Harmonized Tasseled Cap Transformation coefficients for Landsat 8 and 9 OLI sensors using surface reflectance from near-coincident underfly observations

Tasseled Cap Transformation (TCT) is a widely applied remote sensing technique for dimensionality reduction and physical feature enhancement, valued for its interpretability and efficiency. While TCT coefficients have been developed for numerous sensors, no dedicated coefficient set has been proposed to date for Landsat 9 Operational Land Imager-2 (OLI-2) sensor. This study addresses this gap by deriving the first TCT coefficients for Landsat 9 OLI-2 based on surface reflectance data. To ensure cross-sensor consistency, we simultaneously recalculated Landsat 8 Operational Land Imager (OLI) coefficients using strictly matched underfly image pairs and identical sample selection protocols. This harmonized derivation strategy minimizes methodological and sampling-induced discrepancies, enhancing compatibility between the two Landsat sensors. More importantly, the proposed coefficients offer improved performance in capturing wetness-related information across diverse ecological settings. This makes them especially suitable for applications involving soil moisture monitoring, vegetation stress detection, and hydrological modeling in spatially and temporally heterogeneous landscapes. The validation results demonstrate that the newly proposed coefficients effectively enhance spectral differences among surface features with varying brightness, greenness, and moisture content. Moreover, the TCT components from Landsat 8 OLI and Landsat 9 OLI-2 exhibit strong agreement across all three components (R 2 > 0.96, approaching 1), underscoring the high consistency of the derived coefficients. Sensitivity analyses further reveal that the wetness and greenness components remain highly stable under varying sample selection conditions, while the brightness component, though slightly more sensitive, still maintains angular differences within 5°. The results highlight the improved physical consistency and cross-sensor compatibility of the proposed coefficients, facilitating more robust long-term environmental monitoring and multi-source data integration in Earth observation studies.