Derivation of tasseled cap transformation coefficients for GF-6 WFV sensor data

Tasseled cap transformation (TCT), one of the most common methods in image enhancement, has been extensively applied in remote sensing. However, high-resolution satellite sensors (like GF-6 WFV) usually lack short-wave infrared bands, leading to distorted wetness components in TCT coefficients obtained using the conventional Gram-Schmidt (G-S) orthogonalization method. Hence, this study selected 12 GF-6 WFV images covering different regions, temporal phases, and seasons, as well as six synchronous Landsat8 images for wetness component regression, determining the wetness component coefficient of the GF-6 WFV sensor. Furthermore, it employed the inversed G-S algorithm to deduce the brightness, greenness, and other components, deriving the TCT coefficient of the GF-6 WFV sensor. This study found that: ①Adjusting the derivation order of the wetness component in TCT (that is, the derivation of the wetness component comes before that of other components like brightness and greenness) allows more effective derivation of the TCT coefficient of the GF-6 WFV sensor, avoiding the distortion of the wetness component; ②The TCT components of the GF-6 WFV sensor exhibited stable characteristics, with surface features displaying a typical “tasseled cap” distribution in the feature plane composed by various TCT components; ③Despite the differences in band setting and spectral response, GF-6 WFV and Landsat8 OLI sensors manifested high consistency in corresponding TCT components, with a correlation coefficient of up to 0.8.