Bold idea: Landsat calibration is the quiet engine behind trustworthy Earth imagery, and it’s evolving to keep pace with new science and future missions. Here’s a fresh, beginner-friendly rewrite that preserves all key details while offering clearer explanations and a touch more context.
The Landsat Calibration and Validation (Cal/Val) group safeguards Landsat’s standing as the premier reference for satellite imagery. They ensure that the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) aboard Landsats 8 and 9 deliver high-quality, scientifically reliable measurements to users worldwide. In 2025, the Cal/Val team contributed more than 60 pages to the second edition of Comprehensive Remote Sensing, assembling content from NASA, USGS, academia, and industry. Team members wrote multiple sections, including summaries of Landsat 9 results and the evolution of spectral, spatial, and radiometric characteristics across the Landsat program.
At NASA Goddard Space Flight Center, the Cal/Val group works in close collaboration with the Landsat Flight Operations Team to schedule weekly calibration activities that maintain the radiometric accuracy of Landsat products. In October 2025, Landsat 9 experienced an anomaly related to its solar array drive assembly (SADA) potentiometer. The spacecraft and instruments were placed in a safehold, temporarily pausing data collection. After recovery, Cal/Val staff examined instrument telemetry, detector gains, and noise performance. They found a mis-loaded detector map and updated the calibration for both reflective and thermal emissive bands to restore consistent, accurate data. Six days after the safehold, Landsat 9 returned to normal operations.
The NASA Cal/Val team also supports their USGS colleagues with quarterly updates to the Calibration Parameter File (CPF), providing inputs for relative and absolute gains as needed. This effort involves close collaboration with USGS scientists to ensure the consistency of the Combined Radiometric Model (CRaM). CRaM blends radiometric responses from on-board calibrators to improve long-term calibration stability during mission lifetimes, and it also offers an adaptable framework for future satellite missions. A peer-reviewed publication detailing CRaM’s approach and potential applications has been submitted to Science of Remote Sensing.
From January 14–16, 2025, the Landsat Cal/Val team organized the first semiannual Technical Information Meeting (TIM) at NASA Goddard Space Flight Center. NASA and USGS scientists welcomed collaborators from South Dakota State University (SDSU), the University of Arizona, Tucson, and Rochester Institute of Technology, who presented on Landsat imaging performance, algorithms, and instrument health. A second TIM followed on May 28–29, 2025 at SDSU.
The Cal/Val team is validating the Harmonized Landsat and Sentinel-2 (HLS) v2.0 product, which merges data from multiple satellites to produce a continuous record of Earth-surface reflectance since 2013. They are testing the dataset with RadCalNet, a global network of automated ground stations that provide precise, standardized measurements. By comparing measurements from four RadCalNet sites—including the well-known Railroad Valley Playa site in Nevada—with near-simultaneous HLS data, they found strong agreement within expected uncertainty ranges. This result provides robust validation of the HLS product’s accuracy.
These findings were presented at the CEOS IVOS calibration meeting in Tucson, Arizona (September 1–5, 2025), and a peer-reviewed article detailing the full results is in preparation.
Path Forward
The Cal/Val team uses lessons learned from Landsat missions to improve calibration planning for future instruments. Building on performance checklists from Landsat 8/9, they are developing a framework for in-house geometric and radiometric testing and extending algorithms for upcoming Landsat sensors.
A key challenge the team is addressing today is solar irradiance modeling. Advances in hyperspectral sensor technology enable highly accurate solar models with substantially lower uncertainty. However, the remote sensing community still lacks agreed-upon methods to apply these advanced models consistently. A dedicated subgroup within the Landsat Cal/Val Team is creating standardized approaches to bridge this gap. Their goal is to produce clear recommendations and best practices that the scientific community can refine and implement across projects and applications.
In short, this effort aims to turn promising hyperspectral solar modeling into practical, standardized tools that researchers can confidently use, regardless of the project or application.
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