Landsat surface product validation instrumentation: The BigMAC exercise

Sensors
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Abstract

Users of Earth remotely sensed optical imagery are increasingly demanding a surface reflectance or surface temperature product instead of the top-of-atmosphere products that have been produced historically. Validating the accuracy of surface products remains a difficult task since it involves assessment across a range of atmospheric profiles, as well as many different land surface types. Thus, standard approaches from the satellite calibration community do not apply and new technologies need to be developed. The Big Multi-Agency Campaign (BigMAC) was developed to assess current technologies that might be used for validation of surface products derived from satellite imagery, with emphasis on Landsat. Conducted in August, 2021, in Brookings, SD, USA, a variety of measurement technologies were fielded and assessed for accuracy, precision, and deployability. Each technology exhibited its strengths and weaknesses. Handheld spectroradiometers are capable of surface reflectance measurements with accuracies in the 0.01 - 0.02 absolute reflectance units, but are expensive to deploy. Unmanned Aircraft System (UAS)-based radiometers have the potential of making measurements with similar accuracy, but are also difficult to deploy. Mirror-based empirical line methods showed improving accuracy potential, but deployment also remains an issue. However, there are inexpensive radiometers designed for long-term autonomous use that exhibited good accuracy and precision, as well as being easy to deploy. Thermal measurement technologies showed accuracy potential in the 1 - 2K range, and some easily deployable instruments are available. Results from BigMAC indicate there are technologies available today to begin making operational surface reflectance/temperature measurements and strong potential for improvements in the future.
Publication type Article
Publication Subtype Journal Article
Title Landsat surface product validation instrumentation: The BigMAC exercise
Series title Sensors
DOI 10.3390/s25082586
Volume 25
Issue 8
Publication Date April 19, 2025
Year Published 2025
Language English
Publisher MDPI
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 2586, 33 p.
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