Question 1

Snow optics — wavelength band where albedo depends strongly on grain size The semi-infinite snow albedo is approximately proportional to the square root of grain radius most strongly in which spectral band?

Accepted Answer

Introduction / Context: Snow reflectance varies with wavelength and microstructure. In the visible, fresh snow is highly reflective with relatively weak sensitivity to grain size. In the near-infrared (NIR), absorption by ice increases and scattering paths depend sensitively on grain radius, making albedo strongly grain-size dependent. Given Data / Assumptions: Optically thick (semi-infinite) snowpack. Dry snow, neglecting liquid water and soot/dust impurities. Looking for the band where albedo vs. grain size dependence is strongest. Concept / Approach: Radiative transfer models show that in the NIR the absorption coefficient of ice rises, so photons undergo fewer scatterings and the mean free path scales with grain size. Empirical parameterizations often express albedo ∝ sqrt(r_g) or similar functional dependence in NIR windows, capturing the observed rapid albedo decrease as grains coarsen (metamorphism). Step-by-Step Solution: 1) Visible band (0.4–0.8 μm): high albedo, weak grain-size sensitivity.2) NIR (0.8–1.2 μm): strong ice absorption → albedo decreases with larger grains.3) Therefore the strongest proportional dependence to grain radius appears in the 0.8–1.2 μm range. Verification / Alternative check: Snow albedo spectra commonly show steep decline beyond ~0.8 μm; remote sensing of snow grain size exploits NIR/SWIR bands for this reason. Why Other Options Are Wrong: UV and visible bands exhibit much higher, less grain-size-sensitive albedo for clean snow. “None of these” contradicts established snow optics behavior. Common Pitfalls: Ignoring impurity effects (black carbon/dust) which also reduce albedo, especially in visible. Confusing “grain radius” dependence with snow depth dependence. Final Answer: 0.8 to 1.2 μm (near-infrared).

Question 2

Coherence conditions — requirements for perfectly coherent radiation For two waves to be perfectly coherent (ideal case), which set of conditions applies?

Accepted Answer

Introduction / Context: Coherence measures the constancy of phase between waves. In optics and electromagnetics, coherence underpins interference, holography, and many sensing techniques. Perfect coherence is an idealization but helps frame practical requirements for high-contrast interference patterns. Given Data / Assumptions: Idealized “perfect” coherence context. Monochromaticity and controlled geometry are considered. Polarization matching is implicitly satisfied for high-visibility fringes. Concept / Approach: Perfect temporal coherence requires a single frequency (infinite coherence time/length). High spatial coherence requires parallel, collimated beams so that phase across the aperture is predictable. In practice, lasers approximate these conditions; extended sources or finite bandwidths reduce coherence and fringe visibility. Step-by-Step Solution: 1) Enforce single frequency → stable phase over time (temporal coherence).2) Align propagation directions and collimate → stable phase across space (spatial coherence).3) Conclude that all listed conditions support perfect coherence. Verification / Alternative check: Michelson interferometer visibility improves with narrower spectral width and better collimation/alignment, illustrating the role of these conditions. Why Other Options Are Wrong: Selecting any single condition alone does not ensure perfect coherence; the comprehensive set is needed in the ideal case. Common Pitfalls: Overlooking polarization; mismatched polarization reduces interference even with good coherence. Confusing collimation with focusing; focused beams can still interfere but complicate phase control. Final Answer: All of these.

Question 3

Snow albedo — key controlling factors Snow surface albedo varies with multiple environmental and physical parameters. Which set best captures the principal factors?

