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Earth observation Low Earth Orbit (LEO) satellites collect enormous amounts of data that needs to be transferred first to ground stations and then to the cloud, for storage and processing. Satellites today transmit data greedily to ground stations, with full utilization of bandwidth during each contact period. We show that due to the layout of ground stations and orbital characteristics, this approach overloads some ground stations and underloads others, leading to lost throughput and large end-to-end latency for images. We present a new end-to-end scheduler system called Umbra, which plans transfers from large satellite constellations through ground stations to the cloud, by accounting for both spatial and temporal factors, i.e., orbital dynamics, bandwidth constraints, and queue sizes. At the heart of Umbra is a new class of scheduling algorithms called withhold scheduling, wherein the sender (i.e., satellite) selectively under-utilizes some links to ground stations. We show that Umbra’s counter-intuitive approach increases throughput by 13-31% & reduces P90 latency by 3-6 X.