Calibration of SIR-C/X-SAR data, equipment and planned operation of the Oberpfaffenhofen test site

2002 ◽  
Author(s):  
F. Heel ◽  
H. Ottl ◽  
M. Zink
Keyword(s):  
Test Site ◽  
Sar Data

scholarly journals AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

2018 ◽  
Vol XLII-1 ◽  
pp. 451-456 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
G. Bilgin
Keyword(s):  
Classification Accuracy ◽  
Alpha Angle ◽  
Test Site ◽  
Classification Performance ◽  
Support Vector ◽  
Observation Data ◽  
Data Set ◽  
Crop Classification ◽  
Sar Data ◽  
Spatio Temporal

<p><strong>Abstract.</strong> Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (<span style="text-decoration: overline">&amp;alpha;</span>) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H<span style="text-decoration: overline">&amp;alpha;</span>, (2) H<span style="text-decoration: overline">&amp;alpha;</span>Span, (3) H<span style="text-decoration: overline">&amp;alpha;</span>A, (4) H<span style="text-decoration: overline">&amp;alpha;</span>ASpan and (5) coherency [<i>T</i>] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&amp;alpha;ASpan (91.43<span class="thinspace"></span>% for SVM, 92.25<span class="thinspace"></span>% for RF and 90.55<span class="thinspace"></span>% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25<span class="thinspace"></span>% by RF and H&amp;alpha;ASpan while lowest classification accuracy was obtained as 66.99<span class="thinspace"></span>% by NB and H&amp;alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.</p>

The SARSense campaign: A dataset for comparing C- and L-band SAR backscattering behaviour to changes of soil and plant parameters in agricultural areas

2021 ◽  
Author(s):  
David Mengen ◽  
Keyword(s):  
Soil Moisture ◽  
Canopy Structure ◽  
Cosmic Ray ◽  
Test Site ◽  
Temporal Consistency ◽  
Satellite Mission ◽  
Root Crops ◽  
L Band ◽  
Sar Data ◽  
Airborne Sar

&lt;p&gt;With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe at L-band (ROSE-L) and its combination with existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. To investigate the potential for estimating soil and plant parameters, the SARSense campaign was conducted between June and August 2019 at the agricultural test site Selhausen in Germany. In this regard, we introduce a new publicly available, extensive SAR dataset and present a first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. The analysis includes C- and L-band airborne recordings as well as Senitnel-1 and ALOS-2 acquisitions, accompanied by in-situ soil moisture measurements and plant samplings. In addition, soil moisture was measured using cosmic-ray neutron sensing as well as unmanned aerial system (UAS) based multispectral and temperature measurements were taken during the campaign period. First analysis of the dataset revealed, that due to misalignments of corner reflectors during the SAR acquisition, temporal consistency of airborne SAR data is not given. In this regard, a scene-based, spatial analysis of backscatter behaviour from airborne SAR data was conducted, while the spaceborne SAR data enabled the analysis of temporal changes in backscatter behaviour. Focusing on root crops with radial canopy structure (sugar beet and potato) and cereal crops with elongated canopy structure (wheat, barley), the lowest correlations can be observed between backscattering signal and soil moisture, with R&amp;#178; values ranging below 0.35 at C-band and below 0.36 at L-band. Higher correlations can be observed focusing on vegetation water content, with R&amp;#178; values ranging between 0.12 and 0.64 at C-band and 0.06 and 0.64 at L-band. Regarding plant height, at C-band higher correlations with R&amp;#178; up to 0.55 can be seen compared to R&amp;#178; up to 0.36 at L-band. Looking at the individual agricultural corps in more detail, in almost all cases, the backscatter signals of C- and L-band contain a different amount of information about the soil and plant parameters, indicating that a multi-frequency approach is envisaged to disentangle soil and plant contributions to the signal and to identify specific scattering mechanisms related to the crop type, especially related to the different characteristics of root crops and cereals.&lt;/p&gt;

scholarly journals Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Test Site ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Phenological Stages ◽  
Sar Images ◽  
Growing Period ◽  
Entropy Parameter ◽  
Sar Data ◽  
Type Parameter

