| Popis: |
Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time- and resource-consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982-2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe. We assembled the dataset by sampling individual pixels at regular intervals with 0.18° spacing in the latitudinal and longitudinal directions and reporting the data availability across the complete time depth of Landsat and Sentinel-2 data archives. The dataset comprises separate Landsat- and Sentinel-2-specific data records. To facilitate data exploration the data availability records are accompanied by a growing season information, also sampled at the pixel-level in regular intervals with 0.18° spacing. The dataset was derived based on freely available 1982–2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre-Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001-2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much-needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at the local to global scale. |
