The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Sentinel-2 imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values. The seasonal surface reflectance is of the 6 TM-like bands (Blue, Green, Red, NIR, SWIR1, SWIR2), all at 10 m resolution. This dataset is intended to be a 10 m equivalent of the Landsat surface reflectance, using only Sentinel-2. The two 20m bands are resampled using cubic convolution. <br></br> The pixel values are scaled reflectance, as 16-bit integers. To retrieve physical reflectance values, the pixel values should be multiplied by 0.0001.

Dronescape Dataset Description <br></br> Dronescape is a research infrastructure initiative within TERN that integrates uncrewed aerial vehicle (UAV) remote sensing techniques into Australia's terrestrial ecosystem monitoring framework. The project addresses critical spatio-temporal gaps between field-based measurements and satellite observations by deploying UAV systems carrying RGB, 10-band multispectral, and LiDAR sensors across TERN's surveillance monitoring plot network. Through standardized protocols and automated processing workflows, Dronescape generates analysis-ready products including orthomosaics, reflectance maps, vegetation indices, and 3D point clouds.<br></br> As of 2025, the project has surveyed over 120 plots across multiple bioregions, with ongoing expansion to additional monitoring sites. Each plot covers approximately 90,000 m², contributing to a continuously growing dataset that enables long-term monitoring of Australia's diverse vegetation communities.<br></br> Project Scope <br></br> <ul><li>Coverage: More than 120 plots across multiple bioregions (ongoing expansion) </li> <li>Plot Size: Approximately 90,000 m² per monitoring site </li> <li>Temporal Scale: 2–10 year monitoring cycles </li> <li>Data Types: RGB, 10-band multispectral, and LiDAR</li></ul>