Landsat 7's long-term acquisition plan — an innovative approach to building a global imagery archive

The Landsat-7 Long-Term Acquisition Plan (LTAP) automates the selection of Landsat scenes to periodically refresh a global archive of sunlit, substantially cloud-free land images. Its automatic nature reduces the workload on both operations and science office management, and makes the best use of limited resources both on board the satellite and in the ground-processing systems. The core of the LTAP is the definition of seasonality for each scene of interest, where seasonality is defined as the occurrence of change over time. When significant change is occurring, acquisition frequencies are high. Conversely, if little change is occurring, acquisition frequencies are low. This definition of seasonality was augmented with suggested acquisition frequencies from the various science community niches that have historically relied on Landsat data to support their research goals. Cloud climatology for each scene was determined, as monthly historical averages, so it could be compared during scheduling with the daily cloud-cover predictions to discriminate between scenes cloudier than usual (less worthy of acquisition) and scenes clearer than normal (more worthy of acquisition). A scheduler system executes the LTAP and builds the daily schedule by evaluating many factors for each scene, including seasonality, acquisition history, cloud cover, and availability of resources. The result of this evaluation is a list of the “best” 250 scenes to be acquired that day. An important tool in the development of the scheduling system and in the evaluation of the LTAP performance is a modeling capability that allows “what if” analyses to be made and proposed changes to the LTAP or the scheduling software to be evaluated. This modeling capability has proven key to understanding the impacts of potential changes in strategy or software. By considering cloud climatology and current cloud-cover predictions, Landsat 7 has acquired imagery with less cloud contamination than previous Landsat data. After 16 months of operations, a relatively cloud-free global archive exists that captures land-cover change where and when it occurs. Additional user requests have been low, suggesting users are finding suitable data already in the archive. Short-term science campaigns and special requests can be accommodated in the LTAP without perturbing the seasonality of the archive or exceeding the available resources.