is 0.6-0.7 µm and near infrared (NIR) is 0.7-1.1 µm. NIR lies just beyond visible red. Healthy leaves reflect it strongly and clear water absorbs it.
• Thermal infrared remote sensing (beyond NCERT; about 8-14 µm) records heat given off by surfaces. It maps land and sea temperature, urban heat islands, water stress in crops and fires. ISRO and France's CNES are planning the TRISHNA satellite for it (verify current).
• Atmospheric absorption and scattering: carbon dioxide, hydrogen and water vapour absorb middle infrared energy, and dust scatters blue light. That energy never reaches the sensor. So sensors work in "atmospheric windows", the wavelengths that pass through the air easily (beyond NCERT).
• Reflectance is the share of energy received that a surface reflects. Absorptance is the share it absorbs.
• Spectral signature: an object's reflectance pattern across wavelengths, unique like a fingerprint. Healthy vegetation reflects strongly in NIR because its leaves have many layers, and it absorbs red. Fresh water absorbs red and NIR, so it looks dark. Turbid (muddy) water reflects more blue and green, so it looks light. NCERT shows this with images of Sambhar Lake, Rajasthan.
• NDVI (beyond NCERT) = (NIR - Red) ÷ (NIR + Red). It ranges from -1 to +1, and higher values mean denser, healthier vegetation. NRSC uses it to monitor crops and agricultural drought.

3. Orbits and the ground segment

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Feature (Box 6.1)	Sun-synchronous orbit	Geostationary orbit
Height	700-900 km, near-polar	About 36,000 km above the equator (exactly 35,786 km)
Period	About 14 orbits a day	24 hours, moving the same way the earth spins
Coverage	81°N to 81°S	About ⅓ of the globe, from a fixed spot
Resolution	Fine (182 m to 1 m)	Coarse (about 1 km × 1 km)
Uses	Earth resources (IRS, Landsat)	Telecom and weather (INSAT)

• "Sun-synchronous" means the satellite crosses each latitude at the same local solar time (beyond NCERT). IRS passes around mid-morning. The lighting is the same every time, so images from different dates can be compared. Common exam pair: IRS vs INSAT (NCERT exercise).
• Earth receiving station: a ground station that receives image data the satellite sends down electronically. India's station is at Shadnagar near Hyderabad. Errors in the data are corrected first, and then information is extracted.
• National Remote Sensing Centre (NRSC), Hyderabad: an ISRO centre (set up as NRSA in 1974; part of ISRO since 2008). It receives, processes and distributes satellite data. It runs Bhuvan and supports disaster response, and it prepared India's classification of wastelands.
• The Indian Institute of Remote Sensing (IIRS), Dehradun, trains users (beyond NCERT).

4. Sensors: cameras, scanners, radar and LiDAR

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• Sensor: a device that receives EMR, turns it into a signal and shows it as a photographic or digital image.
• Photographic sensor (a camera, analogue): records the whole scene on film in one exposure. It works only in the optical region (0.3-0.9 µm).
• Multispectral scanner (non-photographic): builds the image bit by bit, line by line, as it sweeps across its view. It uses a mirror and detectors, and stores the energy it receives as digital numbers.
• Swath: the width of the ground strip imaged in one pass. The sensor's angular field of view sets it.
• Whiskbroom scanner: a rotating mirror plus a single detector that sweeps sideways across 90°-120°. This full sweep is the total field of view (TFOV). The instantaneous field of view (IFOV) is what the detector sees at one moment, and it fixes the ground cell size.
• Pushbroom scanner: a straight line of detectors, one for each ground cell across the swath, so a whole line is recorded at once. Example: SPOT HRV has a 60 km swath and 20 m cells, so 60,000 ÷ 20 = 3,000 detectors.
• Passive and active remote sensing (beyond NCERT): passive sensors record reflected sunlight or emitted heat, so optical sensors need daylight and clear skies. Active sensors send out their own energy.
• Radar (radio detection and ranging) sends microwave pulses and records the echoes. It works through cloud and at night, which makes it ideal in the monsoon.
• Synthetic aperture radar (SAR) uses the satellite's movement to act like a very long antenna, which gives sharp images. Its interferometry measures ground sinking down to millimetres.

• LiDAR fires laser pulses and times how long they take to return. It maps heights, even the ground under forest cover, for DEMs and 3-D city models.

