Remote Sensing and GIS for CSIR NET Earth Science: Sensors, Indices and PYQ Strategy

Remote sensing and GIS turn satellite images and spatial data into maps and measurements. In CSIR NET Earth Science this unit is scoring because the questions are mostly conceptual: which wavelength band suits which target, how a sensor works, and what a GIS operation does. Once the basics are clear, these questions are quick marks.

What the syllabus covers

• Electromagnetic spectrum: visible, near-infrared, shortwave infrared, thermal infrared and microwave regions, and how each interacts with the atmosphere and the surface.
• Spectral signatures: reflectance of vegetation, water, soil and rock; the red edge; absorption features of clay minerals and iron oxides; atmospheric windows.
• Platforms and sensors: geostationary and sun-synchronous orbits, passive and active sensors, Landsat, Sentinel, IRS/Resourcesat, Cartosat and other satellite series.
• Resolution types: spatial, spectral, radiometric and temporal resolution, and the trade-offs between them.
• Image processing: radiometric and geometric correction, enhancement, band ratios, NDVI, classification (supervised and unsupervised) and accuracy assessment.
• Microwave and radar: SAR, backscatter, polarisation, look angle and applications such as flood mapping and deformation (InSAR).
• GIS: raster and vector data models, coordinate systems and projections, georeferencing, overlay and buffer analysis, digital elevation models, and GPS/GNSS positioning.
• Applications: lithological and structural mapping, mineral exploration, land use and land cover, water resources, glaciers, coastal change and disaster monitoring.

High-yield topics to prioritise

1. Spectral behaviour of vegetation, water and soil, and which band shows what.
2. The four kinds of resolution and how they trade off against each other.
3. NDVI and other indices: formula, range and meaning.
4. Passive versus active sensing, and what SAR can do that optical sensors cannot.
5. Raster versus vector, and common projections.
6. Supervised versus unsupervised classification.

How to study this unit

• Make one table of bands (blue, green, red, NIR, SWIR, thermal) against the surface features each one highlights.
• Learn the NDVI formula, (NIR − Red) / (NIR + Red), and why healthy vegetation gives high values.
• Practise numerical questions on pixel size, scale and swath width. They are short and repeat in similar forms.
• Solve previous year questions by topic. The same ideas appear again with different wording.

PYQ strategy

Go through the solved previous year questions on this unit first and mark the sub-topics that repeat. Our solved PYQ bank groups questions by topic, so you can revise remote sensing and GIS in one sitting and see the pattern for yourself. Then attempt a mock test to check speed.

Common mistakes

• Confusing spatial resolution with spectral resolution.
• Forgetting that thermal infrared records emitted energy, not reflected sunlight.
• Mixing up the vector and raster models and their typical uses.
• Treating NDVI as a measurement of biomass rather than an index of greenness.

Related guides

See our complete CSIR NET Earth Science syllabus guide for all units, and the eligibility guide if you are planning your attempt.