Crop Identification in Remote Sensing – What sensor factors matter? In operational crop mapping and discrimination tasks, which sensor characteristics influence our ability to identify crops correctly?

Introduction / Context:
Accurate crop identification from imagery depends on both what the sensor measures and how well it measures it. Spectral content enables discrimination of vegetation traits, spatial resolution resolves field patterns, and radiometric performance preserves subtle reflectance differences across dates.

Given Data / Assumptions:

• Time-series analysis benefits from consistent acquisition geometry and calibration.
• Fields exhibit intra-field variability and mixed pixels at boundaries.
• Atmospheric correction applied for fair comparison across dates.

Concept / Approach:

Spectral bands (for example, red edge, NIR, SWIR) capture biochemical and water-content signatures; spatial resolution determines whether rows, field shapes, and edges are resolved; radiometric SNR ensures that small differences in reflectance are detectable above noise. The combination governs separability in feature space for classifiers.

Step-by-Step Solution:

Verification / Alternative check:

Studies show improved classification accuracy when moving from RGB to multispectral (adding NIR/SWIR) and when using higher SNR sensors at adequate GSD.

Why Other Options Are Wrong:

Any single-factor choice overlooks critical limitations; “none” is false given established sensor design principles.

Common Pitfalls:

Relying solely on spectral bands while ignoring poor SNR or overly coarse GSD; neglecting calibration consistency across time.

Final Answer:

All of these