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Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios.

This is the English version of theWhite Paper Citizen Science Strategy 2030. This White Paperpresents a strategy with 94 action recommendations in 15 thematic fieldsfor Germany to strengthen Citizen Science by 2030 in order to unleash its innovative potential for science, society and policy. &nbsp

Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest. The currently developed App FieldMApp offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki. In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%, recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices.

Estimation of forest aboveground biomass is crucial for carbon accounting and forest management. In this paper we present the estimation of forest above ground biomass derived from a combination of Sentinel-1 and Sentinel-2 data using the Random Forest Regression approach. The research was conducted over a temperate forest in Poland, where the forest biomass is ranging from 1 to 300 t/ha. The results revealed a saturation effect around 200 t/ha. The biomass model tends to overestimate small values of aboveground biomass and underestimate larger values of biomass (greater than 250 t/ha). The overall RMSE value is equal to 60 t/ha, however it varies for biomass ranges. The independent validation performed at the forest stand level showed the best agreement for biomass ranging for 100–200 t/ha. This research was conducted as part of the ESA funded DUE GlobBiomass project.

Precise forest inventory data are requested by a wide range of users such as scientists, politicians, administrators, forest owners, or the forest industry. One forest inventory parameter of great importance is the forest stem volume (or growing stock volume, GSV). On the one hand, GSV is related to the monetary value of a forest. On the other hand, the amount of bound carbon can be estimated based on GSV. For the determination of the GSV the stem diameter (usually diameter at breast height, DBH), the tree height, the number of trees per unit area, and a species and forest stand specific form factor are required. In forestry, sample based approaches are used to gather these parameters. For minimizing effort and expense, the number and dimensions of these samples are small compared to the total forest area. Also, the repeat time between two inventories is rather large (in the order of ten years). Accordingly, relative GSV errors of approximately 20% have to be accepted. There exists a great interest to minimize both, effort and inventory errors. Precise inventory data are of particular interest in the research domain. For instance, satellite based methods aiming at GSV estimation suffer from inaccurate reference measurements, as the inventory errors propagate to the final satellite based estimates. Airborne light detection and ranging data (LiDAR) can be utilized to detect single trees and to measure the corresponding tree heights with sufficient accuracy for forestry applications. In some Scandinavian countries forest inventories are supported by LiDAR campaigns by default. Moreover, most European countries execute regular and country-wide LiDAR acquisitions, thus LiDAR based tree height measurements could be achieved. For instance, the LiDAR campaign repetition rate in Germany is five years. However, the stem diameter cannot be measured using airborne LiDAR data. Although some technical ground- and low altitude airborne solutions have been proposed, currently the most efficient approach is manual DBH measurement. The simplicity of DBH measurements makes this task an excellent citizen science exercise. To assess the achievable DBH measurement precision, an experiment involving students of a secondary school was carried out in late 2017. The test site “Roda Forest” is located 20 km in the Southeast of Jena. The selected stand is dominated by pine with an age of 60 years. The reference data for the experiment was generated by means of a terrestrial laser scanner (TLS). Based on the TLS data the precise location and the GSV of approximately 200 trees were delineated. The students were equipped with a smartphone application to localize the single trees. During the campaign the circumference of approximately 100 trees was determined using simple measuring tape. These measurements were converted to DBH after the field campaign. The measured DBH varied between 7 cm and 38 cm. In overall, TLS-based and student campaign based measurements were in great agreement (R² = 0.98). Nevertheless, the identification of the correct trees by the students during the campaign was challenging, which was related to general orientation difficulties and a weak GPS signal underneath the forest canopy. This resulted in a remarkable offset between GPS-based and real coordinates. Forthcoming campaigns have to deal with this issue. One option we will explore in the future is the absolute calibration of the GPS signal using checkpoints with precise coordinates.

Biomass retrieval from SAR is one of the main applications of radar remote sensing. Different sensor configurations have been tested in recent years. They are ranging from single polarization SAR data to fully polarimetric and tomographic SAR data sets either at one radar band or at different radar bands. However, it must be said that especially fully polarimetric or multi-band sensors are mostly limited to experimental campaigns. Operational space borne SAR missions offer SAR data at one radar band with mostly on like (HH/VV) and one cross-polarization (HV/VH). Algorithms to retrieve biomass information from these data sets comprise simple regression models as well as sophisticated semi-theoretical radar backscatter models. Since the 1990s, SAR data were regularly acquired by the space agencies (ESA, JAXA, CSA). Latest with the advent of the ESA SAR sensors using the ScanSAR technique (ENVISAT mission with the ASAR instrument and the Copernicus Sentinel-1 satellite series with the C-SAR instrument), C-band data coverage of land surfaces increased substantially. Thus, multi-temporal to hyper-temporal data sets became available. They have been exploited successfully for biomass mapping from regional to global scales using change detection methodologies. Even the SAR data acquired by the latest SAR sensors are still containing ambiguous information. Besides the geo-physical variable of interest (e.g. biomass), other environmental factors may have an influence on the backscattered signals. Soil moisture is one of these environmental factors. There are authors, who assume that even at C-band (radar band used by ESA SAR satellites since the 1990s) the water content of the soil layer below forests may have an effect on the radar backscatter at C-band. Since 2015, the Department of Remote Sensing at the University of Jena maintains a measurement network with 240 sensors measuring soil moisture and soil temperature in a managed forest in Thuringia, Germany. In parallel, all Sentine-1a and b data sets acquired over the region are collected. Additional meteorological information will be used for data interpretation and analysis. We intend to present the results of our ongoing research activities and try to answer the question, if an influence of soil moisture below forest on C-band radar backscatter has been found for our test site region. This may also help with the interpretation and analysis of biomass maps produced by the ESA DUE GlobBiomass and the ESA CCI Biomass projects, where the department has been and is involved.

This paper elaborates on recent advances in the use of ScanSAR technologies for wetland-related research. Applications of active satellite radar systems include the monitoring of inundation dynamics as well as time series analyses of surface soil wetness. For management purposes many wetlands, especially those in dry regions, need to be monitored for short and long-term changes. Another application of these technologies is monitoring the impact of climate change in permafrost transition zones where peatlands form one of the major land cover types. Therefore, examples from boreal and subtropical environments are presented using the analysed ENVISAT ASAR Global mode (GM, 1 km resolution) data acquired in 2005 and 2006. In the case of the ENVISAT ASAR instrument, data availability of the rather coarse Global Mode depends on request priorities of other competing modes, but acquisition frequency may still be on average fortnightly to monthly depending on latitude. Peatland types covering varying permafrost regimes of the West Siberian Lowlands can be distinguished from each other and other land cover by multi-temporal analyses. Up to 75% of oligotrophic bogs can be identified in the seasonal permafrost zone in both years. The high seasonal and inter-annual dynamics of the subtropic Okavango Delta can also be captured by GM time series. Response to increased precipitation in 2006 differs from flood propagation patterns. In addition, relative soil moisture maps may provide a valuable data source in order to account for external hydrological factors of such complex wetland ecosystems.