In image interpretation for remote sensing, the arrangement of terrain features that conveys attributes such as shape, size, and texture of objects is referred to as:

Introduction / Context:
Remote sensing imagery is analyzed through multiple cues: spectral, spatial, temporal, and radiometric. Interpreters use these cues to identify land-cover classes, objects, and patterns. Understanding the difference between these dimensions is fundamental to accurate classification and mapping.

Given Data / Assumptions:

• The question points to arrangement-based attributes: shape, size, texture, and pattern.
• We are not focusing on wavelength-dependent reflectance or time-dependent change.
• Terminology follows standard image interpretation texts.

Concept / Approach:
Spatial variation concerns how pixel values and features are arranged in the two-dimensional image plane. It includes geometric properties (shape, size), contextual relationships (pattern, association), and texture (fine vs. coarse). Spectral variation, by contrast, concerns reflectance/emittance differences across wavelengths; temporal variation concerns changes over time; radiometric variation concerns intensity/brightness levels.

Step-by-Step Solution:
Match the descriptors 'shape, size, texture' to the spatial domain.Exclude spectral (wavelength-based) and temporal (time-based) dimensions.Choose 'Spatial variation' as the correct term.

Verification / Alternative check:
Standard interpretation keys list tone/color (spectral-radiometric), texture, pattern, shape, size, shadow, site, and association; the latter set belongs to spatial/contextual analysis.

Why Other Options Are Wrong:

• Spectral variation: about different bands/wavelengths, not layout.
• Temporal variation: about change detection over time.
• Radiometric variation: about brightness levels, not geometric arrangement.

Common Pitfalls:
Confusing texture (spatial) with radiometric noise; conflating color (spectral) with spatial pattern.

Final Answer:
Spatial variation