Accepted Answer

Introduction / Context: Snow albedo controls Earth's surface energy balance in cold regions, shaping melt timing and hydrology. It is not a single number: it depends on wavelength, snow microstructure, illumination geometry, and even the optical thickness of the pack, among other factors like impurities (soot, dust) and liquid water content. Given Data / Assumptions: Clean to moderately impure snow, wide range of sun angles. We focus on first-order dependencies. Semi-infinite vs. shallow packs considered qualitatively. Concept / Approach: Spectrally, snow is highly reflective in visible wavelengths and much less so in near-IR/SWIR. Larger grains increase path lengths and absorption, lowering albedo (especially in NIR). Low sun angles increase effective path length in the surface layers, often reducing apparent albedo. Thin snowpacks allow sub-surface/ground reflectance or absorption to influence the signal, modifying effective albedo relative to deep, optically thick snow. Step-by-Step Solution: 1) Wavelength: define spectral albedo curve (high in visible, low in NIR/SWIR).2) Grain size: coarsening with metamorphism → lower albedo, strongest in NIR.3) Solar zenith angle: larger angles (sun lower) → reduced apparent albedo.4) Thickness: thin packs allow substrate influence; deep packs approach semi-infinite behavior. Verification / Alternative check: Remote-sensing retrievals of snow properties routinely invert NIR bands for grain size and use angular models (BRDF) to account for solar geometry, confirming these dependencies. Why Other Options Are Wrong: Each listed factor is a known control; excluding any one would be incomplete. Common Pitfalls: Ignoring impurity content, which can dominate in visible bands. Assuming a single “albedo value” without specifying band and geometry. Final Answer: All of these.

Question 4

Energy carried by an electromagnetic (EM) wave — dependence on amplitude In wave physics and remote sensing, the time-averaged transported energy (intensity) of an EM wave is directly related to the field amplitude. Choose the correct proportionalit…

Accepted Answer

Introduction / Context: The energy flow in an electromagnetic (EM) wave is described by the Poynting vector. For sinusoidal waves, the time-averaged magnitude (intensity) depends on the amplitudes of the electric and magnetic fields. This question checks whether you know the correct amplitude–energy relationship used throughout optics, antennas, and remote sensing radiometry. Given Data / Assumptions: Plane monochromatic EM wave in a linear, isotropic, non-magnetic medium (air approximation). Electric field peak amplitude E0; magnetic field peak amplitude H0. We compare how the transported energy varies with amplitude. Concept / Approach: Instantaneous Poynting vector S = E × H. For harmonic waves, the time-average intensity I = is proportional to the product of field amplitudes. Since H0 is proportional to E0 in a given medium, intensity scales as E0^2. Thus, transported energy per unit area per unit time varies with the square of amplitude. Step-by-Step Solution: 1) Write S = E × H; magnitude S = E * H for orthogonal fields.2) In a non-magnetic medium, H0 = E0 / η, where η is wave impedance.3) Therefore, I ∝ E0 * (E0 / η) = (1/η) * E0^2 ⇒ intensity ∝ amplitude^2.4) Hence, transported energy is proportional to the square of the amplitude. Verification / Alternative check: Optical irradiance from a source falls off with distance but, at a given point, doubling field amplitude quadruples intensity, consistent with photodetector response in the linear regime. Why Other Options Are Wrong: Proportional to amplitude / square root / cube: incorrect scaling; the correct is square. None of these: Not true because square-law dependence is standard. Common Pitfalls: Confusing field amplitude with power; forgetting that many detectors and link budgets use the square-law relation for EM waves. Final Answer: proportional to the square of the amplitude

Question 5

Electromagnetic spectrum facts — wavelengths and the visible window Pick the correct combined statement about gamma-ray wavelengths, radio-wave wavelengths, and the visible band extent used in remote sensing.

Accepted Answer

Introduction / Context: Remote sensing spans a wide portion of the electromagnetic spectrum (EMR). Understanding approximate wavelength scales helps relate sensor bands (visible, NIR, SWIR, TIR, microwave) to physical interactions including absorption, scattering, and emission. Given Data / Assumptions: Order-of-magnitude wavelength values are acceptable. “Visible” refers to human-vision band ~0.4–0.7 μm. Long-wavelength radio can reach kilometre scales. Concept / Approach: Gamma rays sit at the extreme short-wavelength end (on the order of 10^-12 to 10^-10 m). Radio waves span millimetres to kilometres; very long waves approach 10^6 m. The visible band is a narrow window between ultraviolet and near-infrared, about 0.4–0.7 μm. Step-by-Step Reasoning: 1) Gamma: representative wavelength ~10^-10 m — extremely short.2) Radio: very long waves can be ~10^6 m (≈1000 km) in extreme cases.3) Visible: standard remote sensing window is ~0.4–0.7 μm. Verification / Alternative check: Textbook EMR charts show the visible as a tiny slice, framed by UV below 0.4 μm and NIR above 0.7 μm; radio spans many orders of magnitude. Why Other Options Are Wrong: Each single statement is correct; selecting only one omits the others. None of these: incorrect because all given statements hold. Common Pitfalls: Confusing micrometres (μm) with millimetres (mm); mixing “typical value” with “only value” — spectrum bands cover ranges. Final Answer: All of these

