Global cross-calibration of Landsat spectral mixture models

Data continuity for the Landsat program relies on accurate cross-calibration among sensors. The Landsat 8 Operational Land Imager (OLI) has been shown to exhibit superior performance to the sensors on Landsats 4-7 with respect to radiometric calibration, signal to noise, and geolocation. However, improvements to the positioning of the spectral response functions on the OLI have resulted in known biases for commonly used spectral indices because the new band responses integrate absorption features differently from previous Landsat sensors. The objective of this analysis is to quantify the impact of these changes on linear spectral mixture models that use imagery collected by different Landsat sensors. The 2013 underflight of Landsat 7 and Landsat 8 provides an opportunity to cross calibrate the spectral mixing spaces of the ETM+ and OLI sensors using near-simultaneous acquisitions of radiance measurements from a wide variety of land cover types worldwide. We use 80,910,343 pairs of OLI and ETM+ spectra to characterize the Landsat 8 OLI spectral mixing space and perform a cross-calibration with Landsat 7 ETM+. This new global collection of Landsat spectra spans a greater spectral diversity than those used in prior studies and the resulting Substrate, Vegetation, and Dark (SVD) spectral endmembers (EMs) supplant prior global Landsat EMs. We find only minor (− 0.01 < μ < 0.01) differences between SVD fractions for coregistered pairs of spectra unmixed using the new sensor-specific endmembers identified in this analysis. Root mean square (RMS) misfit fractions are also shown to be small (< 98% of pixels with < 5% RMS), in accord with previous studies using standardized global endmembers. Finally, vegetation is used as an example to illustrate the empirical and theoretical relationship between commonly used spectral indices and subpixel fractions. We include the new global ETM+ and OLI EMs as Supplementary Materials. SVD fractions unmixed using global EMs thus provide easily computable, linearly scalable, physically based measures of subpixel land cover area which can be compared accurately across the entire Landsat 4-8 archive without introducing any additional cross-sensor corrections.