Seagrass meadows are one of the most important benthic habitats in the Baltic Sea. Nevertheless, spatially continuous mapping data of Zostera marina, the predominant seagrass species in the Baltic Sea, are lacking in the shallow coastal waters. Sentinel‐2 turned out to be valuable for mapping coastal benthic habitats in clear waters, whereas knowledge in turbid waters is rare. Here, we transfer a clear water mapping approach to turbid waters to assess how Sentinel‐2 can contribute to seagrass mapping in the Western Baltic Sea. Sentinel‐2 data were atmospherically corrected using ACOLITE and subsequently corrected for water column effects. To generate a data basis for training and validating random forest classification models, we developed an upscaling approach using video transect data and aerial imagery. We were able to map five coastal benthic habitats: bare sand (25 km²), sand dominated (16 km²), seagrass dominated (7 km²), dense seagrass (25 km²) and mixed substrates with red/ brown algae (3.5 km²) in a study area along the northern German coastline. Validation with independent data pointed out that water column correction does not significantly improve classification results compared to solely atmospherically corrected data (balanced overall accuracies ~0.92). Within optically shallow waters (0–4 m), per class and overall balanced accuracies (>0.82) differed marginally depending on the water depth. Overall balanced accuracy became worse (<0.8) approaching the border to optically deep water (~ 5 m). The spatial resolution of Sentinel‐2 (10–20 m) allowed delineating detailed spatial patterns of seagrass habitats, which may serve as a basis to retrieve spatially continuous data for ecologically relevant metrics such as patchiness. Thus, Sentinel‐2 can contribute unprecedented information for seagrass mapping between 0 and around 5 m water depths in the Western Baltic Sea.

Submerged aquatic vegetation (SAV) plays an important role in freshwater lake ecosystems. Due to its sensitivity to environmental changes, several SAV species serve as bioindicators for the trophic state of freshwater lakes. Variations in water temperature, light availability and nutrient concentration affect SAV growth and species composition. To monitor the trophic state as required by the European Water Framework Directive (WFD), SAV needs to be monitored regularly. This study analyses the development of macrophyte patches at Lake Starnberg, Germany, by exploring four Sentinel-2A acquired within the main growing season in August and September 2015. Two different methods of littoral bottom coverage assessment are compared, i.e. a semi-empirical method using depth-invariant indices and a physically based, bio-optical method using WASI-2D (Water Colour Simulator). For a precise Sentinel-2 imaging by date and hour, satellite measurements were supported by lake bottom spectra delivered by in situ data based reflectance models. Both methods identified vegetated and non-vegetated patches in shallow water areas. Furthermore, tall- and meadow-growing SAV growth classes could be differentiated. Both methods revealed similar results when focusing on the identification of sediment and SAV patches (R² from 0.56 to 0.81), but not for a differentiation on SAV class growth level (R² <0.42).

&lt;p&gt;Seagrass meadows cover large benthic areas of the Baltic Sea, but eutrophication and climate change imply declining seagrass coverage. Apart from acoustic methods and traditional diver mappings, optical remote sensing techniques allow for mapping seagrass. Optical satellite analyses of seagrass mapping may supplement acoustic methods in shallow coastal waters with observations that are more frequent and have a larger spatial coverage.&lt;/p&gt;&lt;p&gt;In the clear Greek Mediterranean Sea, Sentinel-2 was already applied successfully to detect bathymetry and seagrass meadows. We are now testing whether Sentinel-2 data are also suitable for analysing the sublittoral in the turbid waters of the Baltic Sea. We focus on an extensive shallow water area near Kiel/Germany. Based on Sentinel-2 data, we analyse water depth and differentiate between seagrass covered and bare sandy ground. We derive these parameters using empirical and process-based models. First results show that Sentinel-2 allows to determine water depths up to 4 m (RMSE ~ 0.2 m). Comparisons with LiDAR water depths show that inaccuracies increase in overgrown areas. Our study also shows that the atmospheric correction algorithm influences sublittoral ground mappings with Sentinel-2 data. For instance, the absolute water depths of the process-based modelling differ up to 2.5 m on average depending on the atmospheric correction algorithm (ACOLITE, Sen2Cor, iCOR).&lt;/p&gt;&lt;p&gt;Comparing Sentinel-2 seagrass classifications with diver mappings and aerial imagery emphasises that empiric approaches provide plausible sublittoral ground classifications up to approximately 4 m water depth. Combining these results with seagrass mappings based on acoustic measurements (deeper than 4 m water) provides a synthesised sublittoral classification map of the study area up to the present growth limit of seagrass (~ 7 m in the study area).&lt;/p&gt;&lt;p&gt;The Baltic Sea is considered as a very&amp;#160;turbid environment, nevertheless we show that satellite-based remote sensing has a great potential for shedding light into the&amp;#160; &quot;white ribbon&quot;. The spatial coverage and temporal resolution of the analysed Sentinel-2 data increases the knowledge about the occurrence of seagrass and its spatio-temporal dynamics. Nevertheless, the influence of the selected atmospheric correction approach on the results shows that further research in remote sensing is necessary to assess seagrass meadows reliably.&lt;/p&gt;