Question 6

Solar radiation through the atmosphere — identify the incorrect statement As solar radiation traverses the atmosphere it may be scattered and absorbed. Which statement below is incorrect?

Accepted Answer

Introduction / Context: Before reaching sensors or the surface, solar radiation interacts with atmospheric molecules, aerosols, and clouds. Recognising these interactions is essential for atmospheric correction and accurate reflectance retrieval in remote sensing. Given Data / Assumptions: Clear or hazy atmospheres introduce scattering and absorption. Wavelength dependence matters (e.g., Rayleigh ~ λ^-4). We assess qualitative correctness, not magnitudes. Concept / Approach: Solar radiation is altered by scattering (Rayleigh, Mie, nonselective) and by selective absorption (e.g., ozone, water vapour). Therefore, a statement claiming it remains unaffected is incorrect. Step-by-Step Reasoning: 1) Scattering redistributes energy angularly and spectrally.2) Absorption removes energy at specific bands (e.g., O3 in UV, H2O in NIR/SWIR).3) Net effect: irradiance at ground and at-sensor radiance differ from exo-atmospheric values. Verification / Alternative check: Blue sky colour and atmospheric windows (8–14 μm) are direct evidence of wavelength-dependent interaction. Why Other Options Are Wrong: gets scattered: True in real atmospheres. gets absorbed partially: True in specific bands. None/All of these: Do not match the targeted incorrect statement. Common Pitfalls: Ignoring aerosols and water vapour; assuming uniform transmittance across wavelengths. Final Answer: remains unaffected

Question 7

Dielectric properties and relative permittivity — pick the correct statement(s) Consider relative permittivity (dielectric constant) and its implications. Which combined statement is correct?

Accepted Answer

Introduction / Context: Dielectric properties govern how materials polarise in an electric field. In electromagnetics and microwave remote sensing, relative permittivity (εr) influences reflection, transmission, and wave speed in media (soil, rock, water). Given Data / Assumptions: εr = ε / ε0 by definition. Relative permittivity is dimensionless. Liquid water at room temperature has εr ≈ 80 (frequency dependent). Conductivity and permittivity trends are discussed qualitatively. Concept / Approach: Relative permittivity compares material response to vacuum. While exact εr and conductivity σ are frequency- and temperature-dependent, materials termed “dielectrics” typically have high εr and low σ (insulators), contrasting with good conductors that have very high σ and effectively infinite complex permittivity at low frequencies. Step-by-Step Reasoning: 1) Define εr = ε / ε0 → dimensionless.2) Note reference values: water ~80 at ~20 °C (decreases with frequency/temperature).3) In practice, higher εr is associated with polarisable, non-conducting media compared with metals which are characterised by conductivity rather than finite εr. Verification / Alternative check: Microwave retrievals of soil moisture rely on the strong contrast between εr of water and dry soil; this enables dielectric-based sensing. Why Other Options Are Wrong: Each individual statement is correct; the comprehensive choice is best. Common Pitfalls: Assuming εr is constant across frequency; ignoring temperature and salinity effects (e.g., seawater has high εr but also notable conductivity). Final Answer: All of these

Question 8

Remote sensing basics — signature, reflectance, and Lambertian surfaces Choose the comprehensive correct statement set about object signatures, reflectance definition, and Lambertian behaviour in remote sensing.

