Skip to main content
U.S. flag

An official website of the United States government

Evaluation of Landsat image compositing algorithms

December 6, 2022

We proposed a new image compositing algorithm (MAX-RNB) based on the maximum ratio of Near Infrared (NIR) to Blue band (RNB), and evaluated it together with nine other compositing algorithms: MAX-NDVI (maximum Normalized Difference Vegetation Index), MED-NIR (median NIR band), WELD (conterminous United States Web-Enabled Landsat Data), BAP (Best Available Pixel), PAC (Phenology Adaptive Composite), WPS (Weighted Parametric Scoring), MEDOID (medoid measurement), COSSIM (cosine similarity), and NLCD (National Land Cover Database). Each algorithm was applied to time series of Landsat observations collected within two separate years at six locations around the world, to produce monthly (July 1 ± 15 days), seasonal (July 1 ± 45 days), and annual (July 1 ± 180 days) composite images free of cloud, cloud shadow, and snow/ice. By comparing the composite images to reference Landsat images acquired in the growing season (closest to July 1 within ±15 days) for each year, we evaluated the performance of the algorithms in preserving the spectral and spatial fidelity (hereafter referred to as spectral and spatial evaluation, respectively), as well as land cover classification and land change detection (hereafter referred to as application evaluation). The results demonstrated that no single algorithm outperformed all other algorithms in all the evaluations, but that performance depended on compositing intervals and cloud cover. For monthly composites, the MAX-RNB algorithm generally produced the best results in the spectral and application evaluations. For seasonal composites, the NLCD algorithm produced the best results in the spectral and application evaluations. For annual composites, the PAC algorithm produced the best results in the spectral evaluation and change detection, whereas BAP produced the best results in land cover classification. The BAP algorithm also produced the best results in the spatial evaluation for all the compositing periods. This study provides a comprehensive guidance for selecting the most appropriate image compositing algorithm for different Landsat-based applications.

Citation Information

Publication Year 2023
Title Evaluation of Landsat image compositing algorithms
DOI 10.1016/j.rse.2022.113375
Authors Shi Qiu, Zhe Zhu, Pontus Olofsson, Curtis Woodcock, Suming Jin
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing of Environment
Index ID 70241046
Record Source USGS Publications Warehouse
USGS Organization Earth Resources Observation and Science (EROS) Center