Soil compaction results whenever applied soil stress by machinery exceed the soil strength. Both, soil strength and stress, are spatially and temporally highly variable, depending on the weather situation, the current crop type, and the machinery used. Thus, soil compaction risk is very dynamic, changes from day to day and from field to field. The objective of this study was to analyze the spatio-temporal dynamics of soil compaction risk and to identify hot-spot areas of high soil compaction risk at regional scale. Therefore, we selected a study area (∼2,000 km2) with intensive arable farming in Northern Germany, having a high share of cereals, maize and sugar beets. Sentinel-2 images were used to derive the crop types for a 5-years crop rotation (2016–2020). We calculated the soil compaction risk using an updated version of the SaSCiA-model (Spatially explicit Soil Compaction risk Assessment) for each single day of the period, with a spatial resolution of 20 m. The results showed the dynamic changes of soil compaction risk within a year and throughout the entire crop rotation. The relatively dry years 2016 and 2018–2020 reduced the soil compaction risk even at high wheel loads applied to soil during maize and sugar beet harvest. Contrary, high precipitation in 2017 increased the soil compaction risk considerably. Focusing on the complete 5-year period, 2.7% of the cropland area was identified as hot-spots of soil compaction risk, where the highest soil compaction risk class (“extremely high”) occurred every year. Additionally, 39.8% of the cropland was affected by “extremely high” soil compaction at least in one of the 5 years. Although the soil compaction risk analysis does not provide information on the actual extent of the compacted area, the identification of risk areas within a period may contribute to understand the dynamics of soil compaction risk in crop rotation at regional scale and provide advice to mitigate further soil compaction in areas classified as high risk.

&lt;p&gt;This is the most common answer to a common question at the beginning of the semester. Teaching remote sensing skills at university often is associated in physical geography but also geoscience studies. Thus, the topics with which we teach remote sensing skills are often related to these subjects. In undergraduate courses, the thematic interests strongly vary among students. In advanced master courses, students with various thematic and technical backgrounds (geo sciences, computing science, economics, ecology, law, politics etc.) may join remote sensing classes. Additionally, the number of students increases. From the teaching perspective, we aim to address the varying student needs and backgrounds and enable them to further develop their technical skills and have to cope with these challenges. In this presentation, we want to present two practical formats of currently taught remote sensing classes.&lt;/p&gt;&lt;p&gt;In all classes, we work with freely available satellite data (Sentinels, Landsat, MERIS, MODIS etc.) and software (SNAP, QGIS, GoogleEarth, Sentinel-Hub and other browser-based tools). The first class is designed for undergraduate students (geography and related subjects, e.g. geosciences) who have a theoretical remote sensing background (lecture). After completing the class, the students should be able to independently conduct and document a remote sensing processing routine and evaluate results. To this end, the class is split in a part with instructions and a second part with independent work. First, the students work in groups through a modular online implemented course for ten weeks. The modules chronologically follow a basic routine to finally classify land use/ land cover in a study area. The modules contain theoretic background, prepared data, short videos on software usage and broad instructions. To assure the learning process, the students conduct self-tests after completed modules and participate in a weekly on-site tutorial. After completing all modules, they have to independently assess a flood event without detailed instructions and write a fictious report for a catastrophe response unit. Students positively evaluate the split structure, free division of work, videos and self-tests with feedback. Otherwise, they wish more time for asking questions and discuss issues of understanding in the on-site tutorials.&lt;/p&gt;&lt;p&gt;The advanced master course &amp;#8220;Remote Sensing Applications&amp;#8221; is open for students with a basic, practical remote sensing knowledge coming from different master programs. After completing the class, the students should be able to independently process, analyse and discuss remote sensing data and combine them with additional data to work on a geo-/study-related topic (geology, coast, socio-economic, climatic etc.). To this end, we selected New Zealand as study area. Within on-site classes, the students work on the topics geothermics, urban heat islands, droughts, forestry and cloud computing with non-prepared satellite and other data. For the final project, they select a research topic on their own and present their analyses and results in a storymap. Students highly appreciated choosing an own topic for the examination and discussing them in the whole group. &amp;#160;&lt;/p&gt;&lt;p&gt;Here, we aim to reflect the presented classes with the community to further improve our current &amp;#8220;solutions&amp;#8221; for challenges in teaching remote sensing.&lt;/p&gt;