Accepted Answer

Introduction / Context: Objects exhibit diagnostic responses across wavelengths, angles, and polarisations. Recognising these “signatures” and using physically consistent reflectance definitions are foundational to classification and biophysical retrievals. Lambertian behaviour is a useful idealisation for some calibration and modelling tasks. Given Data / Assumptions: Signature encompasses spectral, spatial, temporal, textural cues. Reflectance R = reflected flux / incident flux, bounded [0, 1]. Lambertian surfaces have direction-independent radiance (perfect diffuse). Concept / Approach: Interpretation and algorithms use signatures to distinguish classes. Reflectance normalisation enables comparison across scenes. While real surfaces deviate from Lambertian, the assumption simplifies inversion and BRDF corrections in first order. Step-by-Step Reasoning: 1) Identify signatures → mapping features like vegetation vs water.2) Define reflectance → key for radiometric calibration and indices.3) Recognise Lambertian ideal → constant radiance with cosine law of irradiance. Verification / Alternative check: Targets like Spectralon approximate Lambertian behaviour and are used for calibration. Why Other Options Are Wrong: Any single choice alone is incomplete; all three are correct together. None of these: incorrect because the listed statements are standard. Common Pitfalls: Assuming all natural surfaces are Lambertian; ignoring BRDF effects that cause anisotropy in real data. Final Answer: All of these

Question 9

GIS capabilities versus traditional mapping — identify the incorrect statement Which statement about Geographic Information Systems (GIS) is NOT correct?

Accepted Answer

Introduction / Context: GIS extends traditional cartography by integrating databases, spatial analysis, and modelling. It supports dynamic querying, overlays, and geostatistics that go far beyond static paper maps. Given Data / Assumptions: GIS = data model + spatial operations + cartographic output. Traditional mapping = largely static representation. We must spot the incorrect statement about GIS. Concept / Approach: Statements highlighting analysis (overlay, buffering, network analysis) and environmental applications are correct. Equating GIS with traditional mapping is wrong because GIS is an analytical information system with storage, processing, and modelling capabilities. Step-by-Step Reasoning: 1) Check (b), (c), (d): all describe real GIS capabilities.2) Check (a): incorrectly claims equivalence with traditional mapping.3) Therefore, (a) is the incorrect statement. Verification / Alternative check: Common GIS workflows include raster–vector processing, geocoding, spatial joins, and suitability modelling — none are possible with static maps alone. Why Other Options Are Wrong: (b), (c), (d) accurately reflect GIS features; marking them “not correct” would be false. None of these: incorrect since (a) is clearly wrong. Common Pitfalls: Confusing digital cartography (map drawing) with full GIS (analysis + database + tools). Final Answer: GIS technology is the same as traditional mapping

Question 10

Ocean colour — appearance of waters poor in phytoplankton under sunlight In clear open-ocean conditions with low phytoplankton concentration, what colour does the water typically appear to the human eye?

Accepted Answer

Introduction / Context: Ocean colour depends on absorption and scattering by water molecules, phytoplankton pigments (e.g., chlorophyll-a), coloured dissolved organic matter, and suspended particles. Low-chlorophyll waters exhibit characteristic spectral behaviour that translates to perceived colour. Given Data / Assumptions: Sunlit viewing conditions (no extreme glint). Low phytoplankton concentration and low suspended sediments. Observer perceives bulk water colour away from shallow bottoms. Concept / Approach: Pure water strongly absorbs in the red and reflects/scatters more in the blue, while chlorophyll increases absorption in blue and enhances green reflectance. Therefore, oligotrophic (phytoplankton-poor) waters appear deep blue; productive waters trend greener. Step-by-Step Reasoning: 1) Consider inherent optical properties: water molecules preferentially scatter short wavelengths.2) With little chlorophyll, blue light is less absorbed, so blue upwelling radiance dominates.3) Human eye perceives the ocean as deep blue under these conditions. Verification / Alternative check: Satellite products (e.g., chlorophyll maps) show oligotrophic gyres as dark/deep blue in true-colour imagery. Why Other Options Are Wrong: Red/yellow: Would require high suspended sediments or sunset lighting. Green: Typical of higher chlorophyll waters. None of these: Incorrect since “deep blue” is standard. Common Pitfalls: Confusing shallow sandy bottoms (turquoise) with true open-ocean colour; ignoring atmospheric and sun–sensor geometry effects. Final Answer: deep blue

Question 11

GPS ranging signals — operational L-band frequencies The standard GPS system performs code and carrier ranging using how many L-band frequencies?

