scholarly journals SodSAR: A Tower-Based 1–10 GHz SAR System for Snow, Soil and Vegetation Studies

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6702
Author(s):  
Jorge Jorge Ruiz ◽  
Risto Vehmas ◽  
Juha Lemmetyinen ◽  
Josu Uusitalo ◽  
Janne Lahtinen ◽  
...  
Keyword(s):  
Synthetic Aperture ◽  
The Arctic ◽  
Target Range ◽  
Network Analyzer ◽  
Sar Images ◽  
Finnish Meteorological Institute ◽  
Radar Calibration ◽  
Loss Of Coherence ◽  
Better Than ◽  
Meteorological Institute

We introduce SodSAR, a fully polarimetric tower-based wide frequency (1–10 GHz) range Synthetic Aperture Radar (SAR) aimed at snow, soil and vegetation studies. The instrument is located in the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, Finland. The system is based on a Vector Network Analyzer (VNA)-operated scatterometer mounted on a rail allowing the formation of SAR images, including interferometric pairs separated by a temporal baseline. We present the description of the radar, the applied SAR focusing technique, the radar calibration and measurement stability analysis. Measured stability of the backscattering intensity over a three-month period was observed to be better than 0.5 dB, when measuring a target with a known radar cross section. Deviations of the estimated target range were in the order of a few cm over the same period, indicating also good stability of the measured phase. Interforometric SAR (InSAR) capabilities are also discussed, and as a example, the coherence of subsequent SAR acquisitions over the observed boreal forest stand are analyzed over increasing temporal baselines. The analysis shows good conservation of coherence in particular at L-band, while higher frequencies are susceptible to loss of coherence in particular for dense vegetation. The potential of the instrument for satellite calibration and validation activities is also discussed.

scholarly journals Autonomous driving in adverse winter weather conditions

2021 ◽  
Author(s):  
Marjo Hippi ◽  
Timo Sukuvaara ◽  
Kari Mäenpää ◽  
Toni Perälä ◽  
Daria Stepanova
Keyword(s):  
Weather Conditions ◽  
Autonomous Driving ◽  
The Arctic ◽  
Observation System ◽  
Test Track ◽  
Finnish Meteorological Institute ◽  
Driving Mode ◽  
Autonomous Cars ◽  
Road Condition ◽  
Meteorological Institute

<p>Autonomous driving can be challenging especially in winter conditions when road surface is covered by icy and snow or visibility is low due to precipitation, fog or blowing snow. These harsh weather and road conditions set up very important requirements for the guidance systems of autonomous cars. In the normal conditions autonomous cars can drive without limitations but otherwise the speed must be reduced, and the safety distances increased to ensure safety on the roads. </p><p>Autonomous driving needs very precise and real-time weather and road condition information. Data can be collected from different sources, like (road) weather models, fixed road weather station network, weather radars and vehicle sensors (for example Lidars, radars and dashboard cameras). By combining the all relevant weather and road condition information a weather-based autonomous driving mode system is developed to help and guide autonomous driving. The driving mode system is dividing the driving conditions from perfect conditions to very poor conditions. In between there are several steps with slightly alternate driving modes depending for example snow intensity and friction. In the most challenging weather conditions, automatic driving must be stopped because the sensors guiding the driving are disturbed by for example heavy snowfall or icy road.</p><p>Finnish Meteorological Institute is testing autonomous driving in the Arctic vehicular test track in Sodankylä, Northern Finland. The test track is equipped with road weather observation system network including road weather stations, IoT sensors measuring air temperature and humidity along with various communication systems. Also, tailored road weather services are produced to the test track, like precise road weather model calculations and very accurate radar precipitation observations and nowcasting. The developed weather-based autonomous driving system is tested on Sodankylä test track among other arctic autonomous driving testing.</p><p>This study presents the Sodankylä Arctic vehicular test track environment and weather-based autonomous driving mode system that is developed at the Finnish Meteorological Institute.</p>

On the Performance of the MPI Algorithm for the Retrieval of Significant Wave Height From ERS Synthetic Aperture Radar

2003 ◽  
Author(s):  
Nelson Violante-Carvalho ◽  
Ian S. Robinson
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Wave Spectra ◽  
Sar Images ◽  
Significant Wave ◽  
Directional Wave ◽  
Better Than ◽  
Aperture Radar

