Radiometric Normalization of Multitemporal Landsat and Sentinel-2 Images Using a Reference MODIS Product Through Spatiotemporal Filtering

Abstract

Radiometric normalization is an essential preprocessing step for almost all remote sensing applications such as change detection, image mosaic, and 3-D reconstruction. This article proposes a novel radiometric normalizing method based on spatiotemporal filtering using a reference moderate resolution imaging spectroradiometer (MODIS) product. This differs from traditional relative radiometric normalization (RRN) methods in two folds: first, the number of reference images is more than one, which introduces more complexities than RRN with a single reference image; second, the resolution of MODIS product is significantly lower, thus requiring the algorithms to accommodate scale differences. To address, our approach extends the traditional spatiotemporal filtering method with per image bias that represents both internal (e.g., sensor characteristics) and external (e.g., atmosphere and topography) against the reference data. In addition, we use the Kullback-Leibler divergence metric to statistically determine the resemblance degree between the temporal images for weighting. We applied our proposed method to normalize Landsat Operational Land Imager, Enhanced Thematic Mapper Plus +, and Sentinel MSI using MODIS Nadir BRDF-adjusted reflectance product, covering two study areas of 30\xa0×\xa015\xa0km2 and 32\xa0×\xa052\xa0km2, respectively, and we show a notable radiometric consistency over both temporal and spatial dimension after the processing through three comparative experiments with state-of-the-art methods. 1)\xa03–7% improvement in the contexts of transfer learning, which favors only images with consistent radiometric properties and 2) Mosaic results using our processed images show no apparent seamlines as compared with images processed by other methods.