A Relative Radiometric Normalization Method for Enhancing Radiometric Consistency of Landsat Time-Series Imageries.

Radiometric consistency of multitemporal satellite observations is affected by sensor stability and scene related issues. Relative radiometric normalization (RRN) is a widely used method to reduce these radiometric differences, its performance depends on the accurate identification of representative pseudoinvariant features (PIFs). However, existing RRN methods are mainly developed for bitemporal images and are limited to time-series imageries due to the complexity of identifying effective PIFs. In this study, we proposed a novel RRN method to enhance the radiometric consistency of Landsat time-series imageries. This method includes the following: first, a trend-based PIFs identification considering land cover changes and phenological trends from the entire time series; second, a PIFs optimization involving an automatic reference selection and a PIFs refining for each reference-target image pair; and third, a combined RRN modeling using the M-estimator sample consensus algorithm and robust linear regression. The Landsat surface reflectance products were used to validate the proposed method. The experimental results showed that the trend-based PIFs identification provided the consistent PIFs for all reference-target image pairs; aided by an automatic reference allocation, PIFs optimization filtered the proper PIFs with high spectral and spatial similarity for each image pair in monthly image stack; the proposed RRN method achieved good performance in model precision and radiance consistency improvement; the proposed RRN method outperformed seven commonly used RRN methods on majority images in image stack of December. The normalized images can help generate more comparable time-series analysis results by reducing the uncertainties from radiometric calibration, atmospheric correction, and sensor differences.