Usable Observations over Europe: Evaluation of Compositing Windows for Landsat and Sentinel-2 Time Series

Abstract

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data for studying land cover and land use change (LCLUC) over the past four decades. However, the availability of cloud-, shadow-, and snow-free observations varies spatially and temporally due to climate and satellite data acquisition schemes. Spatio-temporal heterogeneity poses a major issue for some time-series analysis approaches, but can be addressed with pixel-based compositing that generates temporally equidistant cloud-free or near-cloud free synthetic images. Although much consideration is given to methods identifying the ‘best’ pixel value for each composite, determining the aggregation period receives less attention and is often done arbitrary, or based on expert intuition. Here, we evaluated data compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel‑2 time series across Europe. We considered separate and joint use of both data archives and analyzed spatio-temporal availability of composites during each calendar year and pixel-specific growing season. We reported mean annual composites’ availability investigating differences among biogeographical regions, checked feasibility of pan‑European analyses for three LCLUC applications based on annual, monthly and 10-day composites, and analyzed the shortest feasible compositing window ensuring ≥50% temporal data availability and interpolation of the remaining composites for individual years and across a variety of medium- and long‑term time windows. Our results highlighted low data coverage in the 1980s, 1990s, and in 2012, as well as spatial variability in data availability driven by climate and orbit overlaps, which altogether impact spatio-temporal consistency of medium- and long-term time series, limiting feasibility of some LCLUC analyses. We demonstrated that prior to 2011 monthly composites ensured overall 50-62% data coverage for each calendar year, and ~75% afterwards, with further increase to ~82% when Landsat and Sentinel-2 were combined. Temporal consistency of monthly composites was overall low and temporal interpolation augmenting up to 50% missing data each year and across a time window of interest, ensured feasibility of analyses. Applications based on shorter than monthly composites were challenging without joining Landsat and Sentinel‑2 archives after 2015, and beyond the Mediterranean biogeographical region. Using pixel-specific growing season data typically boosted data availability in most geographies and diminished most of the latitudinal differences, but feasibility of complete time series with sub-monthly compositing windows was still restricted to the most recent years, and required data interpolation. Overall, our analyses provided a detailed assessment of Landsat and Sentinel-2 data availability over Europe, and based on selected application examples, highlighted often lacking spatio-temporal consistency of time series with sub-monthly compositing windows and long-time periods, which might hinder feasibility of some LCLUC applications.

Global Overview of Usable Landsat and Sentinel-2 Data for 1982–2023<em> </em>

“…The article describes the dataset in the linked repository, which comprises the 1982-2023 global overview of daily availability of cloud-, snow-, and shade-free free Landsat and Sentinel-2 observations [7]. The data were sampled over land at the pixel level in a regular 0.18° x 0.18°-point grid defined in the EPSG:4326 projection and span area between -179.8867°W and 179.5733°E and -59.05167°S and 83.50834°N, totalling 475,150 points.…”

“…Specifically, inexperienced users often struggle to find a suitable parameterisation of their analysis workflows. Recognizing the existing information gap, we decided to derive a global 1982-2023 overview of cloud-, snow-, and shade-free Landsat and Sentinel-2 observations for a regular 0.18° x 0.18°-point grid [7]. The dataset provides a readily available overview of the usable data coverage, thus supporting, for example, the informed selection of algorithms and compositing windows, and aiding the parametrization of specific vegetation-focused analyses.…”