The Sentinel-1 (S-1) microwave measurements pose a unique opportunity for estimating forest parameters from satellite time series with increased data availability from overlapping orbits. Leveraging data from different orbits comes with varying viewing geometries and acquisition schedules, which can be considered in artificial neural networks.We adapt a transformer architecture for mapping the full S-1 data against median forest height values. By doing so, we propose per-orbit temporal encoders to handle different acquisition times by position, missing data by attention masking, and the addition of viewing geometry context.We show that our adjustments improve performance, with a greater impact of the additional data and a slight improvement in the masking. By optimizing the hyperparameters, our proposed method achieves a preliminary RMSE of 5.9m and an rRMSE of 35% in predicting the per-pixel vertical median forest height.

Digital Elevation Models (DEM) are widely applied to derive the slope steepness and length (LS) factor of the RUSLE in soil erosion assessments. It has been long acknowledged that the DEM grid spacing has a profound impact on the calculated amount of soil loss. In addition, this contribution provides evidence that even within the group of open access, high resolution (i.e., 1 arcsec ~30 m) models, the choice of different DEM can result in considerable soil loss estimate uncertainties of about 25 %.

Microwave scattering from forests generates pixel geolocation shifts in Synthetic aperture radar (SAR) data that require an adequate representation within digital elevation models (DEM) for preprocessing. We analyze the impact of DEM properties on the radiometry and geolocation of radiometric terrain corrected (RTC) Copernicus Sentinel-1 imagery of forests to improve consistency in backscatter intensities for time series analyses. To account for the penetration depth of the C-Band sensor, we approximate the structure of stands in a temperate deciduous forest using height percentiles from Aerial Laser Scanning (ALS) point clouds in the Hainich National Park, Germany. Comparing the RTC results obtained using DEMs of SRTM, Copernicus, and ALS DEMs, the latter reduces topographically induced errors, resulting in visibly smaller effects from topography and spatially shifted information. Based on the P50 ALS vegetation elevation, results show homogeneous intensities within the same orbit and reduce variance from 2.4 dB2to 1.2 dB2in the difference of mid-range data from ascending and descending azimuth directions. Over forest, we observe lower intensities on sensor-facing and increased intensities on away-facing slopes and correlations with the illuminated pixel area (IPA) and local incidence angle. We reduce this bias with linear regressions of intensity on IPA. ALS DEMs in RTC and the proposed regression correction increase the consistency of images across orbits, measured by the inter-orbit range, throughout the selected year at our study site. We suggest the proposed method applies to other areas, requiring further testing under different forest types and topography.

The project “South African Land Degradation Monitor” (SALDi) contributes to the German-South African Programme “Science Partnerships for the Adaptation to Complex Earth System Processes in the Region of Southern Africa” (SPACES) by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. We are utilizing time series information from Sentinel-1 and Sentinel-2 of the European Space Agency (ESA) Copernicus program. The synergetic combination of both satellites hold large potential as both systems measure different properties of the earth's surface. This study analyses the feasibility of detecting breakpoints and land surface changes over time within the six SALDi study sites. The overarching aim is to link the findings of the time series analysis to different land degradation phenomena.

Digital Elevation Models (DEM) represent fundamental data for a wide range of Earth surface process studies. In many countries high resolution DEM are difficult to acquire. Therefore, global data sets are often used for local or regional scale assessments. Here we validate eight different global scale DEM based on differential GNSS ground measurements acquired in the Lowveld savanna of Kruger National Park, South Africa. The global scale DEM include the three TanDEM-X DEM (~12 m, ~30 m, ~90 m posting), the two SRTM DEM (~30 m, ~90 m posting), the ASTER GDEM Vers. 3, the ALOS AW3D30 DEM (~30 m posting) as well as the MERIT DEM (~90 m posting).

