An assessment approach for pixel-based image composites

Remote sensing is one of the main sources of information for monitoring forest dynamics; however, surface reflectance is often not possible to accurately derive due to haze, cloud, or cloud shadow. Pixel-based composites are generated from multi-temporal images to cover the entire area of interest using several different methods. While the availability of free and open remote sensing data has further expanded the use of compositing approaches, to date a comprehensive methodology to assess the quality of these composites does not exist, nor is there a detailed set of compositing requirements to ensure consistent and reliable outputs to produce maps and statistics. Herein, we introduce a pixel-based composite assessment methodology based on five criteria: (i) number of valid observations and number of pixels with no available observations (data gaps), (ii) amount of unscreened clouds, cloud shadows, haze, or smoke (noise), (iii) radiometric consistency of the surface reflectance data, (iv) temporal proximity of pixels acquisition dates, and (v) spatial agreement of pixels acquisition dates. To test our methodology, we processed more than 16,000 Landsat images to generate and assess the Best Available Pixel (BAP) and the Medoid pixel-based composites for summer 2019 (2019-Jun-1 to 2019-Aug-31) over Europe, with a focus on the forested ecosystems. We found that BAP resulted in composites that were more temporally consistent, whereas the Medoid approach resulted in composites that were more radiometrically consistent. Our results illustrate that our assessment approach is effective for comprehensively assessing the quality of pixel-based composites and could be implemented when using composites to generate statistical estimates (i.e. forest area) and for assessing the performance of new compositing algorithms or for selecting an appropriate compositing approach for a specific application.