Spatial resolution

Geography glossary

Topic: Remote Sensing, GIS and GNSS · NCERT: Class 11, Ch 6 "Introduction to Remote Sensing"

Meaning

Spatial resolution is how well a sensor can separate two objects that lie close together on the ground. It is given as the ground size of one pixel, for example 23 m for IRS LISS-III or 5.8 m for IRS PAN. Spatial resolution decides the smallest object a satellite image can show. A field, a road or a building smaller than one pixel cannot be seen on its own.

Explanation

How it works: pixel → ground patch → detail

  • A digital image is made of pixels (the smallest squares of the image).
  • Each pixel stores one value for one patch of ground.
  • A 30 m sensor records one value for each 30 m × 30 m patch.

  • Everything inside that patch (a house, a tree, a road) gets mixed into that single value.

  • Result: objects smaller than the pixel blur together. Objects larger than the pixel can be told apart.

  • The NCERT spectacles analogy: reading glasses help you tell apart letters that sit close together. In the same way, a sensor with finer spatial resolution can tell apart objects that sit close together.

Fine vs coarse: read the number the right way

  • A smaller number means finer detail. 5.8 m is finer than 80 m.
  • NCERT Table 6.1, from coarsest to finest:
Sensor Spatial resolution
Landsat MSS (USA) 80 m
IRS LISS-I (India) 72.5 m
IRS LISS-II (India) 36.25 m
Landsat TM (USA) 30 m
IRS LISS-III (India) 23 m
SPOT HRV-I (France) 20 m
SPOT HRV-II (France) 10 m
IRS PAN (India) 5.8 m
  • LISS-II (36.25 m) is exactly half of LISS-I (72.5 m). So LISS-II sees twice the detail along each side of a pixel.

What sets it: optics, orbit and band width

  • Sensor optics and orbit height: these two decide how big a patch of ground one pixel covers.
  • Panchromatic vs multispectral:
  • A panchromatic sensor records one wide black-and-white band.
  • One wide band collects more light, so each pixel can be made smaller.
  • So the panchromatic image is always sharper than the multispectral image from the same satellite.

  • Example: Landsat 8 has a 15 m panchromatic band, 30 m multispectral bands and a 100 m thermal band (resampled, that is re-gridded, to 30 m) [3].

  • Later Indian sensors on Resourcesat-2A (PSLV-C36, 7 December 2016, Sun-synchronous polar orbit) [2]:

  • LISS-4: 5.8 m (fine) [2]
  • LISS-3: 23.5 m (NCERT's older LISS-III: 23 m) [2]
  • AWiFS (Advanced Wide Field Sensor): 56 m (coarse) [2]

The trade-off: fine detail costs coverage

  • Finer spatial resolution usually means a narrower swath (swath: the width of ground imaged in one pass).
  • The sensor sees a smaller strip of ground each time.
  • So it takes longer to cover the whole Earth.

  • So the revisit interval (temporal resolution) gets longer.

  • Examples:

  • AWiFS (56 m) has a wide swath and is used for regional crop watch [2].
  • LISS-4 (5.8 m) has a narrow swath. It has to be steered ±26° across its track (tilted sideways) to get a five-day revisit [2].

  • Landsat 8 OLI (Operational Land Imager): 30 m, 185 km swath, 16-day revisit [3].

  • The fix is a constellation (a group of satellites working together). Landsat 8 and Landsat 9 together cut the revisit from 16 to 8 days [3].

Where it occurs

  • India (ISRO's IRS series): the detail has improved over time.
  • LISS-I at 72.5 m (IRS-1A, 1988).
  • Then LISS-II at 36.25 m and LISS-III at 23 m.

  • Then 5.8 m with IRS PAN and LISS-4 on Resourcesat-2A [2].

  • Cartosat-3, India: launched on 27 November 2019 by PSLV-C47 from Sriharikota. It flies in a Sun-synchronous polar orbit at 509 km. ISRO calls it its "most complex and advanced" Earth observation satellite. It is an agile satellite, which means it can quickly tilt to point at a target [4].

