A survival guide to Landsat preprocessing

TLDR

Landsat imagery is widely used for ecological monitoring, but inconsistent terminology and outdated methods make selecting an appropriate preprocessing workflow difficult and can lead to erroneous results. The study aims to clarify Landsat missions, sensors, and terminology to streamline preprocessing choices. The authors detail standard preprocessing steps—georeferencing, radiance conversion, solar, atmospheric, topographic, and relative corrections—and provide decision trees to guide workflow selection based on research questions. They recommend a parsimonious preprocessing workflow that omits unnecessary steps and relies on well‑tested, readily available, and well‑documented methods and data products.

Abstract

Abstract Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time‐consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co‐registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.