Vision foundation models for Earth observation (EO) are commonly evaluated on clean benchmarks, but operational EO products can already contain spatial, radiometric, alignment, noise, and harmonization defects before reaching the model. Generic image corruptions and broad domain shifts do not isolate these product-level failures. We introduce \textbf{RSPDBench}, a physically grounded \textbf{r}emote-\textbf{s}ensing-\textbf{p}roduct \textbf{d}egradation \textbf{b}enchmark that evaluates seven foundation-model entries and two supervised baselines on five EO datasets under primitive degradations and compound product chains. Its degradation parameters follow published Sentinel-2 and Landsat~8 specifications and are checked against Sentinel-2's paired $10\,\mathrm{m}$ and $20\,\mathrm{m}$ near-infrared bands. We evaluate each model under its own native protocol and measure robustness as the drop from its own clean baseline. Our analysis reveals that degradation sensitivity is strongly structured: (i) the resolution-conditioned encoder is the most robust to resolution loss yet the most sensitive to radiometric gain on satellite products, (ii) the same physical defect can hurt one model while helping another, and (iii) model rankings under resolution loss and radiometric gain are largely reversed. Compound chains add up to $38$ percentage points of drop beyond their strongest component. On m-BigEarthNet, retraining the probe on degraded views removes about 75\% of the noise and harmonization losses, but resolution loss still costs about 20\% of the clean score. EO robustness must therefore be measured against the structured defects of remote-sensing products, not by clean accuracy or generic perturbations alone. Code: https://github.com/Tanjim131/RSPDBench

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