Document Type

Thesis - Open Access

Award Date

2025

Degree Name

Master of Science (MS)

Department / School

Electrical Engineering and Computer Science

First Advisor

Larry Leigh

Abstract

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band Adjustment Factor (SBAF) estimation, capturing sensor-specific Relative Spectral Response (RSR) variations and introducing the ability to use the multispectral sensorspecific hyperspectral profile for calibrating future satellite sensors like Landsat Next with super-spectral bands. SBAFs were derived from EO-1 Hyperion normalized to multispectral sensors, which were interpolated to 1nm, ensuring precise spectral band adjustments following a Monte Carlo simulation approach for uncertainty quantification. Results show that reference sensor-specific hyperspectral profiles at 1nm spectral resolution improve SBAF accuracy and exhibit total uncertainty within 5.8% across all bands and all sensor pairs with L8 as the reference sensor. These findings demonstrate that integrating reference sensor-specific high-resolution hyperspectral data and stable calibration targets im-proves T2T cross-calibration accuracy, supporting future superspectral missions such as Landsat Next.

Library of Congress Subject Headings

Landsat satellites -- Calibration.
Earth resources technology satellites -- Calibration.
Artificial satellites in remote sensing -- Calibration.
Imaging systems -- Image quality.
Remote sensing -- Data processing.
Artificial satellites -- Stability.

Publisher

South Dakota State University

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Rights Statement

In Copyright