Frequent droughts increasingly threaten ecosystem stability and agricultural production. Soil moisture is a key indicator of drought, but its spatial coverage remains limited. Remote sensing drought indices provide higher spatial resolution, yet their ability to reflect soil moisture variability has not been systematically assessed. This study evaluates the Vegetation Condition Index, Vegetation Water Index, and Temperature Condition Index by combining Pearson correlation analysis with a Copula-based conditional probability framework to assess their long-term and threshold-based relationships with soil moisture across multiple temporal scales in China. The Vegetation Condition Index shows the strongest correlation with soil moisture at the annual scale and remains dominant during spring, summer, and autumn at shorter time scales. Ecosystem-dependent patterns emerge in summer, with the Vegetation Water Index performing better in forests and the Temperature Condition Index in grasslands, reflecting differences in vegetation density and surface energy processes. The Copula-based analysis reveals a contrasting pattern: across regions, remote sensing indices are less likely to reach extreme or severe drought thresholds than to indicate general drought, suggesting weaker vegetation and temperature responses under extreme soil moisture deficits. Under soil moisture drought conditions, the Vegetation Condition Index shows the highest conditional drought probability in forested regions, whereas the Vegetation Water Index is more responsive in northern arid regions and grasslands, and the Temperature Condition Index shows clearer responses during the growing season. These results indicate that vegetation regulation and legacy effects can weaken synchronous responses during extreme droughts, and that dominant drought signals vary among ecosystems.