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1. Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data

   https://doi.org/10.1016/j.rse.2019.111515  

   Bartsch, Annett; Widhalm, Barbara; Leibman, Marina; Ermokhina, Ksenia; Kumpula, Timo; Skarin, Anna; Wilcox, Evan J.; Jones, Benjamin M.; Frost, Gerald V.; Höfler, Angelika; et al (February 2020, Remote Sensing of Environment)
2. Nearshore Bathymetry From Fusion of Sentinel-2 and ICESat-2 Observations

   https://doi.org/10.1109/LGRS.2020.2987778  

   Albright, Andrea; Glennie, Craig (July 2020, IEEE Geoscience and Remote Sensing Letters)
3. Mapping Quaking Aspen Using Seasonal Sentinel-1 and Sentinel-2 Composite Imagery across the Southern Rockies, USA

   https://doi.org/10.3390/rs16091619  

   Cook, Maxwell; Chapman, Teresa; Hart, Sarah; Paudel, Asha; Balch, Jennifer (May 2024, Remote Sensing)

   Quaking aspen is an important deciduous tree species across interior western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (<0.09 ha or 30 m2), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. Using observed annual phenology of aspen across the Southern Rockies and leveraging the frequent temporal resolution of S1 and S2, ecologically relevant seasonal imagery composites were developed. We derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best-performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing products in both accuracy (0.93 average F1-score) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America. 

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4. Modeling Coastal Water Clarity Using Landsat‐8 and Sentinel‐2

   https://doi.org/10.1029/2022EA002579  

   Lang, Sarah E.; Luis, Kelly M. A.; Doney, Scott C.; Cronin‐Golomb, Olivia; Castorani, Max C. N. (July 2023, Earth and Space Science)

   Abstract Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future. 

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5. Arctic Infrastructure: Sentinel-1 and Sentinel-2 derived Arctic Coastal Human Impact dataset, Pan-Arctic Region, 2016 - 2020

   https://doi.org/10.18739/A21J97929  

   Bartsch, Annett; Pointner, Georg; Nitze, Ingmar; Cohen, Juliet; Widhalm, Barbara; Von_Baeckmann, Clemens; Tanguy, Rodrigue (January 2024, NSF Arctic Data Center)

   ### Overview The SACHI (Sentinel-1/2 derived Arctic Coastal Human Impact) dataset has been developed as part of the HORIZON2020 project Nunataryuk by b.geos (www.bgeos.com). V1 covered a 100km buffer from the Arctic Coast (land area), for areas with permafrost near the coast. V2 covers additional selected areas extending the coverage to the south. It is based on Sentinel-1 and Sentinel-2 data from 2016-2020 using the algorithms described in Bartsch et al. (2020). It is a supplement to Bartsch et al. (2023). This dataset contains detected coastal infrastructure separated into seven different categories: linear transport infrastructure (asphalt), linear transport infrastructure (gravel), linear transport infrastructure (undefined), buildings (and other constructions such as bridges), other impacted area (includes gravel pads, mining sites), airstrip, and reservoir or other water body impacted by human activities. This SACHI version 2 dataset was post-processed by the Permafrost Discovery Gateway visualization pipeline. This workflow cleaned, standardized, and visualized the data as two Tile Matrix Sets per year. One Tile Matrix Set is the data in the form of GeoPackages, or staged tiles, and the other Tile Matrix Set is the staged tiles in the form of GeoTIFF tiles. The highest resolution tiles were resampled to produce GeoTIFFs for lower resolutions. This data is visualized on the Permafrost Discovery Gateway portal: https://arcticdata.io/catalog/portals/permafrost/Imagery-Viewer ### References Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., and Pointner, G. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (v2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10160636 Bartsch, A., G. Pointner, I. Nitze, A. Efimova, D. Jakober, S. Ley, E. Högström, G. Grosse, P. Schweitzer (2021): Expanding infrastructure and growing anthropogenic impacts along Arctic coasts. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ac317 Bartsch, A., Pointner, G., Ingeman-Nielsen, T. and Lu, W. (2020), ‘Towards circumpolar mapping of Arctic settlements and infrastructure based on Sentinel-1 and Sentinel-2’, Remote Sensing 12(15), 2368. ### Access Data files output from the visualization workflow are available for download at: [http://arcticdata.io/data/10.18739/A21J97929](http://arcticdata.io/data/10.18739/A21J97929) To download all files in the command line, run the following command in a terminal: `wget -r -np -nH --cut-dirs=3 -R '\?C=' -R robots.txt https://arcticdata.io/data/10.18739/A21J97929/` To download a subdirectory of the archived files, add the subdirectories to the end of the URL above. 

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