Accepted Answer

Introduction / Context: GPS signals occupy L-band (approximately 1–2 GHz) for reliable propagation through the ionosphere and foliage. Classic ranging uses distinct L-band carriers and associated codes for positioning and ionospheric delay mitigation. Given Data / Assumptions: Conventional GPS references the legacy core: L1 and L2. Modern signals (e.g., L5) exist but the canonical answer remains two L-band frequencies for standard dual-frequency operations. Range measurements imply code and carrier observations on these carriers. Concept / Approach: Dual-frequency ranging exploits the dispersive ionosphere: combining measurements on L1 and L2 removes first-order ionospheric error. Additional bands (like L5) enhance performance but the minimum standard for “dual-frequency GPS” is two L-band frequencies. Step-by-Step Reasoning: 1) Identify legacy carriers: L1 (~1575.42 MHz) and L2 (~1227.60 MHz).2) Dual-frequency combination reduces ionospheric delay.3) Therefore, GPS range measurements are traditionally made on two L-band frequencies. Verification / Alternative check: Receiver specifications commonly cite L1/L2 tracking for survey and high-grade navigation; L1-only is single-frequency. Why Other Options Are Wrong: 3 L-band or C-band options: Not representative of classic GPS; C-band is not used for GPS navigation signals. None of these: Incorrect since two L-bands are standard. Common Pitfalls: Confusing modern multi-band GNSS (adding L5/E5) with the minimum dual-frequency requirement. Final Answer: 2 L-band frequencies

Question 12

Structure of an electromagnetic wave — fields, orientation, and polarisation Pick the comprehensive correct statement set about the composition of EM waves, field orientation, and the meaning of polarisation.

Accepted Answer

Introduction / Context: Maxwell’s equations predict coupled electric (E) and magnetic (H) fields forming a transverse wave. Understanding their geometry and polarisation is fundamental in optics, antennas, radar, and remote sensing calibration. Given Data / Assumptions: Homogeneous, source-free medium. Plane wave approximation. Right-handed E–H–k triad. Concept / Approach: In free space, E ⟂ H ⟂ k (propagation direction). For propagation along z, E and H lie in the XY plane. Polarisation specifies how the E-field vector is oriented (and evolves) in time and space (linear, circular, elliptical). Step-by-Step Reasoning: 1) Composition: EM waves comprise E and H fields.2) Orthogonality: E ⟂ H and both are transverse to k.3) Directional example: k along z ⇒ E in XY; H also in XY and orthogonal to E.4) Polarisation: orientation/trajectory of E defines wave polarisation state. Verification / Alternative check: Antenna polarisations (H/V, RHCP/LHCP) demonstrate practical importance for link budgets and scattering behaviour. Why Other Options Are Wrong: Each individual statement is correct; the comprehensive choice is appropriate. Common Pitfalls: Assuming longitudinal components exist in free space plane waves; mixing up polarisation of E vs H (E defines polarisation by convention). Final Answer: All of these

Question 13

Vegetation canopy optics — region with maximum multiple-leaf effect In which part of the reflective optical/infrared spectrum is the effect of multiple leaves in a vegetation canopy (strong internal scattering) most pronounced?

Accepted Answer

Introduction / Context: Vegetation reflectance spectra are shaped by pigment absorption in the visible and by internal leaf–canopy scattering in the near-infrared (NIR). The dramatic reflectance rise near 0.7 μm, often called the red edge, underpins vegetation indices and biophysical retrievals. Given Data / Assumptions: Healthy green vegetation with typical chlorophyll content. Multiple-leaf canopy with gaps and varying leaf angles. Focus on reflective optical/IR, not thermal emission. Concept / Approach: In the visible, pigments (chlorophylls, carotenoids) absorb strongly, limiting multiple scattering. In the NIR, absorption is weak; leaf cellular structure and air–cell interfaces cause strong scattering, elevating canopy reflectance and enhancing multiple interactions between leaves. Step-by-Step Reasoning: 1) Visible (0.4–0.7 μm): strong pigment absorption → low reflectance, limited multiple scattering.2) Near-IR (0.7–1.3 μm): weak absorption + high refractive index contrast → strong internal scattering among leaves.3) Short-wave IR (1.3–2.7 μm): increasing water absorption dampens reflectance and reduces multiple scattering compared with NIR. Verification / Alternative check: Measured leaf and canopy spectra show a sharp increase beyond ~0.7 μm and plateau in NIR; radiative transfer models (e.g., PROSAIL) reproduce this behaviour. Why Other Options Are Wrong: Visible: dominated by absorption, not multiple scattering. SWIR: water absorption bands reduce internal scattering dominance. All of the above: Overstates; NIR is the principal region. Common Pitfalls: Assuming green appearance implies high visible reflectance; most reflectance in vegetation comes from NIR, not the green band. Final Answer: The near-IR (0.7 – 1.3 μm) region

