WISDOM: GNSS-R based flood monitoring

Beckheinrich, Jamila; Beyerle, G.; Schoen, Steffen; Apel, Heiko; Semmling, Maximilian; Wickert, Jens

doi:10.1109/gnssr.2012.6408257

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…A wide range of applications has been reported in the literature for GNSS-R including: water basins detection (e.g., [18,19]), river level monitoring (e.g., [20]), estimation of the surface roughness and wind retrieval (e.g., [21,22]), measurement of the soil moisture (e.g., [23][24][25][26][27]), altimetry (e.g., [28,29]), monitoring the presence of vegetation (e.g., [30,31]), and estimation of snow/ice thickness (e.g., [32]).…”

Section: Introductionmentioning

confidence: 99%

UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study

et al. 2019

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Signals from global navigation satellite systems (GNSS) can be utilized as signals of opportunity in remote sensing applications. Geophysical properties of the earth surface can be detected and monitored by processing the back-scattered GNSS signals from the ground. In the literature, several airborne GNSS-based passive radar experiments have been successfully demonstrated. With the advancements in small unmanned aerial vehicles (UAVs) and their applications for environmental monitoring, we want to investigate whether GNSS-based passive radar can provide valuable geospatial information from such platforms. Low-cost GNSS reflectometry sensors, developed using commercial of the shelf components, can be mounted onboard UAVs and flown to sense environmental parameters. This paper presents the results of a preliminary study to investigate the feasibility of utilizing data collected by UAV-based GNSS-R sensors to detect surface water for a potential application in supporting flood monitoring operations. The study was conducted in the area surrounding the Avigliana lakes in Northern Italy. The results show the possibility of detecting small water surfaces with few tens of meters resolution, and estimating the area of the lake surface with 92% accuracy. Furthermore, it is proved through simulations that the use of multi-GNSS increases this accuracy to about 99%.

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Section: Introductionmentioning

confidence: 99%

UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study

et al. 2019

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“…The phase ambiguity limits the retrieval's accuracy. Ambiguity solutions have been proposed for ground-based phase altimetry [Treuhaft et al, 2001;Belmonte Rivas and Martin-Neira, 2006;Beckheinrich et al, 2012]. The surface topography, that is important for airborne phase altimetry, adds a new challenge.…”

Section: Discussionmentioning

confidence: 99%

Sea surface topography retrieved from GNSS reflectometry phase data of the GEOHALO flight mission

Semmling

Beckheinrich

Wickert

et al. 2014

Geophysical Research Letters

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Sea surface topography observations are deduced from an airborne reflectometry experiment. A GNSS (Global Navigation Satellite System) receiver dedicated for reflectometry was set up aboard the German HALO (High Altitude Long Range) research aircraft. Flights were conducted over the Mediterranean Sea about 3500 m above sea level. A signal path model divided into large‐ and small‐scale contributions is used for phase altimetry. The results depict geoid undulations and resolve anomalies of the sea surface topography. For the whole experiment 65 tracks over the Mediterranean Sea are retrieved and compared with a topography model. Tracks differ between right‐handed and left‐handed circular polarization. The difference, however, is not significant for this study. Precision and spatial resolution decrease disproportionately at low elevations. Eight tracks with centimeter precision are obtained between 11° and 33° of elevation. At higher elevation angles the number of tracks is significantly reduced due to surface roughness. In future such retrievals could contribute to ocean eddy detection.

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“…A and B are both primarily inundated swamp, with B hosting greater amounts of trees, and C primarily observed as a seasonally inundated forest. The GWF-AGB density and γ (Figure 13b) both generally increase across the transect and appear strongly correlated, with the last three points (16)(17)(18) exhibiting an indication of high biomass. Transect measurements demonstrated positive relationships among vegetation structure metrics, with observed LAI and canopy closure shown in Figure 12a, and each of these compared to GFW-AGB in Figure 12b,c respectively.…”

Section: Biometry Measurements and Biomass Estimationmentioning

confidence: 93%

“…5-Janurary-2015 18-Januray-2015 16 16-Feberary-2015 1- March-2015 22 11-May-2015 24-May-2015 25 22-June-2015 5-July-2015 27 20-July-2015 2-Aug-2015 30 31-August-2015 13-Septmember-2015 33 12-October-2015 25-October-2015 36 23-November-2015 6-December-2015 39 4-Janurary-2016 17-Januray-2016 42 15-Feberary-2016 28-Feberary-2016 45 28…”

Section: Appendix a Additional Remote Sensing Dataset Informationmentioning

confidence: 99%

Assessing L-Band GNSS-Reflectometry and Imaging Radar for Detecting Sub-Canopy Inundation Dynamics in a Tropical Wetlands Complex

et al. 2018

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Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth's surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined.biogeochemical processes such as the generation of atmospheric methane and the outgassing of carbon dioxide [2,3]. These flooding regimes can be impacted by future changes in rainfall, evapotranspiration and land use. The ability to accurately monitor the current state and changes in inundation extent would enable further examination of potential climatological and anthropogenic tipping points in these wetlands regions.Satellite remote sensing is the only practical approach that can provide insight into the spatial and temporal dynamics of wetlands on a large scale on a continuous basis. Although they can offer high spatial resolution and sensitivity to photochemical properties of vegetation, optical sensors are severely limited in capturing inundation dynamics in tropical wetlands due to frequent cloud cover and difficulties detecting sub-canopy inundation [4,5]. Microwave sensors, on the other hand, are not significantly affected by clouds or changing solar illumination and are able to observe processes below vegetation canopy. However, tradeoffs exist in the use of microwave remote sensing with respect to...

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WISDOM: GNSS-R based flood monitoring

Cited by 11 publications

References 6 publications

UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study

UAV-Based GNSS-R for Water Detection as a Support to Flood Monitoring Operations: A Feasibility Study

Sea surface topography retrieved from GNSS reflectometry phase data of the GEOHALO flight mission

Assessing L-Band GNSS-Reflectometry and Imaging Radar for Detecting Sub-Canopy Inundation Dynamics in a Tropical Wetlands Complex

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