• Hyperspectral remote sensing: records hundreds of narrow bands that sit next to each other, so each pixel can be matched to a mineral, crop or pollutant. Examples are ISRO's HySIS (2018) and Pixxel's Firefly satellites (2025).

5. Resolution: spatial, spectral, radiometric and temporal

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• Spatial resolution: how well a sensor separates two objects that are close together. It is given as the ground size of one pixel. NCERT compares it to reading glasses that help you tell close letters apart.
• Spectral resolution: how many bands a sensor records and how narrow they are. It is based on the dispersion of light (splitting white light into colours, as in a rainbow or prism). In NCERT's Resourcesat-1 images of Najafgarh, Delhi (2005), fresh water absorbs in band 4 (IR) and dry surfaces reflect in band 2 (green).
• Radiometric resolution: how well a sensor detects small differences in energy, counted in grey levels. Landsat MSS has 0-64, IRS LISS 0-127, and Landsat TM and SPOT 0-255. Beyond NCERT, these are 6-, 7- and 8-bit data; modern sensors use 10-12 bits.
• Temporal resolution: how long a satellite takes to image the same area again. Its orbit sets this.
• Change detection: comparing images of the same area from different dates. NCERT examples: IRS images of the Himalaya in May and November show conifers, then deciduous trees and crops; Banda Aceh, Indonesia, before and after the December 2004 tsunami. GIS overlay is also used for this (section 10).
• Trade-off (beyond NCERT): finer spatial resolution usually means a narrower swath and slower revisits. This is why countries build constellations (groups of satellites).
• Table 6.1 (spatial resolution): Landsat MSS 80 m · LISS-I 72.5 m · LISS-II 36.25 m · Landsat TM 30 m · LISS-III 23 m · SPOT HRV-I 20 m · SPOT HRV-II 10 m · IRS PAN 5.8 m.

6. Data products and image interpretation

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• An image is any picture record, whatever wavelength it uses. A photograph is recorded on film. All photographs are images, but not all images are photographs.
• Aerial photograph: a photo of the ground taken from an aircraft, usually on black-and-white film. Colour, B/W infrared and colour infrared film are also used. It can be enlarged without losing detail, and it is a base for topographic maps.
• Digital image: a grid of pixels (the smallest cells, each with a fixed address) in rows and columns. Each pixel has a digital number (DN), the average brightness of that ground cell. Zooming in too far shows only pixels.
• Digital image processing: computer work on the DNs to correct, enhance and classify the image. Visual image interpretation is identifying objects by eye instead.
• False colour composite (FCC): colours are given to bands they do not belong to. In standard FCC, green is shown as blue, red as green and NIR as red. So healthy vegetation looks red.
• Table 6.2 (standard FCC colours): evergreen red to magenta · deciduous brown to red · cropped land bright red · fallow light blue to white · clear water dark blue to black · turbid water light blue · dense built-up dark blue to bluish green · sand and salt-affected land light blue to white · waterlogged land mottled black.
• Elements of visual interpretation come in two groups. Image characteristics are tone, texture, size, shape, shadow and pattern. Terrain characteristics are location and association.
• Tone: the shade or colour, which depends on reflected energy. Dry, smooth surfaces look light and fresh water looks dark.
• Texture: how often the tone changes in a small area. Dense housing looks fine; sugarcane looks coarse next to the fine texture of rice.
• Size: separates factory complexes from houses. Shape: picks out Sansad Bhawan, or a railway's gentle curves from a road's sharp bends.
• Shadow in image interpretation: identifies tall, thin objects such as the Qutub Minar and water tanks. It is more useful in aerial photographs than in satellite images.
• Pattern in image interpretation: a repeated layout, such as planned housing or orchards.
• Association in image interpretation: what an object is found with. Schools sit near playgrounds; slums sit along drains and railway lines.

