Advances in global satellite-based remote sensing, big data, and machine learning are making large-scale geographic analysis increasingly accessible. Archaeology can benefit from multitemporal and multisensor satellite data even when direct archaeological site and feature detection is not possible by using these data to examine relationships between archaeological, ecological, and climactic variables across vast geographies. With this, archaeologists can ask questions about diverse landscape matrices and how these shaped the flow of past cultural processes. We demonstrate the value of thermal sensor data—common in satellite arrays—by developing an automated routine for analyzing Landsat 8 Thermal Infrared Sensor time series data. We fit a harmonic regression to extract amplitude and phase shift values for thousands of inland lakes in Michigan’s lower peninsula and computed perimeter-to-area ratios. Treating these thermal data not as absolute equivalents to past conditions but as relative indices, we compared lakes with and without Late Precontact (ca. 1200–1600 CE) burial mounds. Results show lakes with mounds warmed later in the spring, cooled later in the fall, and were more regularly shaped, suggesting Late Precontact communities placed mounds on lakes with specific resource benefits, including extended growing seasons. Considering our findings alongside emerging evidence for widespread precontact Indigenous agriculture in the Great Lakes region, it is important to reconsider maize’s subsistence and ceremonial significance. The analytic workflow—leveraging free, global Landsat data in Google Earth Engine—offers a replicable framework for archaeologists worldwide to integrate relative thermal patterns into cultural landscape studies.
Howey et al. (2026) studied this question.