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The organic carbon content of soil is of increasing global interest. Soil is the largest terrestrial store of carbon on the planet, yet many agricultural soils are highly degraded, and a significant source of greenhouse gas emissions. However, appropriate management can mitigate carbon loss and even turn agricultural soils into carbon sinks. Soil Organic Carbon (SOC) content is also a good indicator of soil health: higher SOC levels are typically associated with improved soil structure, moisture retention, and plant nutrient availability. Increasing soil carbon stocks is therefore important from both food security and climate perspectives.Understanding the spatial variability of soil organic carbon (SOC) is critical for accurately monitoring and managing soil carbon stocks. Field-scale soil sampling is widely used to estimate SOC and infer its spatial distribution; however, SOC can vary substantially over short distances, leading to challenges in characterising within-field heterogeneity. When short-range variability is high, low-density sampling and spatial interpolation may fail to capture meaningful structure, resulting in uncertain predictions and misleading estimates of field-scale means.We present a case study wherein we compared hyperlocal (≤10 m) and field-scale soil sampling results. Statistical analyses were applied to evaluate SOC variability across different spatial scales and to assess the significance of hyperlocal soil carbon variability.Results show that hyperlocal variability can closely reflect that observed at the field scale, helping to explain why spatial patterns may not be effectively captured by coarse-scale sampling. Meanwhile, where hyperlocal variance was small, field-scale spatial structure was clearer, making interpolation more defensible. This demonstrates that hyperlocal sampling provides a useful diagnostic for assessing whether field-scale SOC data can meaningfully support spatial modelling.This study highlights the value of incorporating hyperlocal sampling into soil carbon assessments to better capture spatial heterogeneity and improve the reliability of carbon monitoring.