7. Earth observation satellites: the world and India

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• Landsat (USA, from 1972, first called ERTS-1) has the longest continuous record of the earth's land. It carried MSS (80 m) and TM (30 m); Landsat 9 launched in 2021.
• SPOT (France, from 1986) brought pushbroom HRV sensors at 20 m and 10 m.
• Sentinel satellites (EU Copernicus, from 2014) give free data: radar from Sentinel-1 and 10 m multispectral images from Sentinel-2. They are used for floods, crops and forests.
• Bhaskara satellites: Bhaskara-I (1979) and Bhaskara-II (1981) were India's first experiments. They flew on Soviet rockets and carried TV cameras and microwave radiometers.
• Indian Remote Sensing satellites (IRS): the series began with IRS-1A, launched 17 March 1988 from Baikonur. They support work on resources, farming, forests, water and disasters.
• LISS: the multispectral pushbroom cameras on IRS. LISS-I (72.5 m) and LISS-II (36.25 m) flew on IRS-1A. IRS-1C (1995) added LISS-III (23 m) and PAN (5.8 m).
• Resourcesat: IRS-P6 (2003) carried LISS-III, LISS-IV and AWiFS for crop, land and water monitoring. Resourcesat-2 and 2A followed.
• Cartosat series: Cartosat-1 (2005) took stereo pairs for terrain models. Cartosat-3 (2019) reaches about 25 cm.
• Oceansat: maps chlorophyll, potential fishing zones and sea-surface winds. Oceansat-3/EOS-06 launched in 2022.
• RISAT: C-band SAR satellites that work through cloud and at night. RISAT-1 launched in 2012 and EOS-04 (RISAT-1A) in 2022; they serve floods, crops and border watch. The PSLV-C61 launch of EOS-09 failed in May 2025 (verify).
• INSAT-3DS (February 2024, GSLV-F14): a geostationary weather satellite with an imager and sounder. It tracks cyclones and relays distress signals.
• NISAR (NASA-ISRO; launched 30 July 2025 by GSLV-F16): the first satellite with both L-band and S-band radar. It images almost all land and ice every 12 days to track earthquakes, glaciers, landslides, crops and forests.

8. Spatial information technology and GIS: concepts and components

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• A database management system (DBMS) organises and processes tabular data. Computer-assisted cartography draws maps by computer. Neither can answer "what is where, why is it there, and what if it moves?"
• Spatial information technology: the use of technology to collect, store, retrieve, display, manipulate, manage and analyse information tied to locations. It combines remote sensing, GPS, GIS, digital cartography and DBMS.
• Geographic information system (GIS) (possible since the mid-1970s): a system for capturing, storing, checking, combining, manipulating, analysing and displaying data tied to places on the earth. It draws on cartography, statistics, computer science and remote sensing. Class 11, Geography as a Discipline notes that GIS and GPS have widened geography.
• Spatial data: data tied to a location and registered to a coordinate system. Attribute data: the descriptive, non-location data about those features. In NCERT's Box 4.1, a cycle shop's stock register is non-spatial, but state-wise literacy is spatial because states have map locations.
• Point, line and area features: points are positional (wells, towns), lines are linear (roads, rivers) and areas or polygons are areal (lakes, districts).
• Spatial data come from three sources: buying digital data from suppliers, digitising existing maps, or doing your own surveys.
• Advantages of GIS over manual maps: a paper map fixes one presentation and theme, and must be redrawn to change. A GIS stores data separately from the display. So users can query features, draw maps from attributes, run overlay and buffering, and link attributes through a shared location code.
• Components of GIS: hardware, software (data entry and editing, analysis, and display and output modules), data (the backbone), people (from engineers to planners) and procedures.

9. Raster and vector data, data input and matching

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• Raster data model: a grid of equal cells. Each cell has a row-column position and a value, and the cell size sets the resolution. It is used for satellite images, aerial photographs, scanned maps and cheap backdrop maps.
• Vector data model: points stored as real coordinates (x, y or x, y, z), with lines and polygons built from ordered points. It can store topology, the recorded links between features: which lines connect, which areas share a border and which lie inside others. Topology is built after errors are corrected.

Box 4.2	Raster	Vector
Advantages	Simple; easy overlay; fits satellite images; handles high variability	Compact; precise output; good for network analysis and projection change
Disadvantages	Uses much storage; rough shapes; poor for networks and projection change	Complex; harder overlay; does not fit satellite images

• Sequence of GIS work: spatial data input → attribute entry → verification and editing → linking spatial and attribute data → spatial analysis.
• Digitisation: tracing map features into coordinates, the best way to input vector data. Common errors are omissions, undershoots and overshoots.
• A scanner turns paper maps into raster images. Its charge-coupled devices (CCDs) convert light (photons) into electron counts that are stored as digital values. The scanned image must then be cleaned or converted to vector.
• Georeferencing: giving a map or image real-world coordinates so that layers line up. Mistakes here put data at the wrong scale. Distortion can also come from lens faults, relief or folded paper.
• Raster-vector conversion: all layers must share one format. Vector-to-raster (laying a grid of chosen cell size over the data) is more common because most analysis is raster-based. Raster-to-vector is done to save storage.
• Exact matching joins two files on a common key, such as town names. Hierarchical matching adds up small-area data until it fits larger units. Fuzzy matching handles boundaries that do not line up, such as crop fields and soil types, by overlaying the layers and computing each combination.