This study explores the potential of Landsat-8 and Sentinel-2 imagery for annual grass biomass mapping in savannas. To this end, three wet season image mosaics based on Landsat-8 and Sentinel-2 were created for 2016, 2017 and 2018 over Kruger National Park (KNP), South Africa. For the purpose of calibration and validation, use was made of in situ fuel biomass values measured as part of the yearly veld condition assessment (VCA) in KNP. The satellite and reference data were fed into a random forests machine learning approach to make park-wide predictions of grass biomass and to assess the performance of Landsat-8 and Sentinel-2 predictors (i.e., surface reflectance and the normalized difference vegetation index, NDVI). Examples of the data sets used and biomass maps produced are provided together with the obtained error statistics. The latter suggest that wet season NDVI mosaics from Landsat-8 and Sentinel-2 data enable the creation of fairly reliable, annual maps of fuel biomass for KNP. These new biomass estimates represent a slight improvement over recent mapping efforts based on Sentinel-1 data [1].

In this paper, we propose a methodology for upscaling fractional vegetation cover (FVC) estimates derived from unmanned aerial vehicles (UAVs) to a larger area using freely available Sentinel-1/2 and Landsat-8 satellite data in the semi-arid Nama-Karoo biome of South Africa. To the best of our knowledge, such an approach is still lacking yet critical for understanding human-environment interactions, degradation, and the impacts of climate change in this vulnerable dryland ecosystem. The proposed approach utilizes ultra-high spatial resolution UAV imagery (i.e. <; 5 cm) to develop a high quality FVC product. The resulting product is then used to upscale and validate satellite-based estimates of FVC. The rationale for upscaling UAV estimates to the satellite-scale is not only to cover larger areas but also to exploit historical satellite time-series data, in an attempt to understand past trends and vegetation dynamics in the Nama Karoo-Grassland ecotone. The proposed method is expected to produce the first-ever high-resolution continuous maps of FVC and its changes in the above-mentioned ecosystem. Such information is of vital importance as it could help decision makers to gain a better understanding of the extent at which mechanisms such as bush encroachment, grassland expansion, or degradation are occurring in dryland ecosystems.

The overarching goal of SALDi (South African Land Degradation MonItor) is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes in South Africa. This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping in complex savanna systems, invasive slangbos bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons, field trips and permanently installed soil moisture networks.

The recently released TanDEM-X intermediate digital elevation model (IDEM) product by the German Aerospace Center (DLR) provides elevation models at 12-m spatial resolution and above. It was derived from bistatic synthetic aperture radar (SAR) interferometry using the two TerraSAR-X satellites flying in close constellation. The aim of this study was to validate the vertical accuracy of the IDEM data at study sites in the Kruger National Park, South Africa. Two validation locations are presented: a dried up former reservoir (Nhlanganzwani Dam) characterized by bare soil and grassland and the peninsula between the Sabie and Sand Rivers near Skukuza characterized by dense woody cover. The results show that the IDEM elevation data are affected by residual vegetation canopy height and landform features smaller than the IDEM resolution cell. The scattering phase centers are located in the canopy but at a lower height than the location of the LiDAR first return signal. When only LiDAR ground hits or differential GNSS ground survey points are used for comparison, the IDEM data have an rmse <; 1 m and a small bias of less than 0.25 m in moderate terrain.

This contribution presents the results of a comparison between a TanDEM-X derived Digital Surface Model (DSM) and a high resolution, airborne LIDAR-based Digital Terrain Model (DTM) for a catchment characterized by agricultural areas and forest in Germany. It provides evidence for the overall high quality of the TanDEM-X data and guidelines for uncertainty assessment of TanDEM-X derived DSM in areas where high resolution and high quality data is not available.

The high resolution acquisition mode of TerraSAR-X provides a new dimension in fluvial landform change detection. Here we analyzed high resolution (5 m) coherence images with temporal baselines of up to a year from the Palpa Valley in the hyper-arid coastal desert of southern Peru. The results provide evidence that this sensor is suitable for mapping the land surface changes caused by erosion and sedimentation following rainfall and runoff events in bare desert landscape units.

The meter-scale ground resolution of TerraSAR-X spotlight images promises for the first time the 3-D detection of landforms and landform changes on the microrelief scale from a satellite-based remote sensing system. Using repeat-pass pairs of high-resolution spotlight images, this paper analyzes the spatial variation of coherence on the micro- and mesorelief scale and demonstrates the high potential as well as some limitations of this approach for digital elevation model generation, geomorphological mapping, and geomorphic-change detection in contrasting landscapes of the coastal desert of southern Peru.