Question 14

Snow Optics – Spectral reflectance behaviour of fresh, pure snow In remote sensing of cryosphere, how does the reflectance of fresh and pure snow vary across the spectrum (visible, near-IR, and longer wavelengths)?

Accepted Answer

Introduction / Context: Fresh, clean snow has a distinctive spectral signature that underpins snow mapping, albedo estimation, and melt monitoring from satellites. Understanding how reflectance changes from the visible through the near-infrared (near-IR) into the shortwave and thermal infrared helps choose optimal bands and interpret seasonal trends in cryospheric products. Given Data / Assumptions: Target: fresh, pure (clean) snow with small grain size. Spectral regions: visible (~0.4–0.7 μm), near-IR (~0.7–1.3 μm), and longer wavelengths (shortwave IR >1.4 μm to thermal IR). Typical solar-reflective remote sensing conditions. Concept / Approach: Snow's high visible reflectance arises from multiple scattering by air–ice interfaces in fine grains. As wavelength increases into the near-IR, ice absorption increases, reducing reflectance. In the shortwave IR beyond ~1.4 μm, strong absorption bands make reflectance low; thermal IR is governed by emission rather than reflection. Step-by-Step Solution: Visible: strong multiple scattering ⇒ high reflectance (bright snow).Near-IR: increasing ice absorption coefficient ⇒ reflectance decreases rapidly.Longer wavelengths (SWIR/TIR): strong absorption and thermal emission dominance ⇒ low apparent reflectance. Verification / Alternative check: Common snow indices (e.g., using a green/near-IR or red/SWIR contrast) exploit snow's bright-in-visible, dark-in-SWIR behaviour for robust discrimination from clouds/soil. Why Other Options Are Wrong: a, b, c each describe a correct portion of the spectrum; hence the combined choice “All of these” is correct. “None of these” contradicts established snow spectra. Common Pitfalls: Confusing dirty/old snow (lower visible reflectance) with fresh snow, or assuming near-IR stays high like vegetation; in snow it falls markedly. Final Answer: All of these

Question 15

Water Optics – Colour shift of turbid (muddy) water with increasing turbidity As turbidity increases, the peak of upwelling radiance in muddy water shifts towards which region of the visible spectrum, often making the water appear brown?

Accepted Answer

Introduction / Context: Water colour in remote sensing reflects the balance of absorption and scattering by water molecules, suspended sediments, coloured dissolved organic matter, and phytoplankton. Turbid (muddy) waters dominated by mineral particles show characteristic spectral shifts valuable for sediment mapping and dredging monitoring. Given Data / Assumptions: Increasing turbidity due to suspended mineral sediments. Observation in the visible spectrum under daylight. Open surface (no strong bottom reflectance). Concept / Approach: Mineral particles enhance backscattering, particularly at longer visible wavelengths, while pure water absorbs more strongly in the red but much less than it absorbs in the blue/green when particle load is high. As particle concentration rises, backscattering dominates, shifting the apparent reflectance peak toward longer wavelengths, giving a yellowish-brown to reddish tone. Step-by-Step Solution: Low turbidity: peak reflectance often in blue-green.Higher turbidity: particle backscatter increases with wavelength within VIS ⇒ spectral peak drifts toward red.Perceptual outcome: brownish appearance driven by elevated red/yellow reflectance. Verification / Alternative check: Empirical red-band turbidity indices and suspended sediment retrievals rely on the monotonic rise of reflectance into the red with particle load. Why Other Options Are Wrong: Green/Yellow: may increase transiently, but the dominant peak for very muddy water shifts further toward red. Blue/Violet: typically suppressed by absorption and less backscatter under high turbidity. Common Pitfalls: Confusing phytoplankton-driven green peaks (biological) with mineral turbidity; mechanisms differ and produce different colour trends. Final Answer: Red

Question 16

Milestones in Aerial Photography – Early developments and first from aircraft Which of the following statements about the history of photography and aerial imaging are correct?