Spaceborne Synthetic Aperture Radar (SAR) is to date the only source of two dimensional directional wave spectra with continuous and global coverage when operated in the so-called SAR Wave Mode (SWM). Since the launch in 1991 of the first European Remote Sensing Satellite ERS-1 and more recently with ENVISAT millions of SWM imagettes containing detailed spectral information are now available in quasi-real time. This huge amount of directional wave data has opened up many exciting possibilities for the improvement of our knowledge of the dynamics of ocean waves. However the retrieval of wave spectra from SAR images is not a trivial exercise due to the nonlinearities involved in the mapping mechanism. The Max-Planck Institut (MPI) scheme was the first ever proposed and most widely used algorithm to retrieve directional wave spectra from SAR images. In this work significant wave height retrieved from SAR images using the MPI scheme are compared against one year of directional buoy measurements obtained in deep water and against WAM spectra. Our results show that for periods shorter than 12 seconds the WAM model performs better than the MPI method, even considering the fact that the model is used as first guess to the MPI scheme. However, for periods longer than 12 seconds (the part of the spectrum directly observed by SAR) the MPI method performs better than WAM. This is in contrast with the results obtained by Voorrips et al. (2001), who found that the performance of the WAM model is superior even when only the low wavenumber part of the spectrum is considered.

scholarly journals Accurate Antenna Pattern Modeling for Phased Array Antennas in SAR Applications—Demonstration on TerraSAR-X

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Markus Bachmann ◽  
Marco Schwerdt ◽  
Benjamin Bräutigam
Keyword(s):  
Synthetic Aperture ◽  
Active Antenna ◽  
Sar Images ◽  
Phased Array Antennas ◽  
Antenna Steering ◽  
Pattern Modeling ◽  
High Flexibility ◽  
Sar Processing ◽  
Antenna Model ◽  
Better Than

The high flexibility and tight accuracy requirements of today's spaceborne synthetic aperture radar (SAR) systems require innovative technologies to calibrate and process the SAR images. To perform accurate pattern correction during SAR processing, an Antenna Model is used to derive the multitude of different antenna beams generated by active antenna steering. The application of such an Antenna Model could be successfully demonstrated for the TerraSAR-X mission, launched in 2007. The methodology and the results of the inorbit verification with an achieved accuracy of better than  dB is reviewed in this paper in detail showing its outstanding accuracy.

scholarly journals Bias-adjusted seasonal forecasts of soil moisture for forestry applications in Finland

2020 ◽  
Vol 17 ◽  
pp. 23-27
Author(s):  
Otto Hyvärinen ◽  
Ari Venäläinen ◽  
Andrea Vajda
Keyword(s):  
Soil Moisture ◽  
Bearing Capacity ◽  
Economic Value ◽  
Timber Harvesting ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Finnish Meteorological Institute ◽  
Adverse Conditions ◽  
Better Than ◽  
Meteorological Institute

Abstract. Seasonal forecasts for forestry have been developed in the Finnish Meteorological Institute in cooperation with Finnish end-users. Such forecasts could help forest companies in preparing for adverse conditions of timber harvesting operations. Bearing capacity for harvesting operations is dependent on soil moisture, and skillful forecasts have potentially large economic value. Using the ECMWF seasonal forecasts, we evaluated the monthly mean soil moisture forecasts for four different start months, with lead times from 0 to 2 months. Forecasts were bias adjusted, and showed skill in the first month for all four months. After the first lead month, winter months fared a bit better than summer months.

scholarly journals Nordic Snow Radar Experiment

2016 ◽  
Vol 5 (2) ◽  
pp. 403-415 ◽  
Author(s):  
Juha Lemmetyinen ◽  
Anna Kontu ◽  
Jouni Pulliainen ◽  
Juho Vehviläinen ◽  
Kimmo Rautiainen ◽  
...  
Keyword(s):  
European Space Agency ◽  
Winter Season ◽  
Test Site ◽  
Microwave Emission ◽  
The Arctic ◽  
Research Centre ◽  
Easy Access ◽  
Finnish Meteorological Institute ◽  
Meteorological Institute

