Showing papers by "Patrick Henkel published in 2019"

Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Abstract: For numerous hydrological applications, information on snow water equivalent (SWE) and snow liquid water content (LWC) are fundamental. In situ data are much needed for the validation of model and remote sensing products; however, they are often scarce, invasive, expensive, or labor‐intense. We developed a novel nondestructive approach based on Global Positioning System (GPS) signals to derive SWE, snow height (HS), and LWC simultaneously using one sensor setup only. We installed two low‐cost GPS sensors at the high‐alpine site Weissfluhjoch (Switzerland) and processed data for three entire winter seasons between October 2015 and July 2018. One antenna was mounted on a pole, being permanently snow‐free; the other one was placed on the ground and hence seasonally covered by snow. While SWE can be derived by exploiting GPS carrier phases for dry‐snow conditions, the GPS signals are increasingly delayed and attenuated under wet snow. Therefore, we combined carrier phase and signal strength information, dielectric models, and simple snow densification approaches to jointly derive SWE, HS, and LWC. The agreement with the validationmeasurements was very good, even for large values of SWE (>1,000 mm) and HS (> 3 m). Regarding SWE, the agreement (root‐mean‐square error (RMSE); coefficient of determination (R)) for dry snow (41 mm; 0.99) was very high and slightly better than for wet snow (73 mm; 0.93). Regarding HS, the agreement was even better and almost equally good for dry (0.13 m; 0.98) and wet snow (0.14 m; 0.95). The approach presented is suited to establish sensor networks that may improve the spatial and temporal resolution of snow data in remote areas.

Advances in Snow Hydrology Using a Combined Approach of GNSS In Situ Stations, Hydrological Modelling and Earth Observation—A Case Study in Canada

Abstract: The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015–2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Foret Montmorency near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada.

Precise Point Positioning with Kepler

Abstract: Precise Point Positioning (PPP) enables an absolute positioning with centimeter-level accuracy without the need of raw measurements from a reference station. However, today's GPS L1/ L2-based PPP solutions typically need more than 30 minutes to converge. In this paper, we present a PPP solution with a much faster convergence using the proposed next-generation GNSS Kepler. We exploit the high accuracy of the satellite position, clock offset and bias estimates enabled by highly accurate optical inter-satellite range measurements. We additionally exploit the low noise level of the E1, E5 and E6 pseudorange measurements based on wideband signals as provided already by Galileo. Our PPP solution determines the receiver position and clock offset, tropospheric and ionospheric zenith delays, and pseudorange multipath errors without the need of any prior information. We show that the PPP convergence time can be reduced from more than 30 minutes to less than 5 minutes with Kepler.

Precise Positioning of Robots with Fusion of GNSS, INS, Odometry, LPS and Vision

Abstract: The autonomous driving of robots is coming and requires precise and reliable positioning information with low-cost sensors for the mass market. In this paper, we propose a tightly coupled sensor fusion of multiple complementary sensors including Global Navigation Satellite System (GNSS) receivers with Real-Time Kinematics (RTK), Inertial Measurement Units (IMUs), wheel odometry, Local Positioning System (LPS) and Visual Positioning. The focus of this paper is on the integration of LPS and vision since the coupling of GNSS-RTK, INS and wheel odometry is already state of the art. We include the positions of the LPS anchors and the bearing vectors and distances from the robot's camera towards the patch features as state vectors in our Kalman filter, and show the achievable positioning accuracies.

Visual-Inertial Odometry with Sparse Map Constraints for Planetary Swarm Exploration

Abstract: This paper presents an Unscented Kalman Filter approach to visual-inertial odometry with sparse inequality map constraints. The system setup is motivated by a planetary swarm-exploration scenario, in which agile and light-weight agents (e.g. UAVs) navigate in an environment that was already partially mapped by complementary swarm vehicles (e.g. rovers). It is based on a state-of-the-art monocular system that facilitates state updates via three-view geometrical constraints of matched features. An IMU provides measurements for state prediction between successive updates. Our central contribution to the state-of the-art is the introduction of a method to incorporate inequality constraints on accessible space. These are given in the form of ordered sparse coordinates with associated vectors that identify accessible areas. We present experiments with data from the KITTI Vision Benchmark Suite, which contains all necessary data types acquired by an automotive system, along with simulated map constraints. Our results illustrate the potential and limitations of such sparse inequality constraints to correct for the drift of the odometry system.