Accepted Answer

Introduction / Context: Aerial photography predates satellites and laid the groundwork for photogrammetry and remote sensing. Knowing the timeline helps contextualize sensor evolution from balloons and kites to powered aircraft and later spacecraft. Given Data / Assumptions: Photography's earliest permanent image dates to 1826–1827 (Nicéphore Niépce). Balloons were used as aerial platforms in the early 1900s (and earlier), providing relatively stable vantage points. Powered aircraft enabled systematic aerial surveys by the 1900s. Concept / Approach: Check each claim against widely cited historical records. While balloon photography began even earlier than 1903, their use in 1903 is still a correct statement (it does not claim to be the first). Wilbur Wright’s 1909 Italian flights are commonly credited with early aircraft-based aerial photographs. Step-by-Step Solution: (a) 1826: aligns with Niépce’s heliograph; accepted in standard histories.(b) Balloons served as platforms in the late 19th and early 20th centuries, therefore their use in 1903 is correct as phrased.(c) 1909: Wilbur Wright performed aerial photography in Italy; recognized in historical notes. Verification / Alternative check: Photogrammetry texts and museum archives list these milestones, with dates varying slightly by source but consistent with the statements’ correctness as written. Why Other Options Are Wrong: “Only (a) and (c)” needlessly excludes (b), which is not false. Common Pitfalls: Interpreting (b) as “first used in 1903” when it merely states they were used in that year; earlier use does not invalidate the statement. Final Answer: All of these

Question 17

Photographic Cameras as Remote Sensors – Spectral coverage limits Which statements correctly describe the spectral response of a conventional photographic camera used as a remote sensor?

Accepted Answer

Introduction / Context: Film and standard silicon-based photographic cameras primarily sense the visible and a small portion of the near-infrared. Understanding their spectral limits clarifies why specialized sensors are needed for SWIR and thermal applications like moisture mapping or temperature retrievals. Given Data / Assumptions: Conventional photographic camera (film or silicon detector). No special IR-extended detectors or cooled arrays. Passive solar-reflective imaging context. Concept / Approach: Typical sensitivity extends from ~0.4 μm to ~0.9 μm. SWIR spans roughly 1.4–3 μm and is outside this response. Thermal IR (≈8–14 μm) is far beyond; cameras designed for visible/NIR cannot detect long-wave thermal emission without special sensors and optics. Step-by-Step Solution: Confirm upper cutoff ~0.9 μm ⇒ near-IR edge only.SWIR not covered ⇒ requires InGaAs, HgCdTe, etc.Thermal IR not covered ⇒ requires microbolometers or cooled photon detectors. Verification / Alternative check: Camera response curves from manufacturers show rapid fall-off beyond ~1.0 μm for standard sensors/films. Why Other Options Are Wrong: Options (a), (b), and (c) are simultaneously true; hence (d) is correct. Common Pitfalls: Assuming “infrared photography” with special films equals SWIR/TIR capability; most IR films are near-IR sensitive only. Final Answer: All of the above

Question 18

Snow Cover – Reflectance and grain size variability Which statements about snow cover on Earth's surface are correct regarding spectral reflectance and physical variability?

