The monitoring of soil moisture content (SMC) at very high spatial resolution (<10m) using unmanned aerial systems (UAS) is of high interest for precision agriculture and the validation of large scale SMC products. Data-driven approaches are the most common method to retrieve SMC with UAS-borne data at water limited sites over non-disturbed agricultural crops. A major disadvantage of data-driven algorithms is the limited transferability in space and time and the need of a high number of ground reference samples. Physically-based approaches are less dependent on the amount of samples and are transferable in space and time. This study explores the potential of (1) a hybrid method targeting the soil brightness factor of the PROSAIL model using a variational heteroscedastic Gaussian Processes regression (VHGPR) algorithm, and (2) a data-driven method employing VHGPR for the retrieval of SMC over three grassland sites based on UAS-borne VIS-NIR (399-1001 nm) hyperspectral data. The sites were managed by mowing (Fendt), grazing (Grosses Bruch) and irrigation (Marquardt). With these distinct local pre-conditions we aimed to identify factors that favor and limit the retrieval of SMC.

Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. Based on a valid statistical analysis of the acquired data sets, we derive general suggestions for the planning of a successful field campaign as well as recommendations for a suitable preprocessing workflow.

Questions: Is it possible to map floristic gradients in heterogeneous boreal vegetation by using remote‐sensing data? Does a continuous vegetation map enable the creation of a spatially continuous map of seasonal permafrost soil thaw depth?Location: Bonanza Creek LTER, Fairbanks, Alaska, USA.Methods: Vegetation records are subjected to an ordination to extract the predominant floristic gradient. The ordination scores are then extrapolated using Sentinel 2 imagery and a digital elevation model (DEM). As the relation between vegetation pattern and seasonal thaw depth was confirmed in this study, the spatial distribution of ordination scores is then used to predict seasonal thaw depth over the same area.Results: The first dimension of the ordination space separates species corresponding to moist and cold soil conditions from species associated with well‐drained soils. This floristic gradient was successfully mapped within the sampled plant communities. The extrapolated thaw depths follow the typical distribution along a topographical and geomorphological gradient for this region. Besides vegetation information also DEM derivatives show high contributions to the thaw depth modeling.Conclusion: We demonstrate that floristic gradient mapping in boreal vegetation is possible. The accuracy of the thaw depth prediction model is comparable to that in previous analyses but uses a more parsimonious set of predictors, underlining the efficacy of this approach.

Soil moisture content (SMC) is of fundamental importance to many hydrological, biological, biochemical and atmospheric processes. Common soil moisture measurements range from local point measurements to global remote sensing-based SMC datasets. Nevertheless, they always compromise between temporal and spatial resolution. Thus, it is still challenging to quantify spatially and temporally distributed SMC at a regional scale which is extremely relevant for hydrological modeling or agricultural management. The innovative technology Cosmic-Ray Neutron Sensing (CRNS) shows significant potential to fill this gap by quantifying the present hydrogen pools within footprints larger than 0.1 ha.Owing to the difference in scale between the ground resolution of satellites used to retrieve soil moisture and the common point scale of ground-based soil moisture instruments, the large footprint of the CRNS poses a high potential for the validation of SMC remote sensing products. When linking the CRNS measurements with remote sensing data, the vertical and horizontal characteristics of its footprint need to be considered.To examine the influence of the CRNS footprint characteristics on the linkage of CRNS and remote sensing data, we couple CRNS measurements with high-resolution UAS-based thermal imagery acquired at two sites in Bavaria and Brandenburg (Germany) using a radiometrically calibrated FLIR Tau 2 336 (FLIR Systems, Inc., Wilsonville, OR, USA) with a focal length of 9 mm. Within this context, we evaluate the added value of applying a horizontal weighting function to the spatially distributed thermal data in comparison to an unweighted mean when statistically representing the corrected neutron counting rates.The project is part of the DFG-funded research group Cosmic Sense, which aims to provide interdisciplinary new representative insights into hydrological changes at the land surface.

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