F. Appel

Application of flood monitoring from satellite for insurances

Flood monitoring from satellite data provides the opportunity to quickly and precisely overview flooded areas. The extent of the flooding and affected areas can be delivered to water authorities, civil protection agencies or insurances. Evaluations include information to facilitate damage assessment, to better estimate risk in future, and to prepare protection measures. For demonstrating the potential of satellite based evaluations, results for the Elbe flood that occurred in central Europe in August 2002 are presented. This flood had a geographical and economic large impact. In order to identify the affected areas, a strip of 450 km along the river Elbe was investigated. Satellite data of different sensor systems (Landsat, SPOT, ERS) had to be analyzed to derive a complete flood extent data set. For different river sections satellite data of different dates were used to perform the best fit to the flood peak along the river. Damage assessment can be supported using the flood extent overlaid with a city map in high resolution. Maps of complete Dresden have been requested by a German insurance company to support their damage assessment. The evaluation of flooded areas further allows improvements for planning as e.g. the declaration of risk areas. Observed flood extents can serve as validation source for simulations of inundations using hydrodynamic models. The combination of additional information to the flood extent layer opens up various applications. Landuse classified in industrial areas, settlements, agriculture, and forest, which were derived from up-to-date satellite images, are intersected with the inundated areas. This intersection results into the detection of affected areas (and economic values), which have to be protected against flood events in future. Multitemporal evaluations using a set of images of different days in succession document the spatial and temporal dynamics of a flood event. They allow to better rate damages as well as consequential costs.

Near-real-time derivation of snow cover maps for hydrological modeling using operational remote sensing data

A fully automatic operational algorithm to derive the snow covered area from optical data will be presented. Snow-cover maps are processed and provided to the user within a few minutes after online reception of NOAA-AVHRR data. Processing consists of calibration, geometric correction and classification, including automatic cloud detection. To get maximum information and in order to reduce the cloud cover, the daily pathes from all available NOAA-AVHRR satellites were analyzed synergistically. For operational applications high geometric accuracy is necessary. This is achieved by integrating an automatic iterative procedure using satellite orbit information and parametric geocoding including a DEM based terrain correction. Further innovations of the algorithm are the threshold dependency on landuse and the classification of areas with expected melting/temperated snow. For operational application, the processing and distribution chain includes a post-classification, snow line detection and quality assessment of the product before delivery. Since late 2001 the automatic generation of snow maps for Southwest Germany is provided as an operational service to flood forecast centers based on NOAA-AVHRR data. Results for the winter seasons 1998/1999 and 2001/2002 show the applicability of the selected methods in the investigated highland area (accuracy: 95%). During the last two winters, the operational service of snow cover maps provided more than 250 products, containing spatial information for the integration in operational water balance modeling.

Operational monitoring of the snow cover dynamics in Southern Germany: capabilities of optical and microwave remote sensing for improved flood forecast

For reliable water balance and flood forecast modeling in snow influenced runoff regimes, frequent observations of the snow cover from operational (fullyautomatic) remote sensing methods are essential. Within this paper the developed methods and obtained results from operational multisensoral remote sensing of snow will be presented and discussed. For operational processing of directly receipted NOAA and near-real-time available ENVISAT data, processing methodologies and software were enhanced to obtain stable and reliable results from heterogeneous image geometries. The joined analysis of ASAR WSM and AVHRR datasets allow the independent determination of snow parameters covering the entire area of the investigated catchments in Southern Germany with sufficient spatial and good temporal resolution.

Snow Water Equivalent of Dry Snow Derived From GNSS Carrier Phases

Snow water equivalent (SWE) is a key variable for various hydrological applications. It is defined as the depth of water that would result upon complete melting of a mass of snow. However, until now, continuous measurements of the SWE are either scarce, expensive, labor-intense, or lack temporal or spatial resolution especially in mountainous and remote regions. We derive the SWE for dry-snow conditions using carrier phase measurements from the Global Navigation Satellite System (GNSS) receivers. Two static GNSS receivers are used, whereby one antenna is placed below the snow and the other antenna is placed above the snow. The carrier phase measurements of both receivers are combined in double differences (DDs) to eliminate clock offsets and phase biases and to mitigate atmospheric errors. Each DD carrier phase measurement depends on the relative position between both antennas, an integer ambiguity due to the periodic nature of the carrier phase signal, and the SWE projected into the direction of incidence. The relative positions of the antennas are determined under snow-free conditions with millimeter accuracy using real-time kinematic positioning. Subsequently, the SWE and carrier phase integer ambiguities are jointly estimated with an integer least-squares estimator. We tested our method at an Alpine test site in Switzerland during the dry-snow season 2015–2016. The SWE derived solely by the GNSS shows very high correlation with conventionally measured snow pillow (root mean square error: 11 mm) and manual snow pit data. This method can be applied to dense low-cost GNSS receiver networks to improve the spatial and temporal information on snow.

Provision of snow water equivalent from satellite data and the hydrological model PROMET using data assimilation techniques

Information on snow cover and snow properties is an important factor for hydrology and runoff modelling. Frequent updates of snow cover information can help to improve water balance and discharge calculations. Within the frame of polar view, snow products from multisensoral satellite data are operationally provided to control and update water balance models for large parts of Southern Germany. Optical AVHRR sensors of the NOAA satellite are used for snow mapping and snow line delineation. Although these acquisitions are available several times per day, cloud cover hinders frequent updates of snow cover maps. As an additional remote sensing data source microwave data from ASAR on ENVISAT is used. Since C-band SAR sensors are only sensitive to snow with a high content of liquid water, the application of ASAR is limited to the melting periods. However under these conditions the developed procedure allows not only to delineate the snow cover in a comparable way as from optical data, also the additional information where the snow is melting is provided. In order to demonstrate how the remote sensing products can be used for improved water balance modelling, an application example for the watershed of the Upper Danube will be presented. This testsite is the research area of the integrative research project GLOWA-DANUBE that is conducted by the University of Munich. Model results using the PROMET-model of snow distributions with and without data assimilation of the remote sensing products will be given. Developed data assimilation concepts will be presented. Through data assimilation, the modelled snow cover agrees better with the mapped snow cover information from satellite. The optimised model provides maps of snow water equivalent, that can not directed be assessed by remote sensing. The impact of data assimilation on the modelled runoff will thus further be analysed.

Refined incidence angle correction for operational soil moisture retrieval from ENVISAT ASAR WSM observations

Within this paper, the improvement of the incidence angle correction of ENVISAT ASAR WSM, based on the analyses of time series of observations in Southern Germany, are described. Based on operational ASAR processing, the soil moisture of the Upper Danube catchment was retrieved. During the analysis of the Wide Swath data, a remaining incidence angle effect, independent from terrain and land surface properties was observed. Based on the data of more than 400 WSM datasets, an empiric correction method was tested and applied to refine the soil moisture retrieval for the area. After this correction, the results of soil moisture comparisons between ASAR observations, SMOS retrieval and station measurements could be improved.