HARMU: A Multiband Sensor Harmonization for Building Virtual Constellations. Application to Landsat 8 and Sentinel-2

文献类型： 外文期刊

第一作者： Wang, Changjing

作者： Wang, Changjing;Yin, Gaofei;Fu, Rui;Descals, Adria;Descals, Adria;Li, Wenjuan;Weiss, Marie;Verger, Aleixandre;Verger, Aleixandre

作者机构：

关键词： Reflectivity; Landsat; Land surface; Training; Atmospheric modeling; Linear regression; Spatiotemporal phenomena; Spatial resolution; Geology; Earth; Landsat 8; red-edge (RE) band; Sentinel-2; spectral harmonization; virtual constellation

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCI

摘要： The combination of Sentinel-2 multispectral instrument (MSI) and Landsat 8 operational land imager (OLI) creates a virtual constellation of decametric sensors with high revisiting frequency. However, the differences in the spectral characteristics of the two sensors cause inconsistencies in downstream applications. This study proposed a multiband constraint spectral harmonization method called HARMU. In comparison to existing methods, HARMU uses all the spectral bands in the source sensor to predict the reflectance of the targeting sensor and so fully exploits spectral linkage among different bands. HARMU was specifically implemented by Gaussian process regression (GPR), with training data collected from the spatiotemporally representative BEnchmark Land Multisite ANalysis and Intercomparison of Products 2.1 (BELMANIP2.1) sites. We reproduced the top of the canopy reflectance at both common bands of OLI and MSI and also reflectance at red-edge (RE) bands that are only equipped on MSI. The results indicated that HARMU performed satisfactorily with R(2 )larger than 0.91 and Rel-Bias less than 0.19 for all bands over BELMANIP2.1 sites. HARMU offered similar performances as the widely used Harmonized Landsat and Sentinel-2 (HLS) products: average R(2 )slightly improved from 0.86 for HLS to 0.88 for HARMU for the common bands as evaluated over ground-based observations for validation (GBOV) sites, and additionally, it well reconstructs the missing RE band in HLS-based OLI ( R-2>0.81 and Rel-Bias <0.15). HARMU will substantially contribute to generating spatiotemporally continuous time series of decametric data from the MSI-OLI virtual constellation and monitoring vegetation dynamics in large-scale and long-time sequences.

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