Description: This Sentinel-2 Level-2 (Bottom-of-Atmosphere) image patches with seasonal information for Central Yakutia and Chukotka vegetation plots dataset in Siberia, Russia, is a part of the SiDroForest data collection. The aim of SiDroForest is to map current vegetation dynamics in the boreal to sub arctic region of Siberia. Sentinel-2 is an ESA optical satellite mission providing satellite imagery globally- and freely-available, which facilitates low-cost large-scale analyses of circumpolar boreal forests. The Sentinel-2 mission is composed of two identical satellites that were launched in 2015 and 2017. Though freely available, Sentinel-2 data often contains clouds and finding a cloud and haze-free acquisition can take time. Therefore, this Data Collection provides cloud free atmospherically corrected image patches of the vegetation plots in three different seasons of the vegetation period in Siberia: early summer, peak summer and summer. The atmospherically corrected Sentinel-2 data were optimized prior to vegetation related analyses: we resampled the spectral bands to 10 m spatial resolution to make them comparable in the same spatial pixel resolution, removing the 60m bands that support atmospheric correction but are not optimal for land surface classification. In addition, for vegetation monitoring it is common to apply the Normalized Difference Vegetation Index (NDVI) that we provide as an additional band. The dataset presented here contains subsets of Sentinel-2 acquisitions that cover all the 54 locations where fieldwork was performed in Siberia during a 2-month fieldwork expedition in 2018 by the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research in Germany (Kruse et al., 2019). 30 by 30m image patches were cropped and given a vegetation label (classes 1-11).

Description: Lake Süßer See is a eutrophic to hypertrophic medium-sized lake (max. depth 8.2 m, mean depth 4.3 m; volume 11.6 Mio. m³, surface area 268 ha) which receives water from the south-eastern foothills of the Harz Mountains and a former mining area for copper shale (Mansfelder Land) by the stream “Böse Sieben” (Becker et al. 2001; Lewandowski et al. 2003). The lake basin represents a sinkhole that has been formed by dissolution of underlying Permian evaporates (Wennrich et al. 2007). High P load by the inflows are a result of extensive fertilizer application in the catchment further supporting the high trophic state of the lake. Although phosphorus removal by aluminum sulfate application between 1976 and 1992 reduced internal P loading from the sediments high external, diffuse-source TP loads prevented substantial water quality improvements (TPlake about 200 µg/L) and annually occurring algae blooms of cyanobacteria persist. This publication series includes datasets collected on Lake Süßer See during the Inland Water Remote Sensing Validation Campaign 2017 (Bumberger et al. 2023).

Description: In this measurement campaign of five water bodies (lakes and reservoirs) several German research groups organised a joint effort to collect a data set for testing, evaluating, and potentially improving the abilities of satellite-based monitoring of water quality in standing waters. The strategy of the campaign is summarised in Figure 1 (documentation "Conceptual design of Inland Water Remote Sensing Validation Campaign 2017") and consists of three independently measured categories of data: (i) satellite-based monitoring, (ii) in situ monitoring, and (iii) bio-optical characterisation. The latter aspect, in particular, was intended in order to go beyond classical comparison of satellite-based and in-situ observations and to enable a more process-oriented and physically-based assessment of the observations made during the satellite overcasts. We concentrated our work on one week in summer 2017 and organised a synoptically measurement campaign on five lakes in Central Germany (Lake Arendsee, Lake Geiseltalsee, Kelbra Reservoir, Rappbode Reservoir, Lake Süßer See, see Tab. 1 in documentation "Main physical and limnological characteristics of the five water bodies from Inland Water Remote Sensing Validation Campaign 2017") based on various field and lab methods. The synoptically approach required the equipment of five sampling teams that are able to work independently from each other. Field- instruments used during the campaign (which required to be available in five sets) had been compared with each other in a separate intercalibration day. All lab-based measurements took place at the central lab of the Helmholtz-Centre for Environmental Research in Magdeburg using methods as outlined in Friese et al. (2014). The five water bodies were intentionally chosen because they reflect a broad range of temperate standing waters with respect to size, depth, trophic state, and the occurrence of cyanobacterial blooms. In addition, also natural and artificial water bodies are reflected by this set of lakes/reservoirs. To our knowledge, this is one of the rare multiple-teams efforts in remote sensing research on water quality making the collection of data in terms of their synoptic evaluation and broad methodological basis particularly useful and valuable.

Description: The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot. ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future. iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species. iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.