Abstract. The objective of the Nordic Snow Radar Experiment (NoSREx) campaign was to provide a continuous time series of active and passive microwave observations of snow cover at a representative location of the Arctic boreal forest area, covering a whole winter season. The activity was a part of Phase A studies for the ESA Earth Explorer 7 candidate mission CoReH2O (Cold Regions Hydrology High-resolution Observatory). The NoSREx campaign, conducted at the Finnish Meteorological Institute Arctic Research Centre (FMI-ARC) in Sodankylä, Finland, hosted a frequency scanning scatterometer operating at frequencies from X- to Ku-band. The radar observations were complemented by a microwave dual-polarization radiometer system operating from X- to W-bands. In situ measurements consisted of manual snow pit measurements at the main test site as well as extensive automated measurements on snow, ground and meteorological parameters. This study provides a summary of the obtained data, detailing measurement protocols for each microwave instrument and in situ reference data. A first analysis of the microwave signatures against snow parameters is given, also comparing observed radar backscattering and microwave emission to predictions of an active/passive forward model. All data, including the raw data observations, are available for research purposes through the European Space Agency and the Finnish Meteorological Institute. A consolidated dataset of observations, comprising the key microwave and in situ observations, is provided through the ESA campaign data portal to enable easy access to the data.

Sampling, filtering and analyzing procedures for thermal-optical OCEC analysis to determine black carbon, organic carbon and total carbon concentrations in Arctic snow, ice and water samples

2020 ◽  
Author(s):  
Outi Meinander ◽  
Enna Heikkinen ◽  
Minna Aurela
Keyword(s):  
Organic Carbon ◽  
Black Carbon ◽  
Water Samples ◽  
Total Carbon ◽  
Light Transmittance ◽  
The Arctic ◽  
Arctic Circle ◽  
Finnish Meteorological Institute ◽  
Center Of Excellence ◽  
Meteorological Institute

<p>Seemingly small amounts of black carbon (BC) in snow, of the order of 10–100 parts per billion by mass (ppb), have been shown to decrease its albedo by 1–5 %. Due to the albedo-feedback mechanism, surface darkening accelerates snow and ice melt and contributes to Arctic warming.</p><p>Here we present the most recent procedures we use for sampling, filtering and analysis of Arctic snow, ice and water samples, to determine their black carbon (BC), organic carbon (OC) and total carbon (TC) contents. For the purpose, we apply the OCEC analyzer of the Finnish Meteorological Institute’s aerosol laboratory, Helsinki, Finland (60°12 N). Particles are collected on a quarz-fiber filter and subjected to different temperature ramps following the protocols (NIOSH-870, EUSAAR2, or IMPROVE). Pyrolysis correction is by laser transmittance. Light transmittance through the filter is monitored during the collection phase to quantify BC. The OCEC thermal-optical method is the current European standard method for determination of atmospheric BC.  </p><p>Our Arctic samples include surface snow and snow profile samples collected north of the Arctic Circle at the Finnish Meteorological Institute Arctic Space Center in Sodankylä, Finland (67◦37 N, 26◦63 E), which is also a World Meteorological Institute’s Global Atmospheric Watch station (WMO GAW). In addition, samples from H2020 EU-Interact stations of Faroes FINI, Iceland Sudurnes and UK Cairngorms, and elsewhere from Iceland and Finland, including Helsinki Kumpula SMEAR-III station (60°12 N, 24°57 E, Station for Measuring Ecosystem-Atmosphere Relations, https://www.atm.helsinki.fi/SMEAR/index.php/smear-iii) and the most northern research catchment area of Pallas (68°N, about 130 km north from the Arctic Circle, https://blogs.egu.eu/divisions/hs/2019/06/19/featured-catchment-series-pallas/), have been sampled and analyzed. The BC concentrations have been detected to vary according to the origin of the air masses and as a result of the seasonal snow melt process.</p><p><em>Acknowledgements. We gratefully acknowledge support from the EU-Interact-BLACK-project Black Carbon in snow and water (H2020 Grant Agreement No. 730938); the Academy of Finland NABCEA-project of Novel Assessment of Black Carbon in the Eurasian Arctic (No. 296302), Ministry for Foreign Affairs of Finland IBA-project Black Carbon in the Arctic and significance compared to dust sources (No. PC0TQ4BT-25); the Academy of Finland Center of Excellence program The Centre of Excellence in Atmospheric Science - From Molecular and Biological processes to The Global Climate (No. 272041), and The Nordic Center of Excellence CRAICC Cryosphere–Atmosphere Interactions in a Changing Arctic Climate.</em></p><p> </p><p> </p>

scholarly journals Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1250 ◽  
Author(s):  
Alexandre Bouvet ◽  
Stéphane Mermoz ◽  
Marie Ballère ◽  
Thierry Koleck ◽  
Thuy Le Toan
Keyword(s):  
Time Series ◽  
Detection Rate ◽  
Large Scale ◽  
Detection Method ◽  
Earth Observation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Shadowing Effect ◽  
Potential Exploitation ◽  
Better Than

To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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