Global cross-calibration of Landsat spectral mixture models

TL;DR

This study develops a global cross-calibration method for Landsat spectral mixture models, enabling accurate comparison of land cover fractions across sensors without additional corrections, based on a large spectral dataset from Landsat 7 and 8.

Contribution

It introduces a new global spectral endmember collection and demonstrates that subpixel land cover fractions are consistent across Landsat sensors using this calibration.

Findings

Minor differences in spectral fractions between sensors

High accuracy of spectral unmixing with <5% RMSE for most pixels

Global endmembers improve cross-sensor comparability

Abstract

Data continuity for the Landsat program relies on accurate cross-calibration among sensors. The Landsat 8 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 8 provides an opportunity to cross calibrate the spectral mixing spaces of the ETM+ and OLI sensors using near-simultaneous acquisitions from a wide variety of land cover types worldwide. We use 80,910,343 pairs of OLI and ETM+ spectra to characterize the OLI spectral mixing space and perform a cross-calibration with 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 < u < 0.01) differences between SVD fractions unmixed using sensor-specific EMs. RMS misfit fractions are also small (<98% of pixels with <5% RMSE), in accord with past studies. Finally, vegetation is used as an example to illustrate the empirical and theoretical relationship between commonly used spectral indices and subpixel fractions. SVD fractions unmixed using global EMs thus provide easily computable, linearly scalable, physically based measures of subpixel land cover which can be compared accurately across the entire Landsat 4-8 archive without introducing any additional cross-sensor corrections.