10. Spatial analysis: overlay, buffer and network

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• Spatial analysis is what sets GIS apart from other information systems. It uses spatial and attribute data together to answer real questions and model trends for decision-makers, after the problem has been defined. NCERT lists four operations: overlay, buffer, network analysis and the digital terrain model.
• Overlay analysis, the hallmark of GIS, stacks thematic layers of the same area to make a new layer. It is the digital form of sieve mapping (placing map tracings on top of each other on a light table).
• NCERT example: overlaying Aligarh's urban land use of 1974 and 2001 shows land change and urban sprawl.

• It is also used for land-suitability analysis, such as siting a landfill away from water and settlements.

• Buffer analysis draws zones of a set distance around points, lines or areas to find who is served or affected. This is called proximity analysis.

• NCERT example: 2-10 km rings around hospitals in Saharanpur, Muzaffarnagar, Meerut, Ghaziabad, Gautam Buddh Nagar and Aligarh (western Uttar Pradesh) show which areas are poorly served.

• Another example: households within 1 km of a chemical unit form an exposure zone.

• Network analysis finds the shortest or fastest routes and service areas on roads, railways, pipelines or rivers. Vector data suit it best.

• Software named in NCERT: ArcView/ArcGIS, GeoMedia and the free QGIS.
• Exam pairing: urban change detection = overlay; population served = buffer.

11. Satellite navigation: GNSS, GPS, NavIC and GAGAN

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• Global navigation satellite system (GNSS): satellite groups that give positioning, navigation and timing. Global systems: the US GPS, Russia's GLONASS, the EU's Galileo and China's BeiDou (global since 2020). Regional systems: India's NavIC and Japan's QZSS.
• Global Positioning System (GPS): the US system, with about 31 operational satellites in medium earth orbit about 20,200 km up (verify current). NCERT mentions GPS only as part of spatial information technology.
• Trilateration: how a receiver fixes its position. It works out its distance to at least four satellites from the signals' travel times. Three distances give the position and the fourth corrects the receiver's clock. This is why very precise atomic clocks are central to the system.
• NavIC (the Indian Regional Navigation Satellite System, renamed in 2016):
• Planned as seven satellites (three geostationary, four inclined geosynchronous).
• Covers India and about 1,500 km around it, with a target accuracy better than 20 m.
• IRNSS-1A launched in 2013, and the IMO (International Maritime Organization) recognised NavIC in 2020.

• Setbacks: atomic clocks failed on early satellites, and IRNSS-1H failed in 2017.

• NVS series satellites: the second generation, with an indigenous rubidium atomic clock and an L1 signal that phones can use. NVS-01 launched in May 2023. NVS-02 (January 2025) failed to reach its planned orbit because of a valve fault (verify current constellation status).

• GAGAN (GPS Aided GEO Augmented Navigation; ISRO and the Airports Authority of India) corrects GPS signals over India to guide aircraft approaches. It was certified in 2015, the first such system serving the equatorial region.
• Continuously Operating Reference Stations (CORS): a Survey of India network of fixed stations. It sends corrections that give centimetre-level positions for surveying, mapping and construction.

12. India's geospatial ecosystem: Bhuvan, policies and governance uses

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• Bhuvan (NRSC, launched 2009): ISRO's online geoportal for Indian satellite imagery, thematic maps and services.
• Geotagging through Bhuvan: attaching GPS coordinates and photos to assets so the work can be checked on the ground. It is used for MGNREGA assets (since 2016), PMAY-G houses and Amrit Sarovars.
• National Spatial Data Infrastructure (NSDI, 2006): shared standards and a national geoportal, so that government agencies stop repeating the same mapping work.
• Geospatial guidelines 2021 (15 February 2021): Indian entities no longer need prior approval to collect, make or publish maps and data, except on a negative list of sensitive attributes. The finest-resolution data is kept for Indian entities.
• Drone Rules 2021 (August 2021): fewer forms and fees, and green, yellow and red airspace zones on the Digital Sky platform.
• SVAMITVA scheme (24 April 2020): drones and the Survey of India map inhabited village land, and owners get property cards. These help them get loans and reduce disputes (coverage: verify current).
• National Geospatial Policy 2022 (notified December 2022):
• Sets up a Geospatial Data Promotion and Development Committee.
• Aims for high-accuracy topographic mapping and a national DEM by 2030.