Accepted Answer

Introduction / Context: Snow albedo strongly influences surface energy balance and hydrologic forecasts. Remote sensing leverages snow's bright visible reflectance and darker SWIR response, but both are modulated by grain size and impurities such as dust or soot. Given Data / Assumptions: Natural snowpacks of varying age and cleanliness. Spectral regions spanning visible and SWIR. Typical solar illumination conditions. Concept / Approach: Fresh, clean snow is among the brightest terrestrial targets in visible bands. As grains grow (metamorphism) or contaminants accumulate, visible reflectance decreases and SWIR absorption changes, altering indices such as NDSI (which contrasts visible and SWIR bands). Grain size governs path length within ice, directly affecting absorption and scattering. Step-by-Step Solution: Identify baseline: very high visible reflectance versus soil/vegetation.Account for variability: age, melt–freeze cycles, impurities reduce reflectance and increase SWIR absorption.Relate to physics: larger grains increase effective absorption path ⇒ darker appearance in NIR/SWIR. Verification / Alternative check: Operational snow products exploit visible–SWIR contrast; changes in grain size/impurities are monitored using multi-temporal reflectance trends. Why Other Options Are Wrong: Each of (a), (b), and (c) holds for real snowpacks; thus “All of the above” is correct. “None of these” contradicts well-established snow optics. Common Pitfalls: Assuming snow reflectance is constant irrespective of aging; in practice it evolves rapidly with weather and deposition. Final Answer: All of the above

Question 19

Atmospheric Absorption – Ozone absorption maxima (UV region) At which wavelengths does ozone show its maximum absorption of incoming solar radiation?

Accepted Answer

Introduction / Context: Ozone in the stratosphere protects life by absorbing harmful ultraviolet radiation. Remote sensing of ozone and radiative transfer modelling both rely on recognizing where ozone absorption bands lie in the solar spectrum. Given Data / Assumptions: Solar wavelengths spanning ultraviolet to visible. Interest in strong ozone absorption features. Concept / Approach: Ozone exhibits strongest absorption in the Hartley (≈200–300 nm) and Huggins (≈320–360 nm, weaker) bands. The most intense absorption is at wavelengths shorter than 0.3 μm, greatly attenuating UV-C and part of UV-B before reaching the surface. Step-by-Step Solution: Identify Hartley band: ~0.2–0.3 μm ⇒ very strong absorption.Huggins band: ~0.32–0.36 μm ⇒ weaker but notable.Therefore “shorter than 0.3 μm” captures the maximum absorption region. Verification / Alternative check: Surface UV indices and satellite ozone products (e.g., TCO) hinge on the strong sub-0.3 μm attenuation by stratospheric ozone. Why Other Options Are Wrong: “Shorter than 0.2 μm only” is too restrictive. “Greater than 0.3 μm” misses the main absorption peak. “None of these” and “~1.0 μm” contradict ozone’s UV-centric absorption. Common Pitfalls: Confusing ozone absorption with water vapour bands in NIR/SWIR or with Rayleigh scattering that also affects the UV/blue region. Final Answer: Shorter than 0.3 μm (UV-C/UV-B range)

Question 20

Electromagnetic Induction – Naming the law The statement “A time-varying magnetic field produces an electric field” refers to which fundamental law of electromagnetism?

Accepted Answer

Introduction / Context: Remote sensing instruments, antennas, and scanning electronics all rely on Maxwell’s equations. Recognizing which law links changing magnetic fields to induced electric fields is foundational for understanding wave generation and transformer/coil behaviour. Given Data / Assumptions: Time-varying magnetic field B(t). Induced electric field E in space (non-electrostatic). Concept / Approach: Faraday’s law of induction states that a changing magnetic flux induces an electromotive force and a corresponding electric field, quantitatively expressed in integral or differential form (curl E is proportional to the negative time derivative of B). Step-by-Step Solution: Identify cause: dB/dt ≠ 0.Result: rotational electric field E appears (induction).Associate with formal name: Faraday’s law. Verification / Alternative check: Complementary law: Ampère–Maxwell relates changing electric field to magnetic field (displacement current), not vice versa, confirming the specific attribution to Faraday for E from dB/dt. Why Other Options Are Wrong: Newton/Kirchhoff/Coulomb: mechanics/circuit/statics laws not describing induction. Ampère–Maxwell law addresses B from changing E or current density. Common Pitfalls: Swapping Faraday’s and Ampère–Maxwell’s roles when recalling the curl equations. Final Answer: Faraday's law

Elements of Remote Sensing Questions