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Low Earth Orbit (LEO) satellite-IoT systems increasingly provide connectivity to mobile assets in remote areas, which makes their resilience to radio-frequency jamming a growing concern. Timely jamming detection, i.e., before complete link failure, has been studied for terrestrial networks and Global Navigation Satellite Systems (GNSS), but it remains unclear whether these approaches are effective on LEO links, where propagation varies rapidly, the received signal is weak, and jamming should be detected from the messages that are still correctly received. This paper investigates the extent to which jamming of Iridium LEO satellite-IoT links can be detected from the physical layer of correctly decoded messages. To this aim, we collect and publicly release a real-world dataset of Iridium messages under 47 jamming conditions, spanning two propagation environments, static and mobile receivers, and different jammer distances, powers, and signal types. We use this dataset to analyze three one-class autoencoder-based anomaly detectors operating on physical-layer information and compare them against two signal-quality estimators commonly used as jamming indicators in GNSS-based applications. Our results show that detectability is governed by the rise of the receiver noise floor, through which jammer power, distance, and receiver mobility act. With 1000-sample chunks, i.e., 40 ms of received signal at 25,000 samples per second (about four correctly decoded messages), the autoencoders reach an AUC of at least 0.84 whenever a noise jammer raises the noise floor by at least 9 dB, whereas the $C/N_0$ and SNR estimators fail, since received messages, although correctly decoded, retain a high SNR under jamming. Therefore, timely jamming detection for LEO satellite-IoT links like Iridium should rely on physical-layer information beyond the signal-quality metrics adopted in the GNSS domain.