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Landslides of the slide-type movement represent a potential threat to people and infrastructure in mountain areas all over the world. At regional scales, data-driven models are typically used to assess landslide susceptibility, i.e., to map where landslides are more or less likely to occur. Such assessments frequently serve as basic input for landslide risk assessment, applications in spatial planning, and landslide early warning. Data-driven landslide susceptibility models strongly rely on the quality of the landslide inventory data, and therefore, their explanatory power depends on various errors associated with the underlying landslide data collection process. Previous research has highlighted how ignoring systematic errors inherent in the landslide data can lead to erroneous model inference and landslide susceptibility maps with limited practical applicability. In this context, this study aims to counteract the challenge of spatial landslide-inventory biases (e.g., incomplete landslide mapping far from infrastructure) by testing several possibilities to consider information on Effectively Surveyed Areas (ESA). The concept of ESA was introduced in previous research as the areas which were explicitly surveyed during the preparation of the landslide inventory, and hence, landslides occurring outside these areas are not usually reflected in the inventory data. Consequently, accounting for ESA may lead to landslide susceptibility maps more suitable for practical applications. In this contribution, we carried out a comparative analysis of three different landslide susceptibility models that focus on different strategies to handle information on ESA. The analyses focused on the Italian province of South Tyrol (7,400 km²) and built upon landslide data from 2000 to 2020. This data relates to damage-causing and infrastructure-threatening landslide events and therefore ignores slope instability far from the elements at risk. The first tested strategy focused on accounting for the ESA during model fitting while averaging out its effect for the prediction into space. The second strategy builds upon the first strategy but additionally excludes easy-to-classify “trivial” terrain (e.g., alluvial plains, rock faces, water bodies). The third strategy does not account for the ESA during model fitting but considers its information during the preceding sampling process (i.e., the sampling of stable and unstable mapping units is based on an ESA mask). The workflow comprised the delineation of slope units, the derivation of the ESA, and the spatial aggregation of the predisposing factors (e.g., lithology, land cover, topographic indices). The subsequent exploratory data analysis was conducted to explore the association of each factor with landslide occurrence data. Generalized Additive Models were implemented to derive linear or non-linear relationships between shallow landslide occurrence and the predisposing factors and to assess relative variable importance. The results were validated through random cross-validation, spatial cross-validation, and geomorphic plausibility checks. The findings confirmed the importance and benefits of accounting for the ESA. The various results showed how not accounting for the ESA can lead to a misleading depiction of landslide susceptibility in certain areas. This study was framed within the PROSLIDE project, that has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution, only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product’s performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series.

Earth observation time series are well suited to monitor global surface dynamics. However, data products that are aimed at assessing large-area dynamics with a high temporal resolution often face various error sources (e.g., retrieval errors, sampling errors) in their acquisition chain. Addressing uncertainties in a spatiotemporal consistent manner is challenging, as extensive high-quality validation data is typically scarce. Here we propose a new method that utilizes time series inherent information to assess the temporal interpolation uncertainty of time series datasets. For this, we utilized data from the DLR-DFD Global WaterPack (GWP), which provides daily information on global inland surface water. As the time series is primarily based on optical MODIS (Moderate Resolution Imaging Spectroradiometer) images, the requirement of data gap interpolation due to clouds constitutes the main uncertainty source of the product. With a focus on different temporal and spatial characteristics of surface water dynamics, seven auxiliary layers were derived. Each layer provides probability and reliability estimates regarding water observations at pixel-level. This enables the quantification of uncertainty corresponding to the full spatiotemporal range of the product. Furthermore, the ability of temporal layers to approximate unknown pixel states was evaluated for stratified artificial gaps, which were introduced into the original time series of four climatologic diverse test regions. Results show that uncertainty is quantified accurately (>90%), consequently enhancing the product’s quality with respect to its use for modeling and the geoscientific community.