  • How India uses each level of detail:
  • Fine (LISS-4, Cartosat): cadastral maps (land-ownership maps), urban planning and infrastructure.
  • Coarse but wide (AWiFS 56 m): crop estimates over a whole state [2].

  • The data is shared through Bhuvan (ISRO's geoportal) and opened up under the National Geospatial Policy 2022.

  • The world:

  • USA: Landsat MSS (80 m), Landsat TM (30 m), Landsat 8 (30 m multispectral, 15 m panchromatic) [3].
  • France: SPOT HRV-I (20 m) and SPOT HRV-II (10 m).

Don't confuse with

  • Spectral resolution: this counts how many wavelength bands a sensor records and how narrow each band is. It is based on the dispersion of light (the way a prism or a rainbow splits white light). Spatial resolution is about the size of the ground patch, not about colours.
  • Radiometric resolution: this counts grey levels in bits (Landsat MSS 6-bit, Landsat TM 8-bit, Landsat 8 OLI 12-bit with 4,096 values [3]). It measures small changes in brightness, not the size of objects.
  • Temporal resolution: this is how often a satellite sees the same place again (Landsat 8: 16 days [3]). The orbit sets it. Finer spatial resolution usually makes the revisit longer, not shorter.
  • Swath: this is the width of ground covered in one pass. A wide swath often comes with coarse resolution (AWiFS), so a wide image does not mean a sharp one.

Prelims Hooks

  • Spatial resolution = ground size of one pixel. The smaller the number, the finer the detail.
  • NCERT Table 6.1: the finest sensor is IRS PAN (5.8 m). The coarsest is Landsat MSS (80 m). SPOT HRV-II (10 m) is finer than LISS-III (23 m).
  • Resourcesat-2A (PSLV-C36, 7 December 2016): LISS-4 5.8 m, LISS-3 23.5 m, AWiFS 56 m [2].
  • Landsat 8: panchromatic 15 m, multispectral 30 m, thermal 100 m [3]. A panchromatic band is always sharper than the multispectral bands on the same satellite.
  • Cartosat-3: PSLV-C47, 27 November 2019, Sun-synchronous orbit at 509 km, an agile satellite [4].
  • Trap: "Finer spatial resolution gives a wider swath and a shorter revisit." This is wrong. Finer resolution usually means a narrower swath and a longer revisit.

Mains Points

  • Matching the level of detail to the task (GS-III, resources and planning):
  • Fine resolution (LISS-4 at 5.8 m [2], Cartosat-3 [4]) suits cadastral mapping, urban planning and infrastructure.
  • Coarse but wide sensors (AWiFS at 56 m [2]) suit crop forecasts over a whole state.

  • Choosing the wrong level wastes money or misses detail.

  • The trade-off shapes India's satellite strategy:

  • One satellite cannot be fine, wide and frequent all at once.
  • So India runs series and constellations (Resourcesat-2/2A, Cartosat, RISAT) and steers sensors sideways (LISS-4, ±26°, 5-day revisit [2]).

  • Their data reaches users through Bhuvan and the National Geospatial Policy 2022.

  • Finer resolution shows India's growing self-reliance in space:

  • Detail went from 72.5 m (LISS-I, IRS-1A, 1988) to 5.8 m (IRS PAN, LISS-4), and then to Cartosat-3 in 2019 [4].
  • This cuts dependence on foreign imagery for defence, mapping, disaster damage checks and governance.

Related concepts

Read more

Sources

  1. 1Class 11, Ch 6 "Introduction to Remote Sensing" (primary)
  2. 2ISRO — RESOURCESAT-2A mission pageisro.gov.in · tier 1
  3. 3NASA Science — Landsat 8 missionscience.nasa.gov · tier 2
  4. 4ISRO — PSLV-C47/Cartosat-3 press releaseisro.gov.in · tier 1