• Aims for digital twins (detailed digital 3-D copies) of major cities by 2035.

• Governance uses:

• FASAL: pre-harvest crop forecasts made by the Mahalanobis National Crop Forecast Centre, New Delhi.
• NRSC: drought and flood maps, and the National Database for Emergency Management. It also produced Joshimath subsidence images in January 2023.

• PM Gati Shakti National Master Plan (October 2021): a GIS platform built by BISAG-N for infrastructure planning.

• Mains angle: privacy, data sovereignty (a country's control over its own data) and the capacity of states to use these tools.

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Exam angles

Prelims — high-yield facts and traps

• Sun-synchronous orbit: 700-900 km, near-polar, about 14 orbits a day, fine resolution, used by IRS and Landsat. Geostationary orbit: about 36,000 km, over the equator, 24-hour period, coarse resolution (about 1 km), used by INSAT.
• Standard FCC: NIR shown red, red shown green, green shown blue. Healthy vegetation is red, clear water dark blue to black, turbid water light blue, fallow land light blue to white.
• "Remote sensing uses X-rays": FALSE. It uses visible, infrared and microwave.
• "Radar needs sunlight": FALSE. It is active and works at night and through cloud.
• Pushbroom = a line of detectors (SPOT, IRS LISS; SPOT HRV needs 3,000). Whiskbroom = a rotating mirror plus one detector sweeping 90°-120°.
• The four resolutions: spatial (pixel size), spectral (bands), radiometric (grey levels: LISS 0-127, TM 0-255), temporal (revisit time).
• Digital image processing is not a visual interpretation technique (NCERT exercise). Shadow is more useful in aerial photographs than in satellite images.
• Raster is easy to overlay and fits satellite images. Vector is compact, precise and good for network analysis. Urban change = overlay; proximity = buffer.
• Order of Indian satellites: Bhaskara-I (1979) → Bhaskara-II (1981) → IRS-1A (1988, Baikonur) → IRS-1C (1995) → Resourcesat-1 (2003) → Cartosat-1 (2005) → RISAT-1 (2012) → Cartosat-3 (2019, about 25 cm) → EOS-04 (2022) → INSAT-3DS (2024) → NISAR (2025, L- and S-band).
• NavIC is regional: 7 satellites, about 1,500 km beyond India. The NVS series carries an indigenous rubidium clock. GAGAN is for aviation. A GNSS fix needs at least four satellites.
• NDVI = (NIR - Red) ÷ (NIR + Red). Earth station at Shadnagar; NRSC in Hyderabad; IIRS in Dehradun; Bhuvan launched 2009.
• Policy dates: SVAMITVA (24 April 2020), geospatial guidelines (15 February 2021), Drone Rules (August 2021), Gati Shakti (October 2021), National Geospatial Policy (December 2022).

Mains — GS-I and GS-III themes

1. Uses of remote sensing and GIS in disaster management, agriculture (FASAL, NDVI drought monitoring) and urban planning (sprawl mapping, digital twins).
2. How the 2021 guidelines and the National Geospatial Policy 2022 moved India from guarding geospatial data to treating it as public infrastructure. Weigh the opportunities against security and privacy concerns.
3. NavIC as strategic infrastructure: why a regional system matters for independence from foreign systems, and the problems with clock failures and the NVS-02 failure.
4. Drone-based land mapping (SVAMITVA) and its role in access to credit, fewer disputes and gram panchayat planning.
5. Optical vs radar remote sensing for monsoon flood mapping and crop monitoring in India (RISAT, EOS-04, NISAR).

Current-affairs hooks

• ISRO earth observation and NavIC launches, including NVS replacements, EOS missions and failures such as EOS-09 and NVS-02 (verify current).
• NISAR's data releases and its use for Himalayan glaciers, landslides and crops.
• NRSC satellite maps of floods, landslides and subsidence after disasters (for example, Joshimath 2023).
• Private earth observation constellations (for example, Pixxel) under IN-SPACe and the Indian Space Policy 2023 (verify current).
• SVAMITVA property cards, Gati Shakti and Bhuvan geotagging in how schemes are monitored.
• Drone policy updates, Namo Drone Didi and the domestic drone industry.