<div>Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier's parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological</div><div>stages.</div>

scholarly journals APPLICATIONS OF SENTINEL-1 SYNTHETIC APERTURE RADAR IMAGERY FOR FLOODS DAMAGE ASSESSMENT: A CASE STUDY OF NAKHON SI THAMMARAT, THAILAND

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1927-1931 ◽  
Author(s):  
G. Dadhich ◽  
H. Miyazaki ◽  
M. Babel
Keyword(s):  
Land Surface ◽  
Local Economic Development ◽  
European Space Agency ◽  
Weather Conditions ◽  
Economic Loss ◽  
Test Site ◽  
Flood Damage ◽  
Recurrent Event ◽  
Sar Data

<p><strong>Abstract.</strong> Flooding is one of the major disasters occurring in various parts of the world. Estimation of economic loss due to flood often becomes necessary for flood damage mitigation. This present practice to carry out post flood survey to estimate damage, which is a laborious and time-consuming task. This paper presents a framework of rapid estimation of flood damage using SAR earth observation satellite data.</p><p>In Nakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province, especially the urban area. Every year, it causes lives and damages to infrastructure, agricultural production and severely affects local economic development. In order to monitor and estimate flood damages in near-real time, numerous techniques can be used, from a simply digitizing on maps, to using detailed surveys or remote sensing techniques. However, when using the last-mentioned technique, the results are conditioned by the time of data acquisition (day or night) as well as by weather conditions. Although, these impediments can be surpassed by using RADAR satellite imagery. The aim of this study is to delineate the land surface of Chian Yai, Pak Phanang and Hua Sai districts of that was affected by floods in December 2018 and January 2019. For this case study, Sentinel-1 C-Band SAR data provided by ESA (European Space Agency) were used. The data sets were taken before and after the flood took place, all within 1 days and were processed using Sentinel Toolbox. Cropland mapping has been carried out to assess the agricultural loss in study area using Sentinel-1 SAR data. The thematic accuracy has been assessed for cropland classification for test site shows encouraging overall accuracy as 82.63 % and kappa coefficients (&amp;kappa;) as 0.78.</p>

scholarly journals Novel Clustering Schemes for Full and Compact Polarimetric SAR Data: An Application for Rice Phenology Characterization

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Heather McNairn ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Test Site ◽  
Unsupervised Clustering ◽  
Degree Of Polarization ◽  
Phenological Stages ◽  
Sar Images ◽  
Growing Period ◽  
Entropy Parameter ◽  
Sar Data ◽  
Type Parameter

<div>Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier's parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological</div><div>stages.</div>

Comparison of L- and C-Band SAR data in the Saar Mining District, Germany

2021 ◽  
Author(s):  
Andre C. Kalia ◽  
Volker Spreckels ◽  
Thomas Lege
Keyword(s):  
Ground Motion ◽  
Rural Areas ◽  
Test Site ◽  
Hard Coal ◽  
Mining District ◽  
Synthetic Aperture Radar Data ◽  
Surface Deformations ◽  
Active Coal ◽  
L Band ◽  
Sar Data

&lt;p&gt;The interferometric utilization of Synthetic Aperture Radar data from L-band and C-band has an important role for the monitoring of land surface deformations like former evaluations have proven [1]. Meanwhile several multi-sensor ground-stations are available, equipped with bi-directional artificial corner-reflectors (CR) and permanent GNSS stations, attached to fine leveling baselines. The long wavelength of L-band SAR missions like ALOS-2 (&amp;#955; = 22.9 cm) provides highly coherent interferograms, but here large-sized CR are required e.g. for absolute motion calibration. SAR missions with shorter wavelengths, like the C-band onboard the Sentinel-1 mission (&amp;#955; = 5.6 cm) provide, in general, less coherent interferograms, but a smaller CR size is sufficient. In order to assess the capabilities of L- and C-band SAR data the impulse response function will be calculated at corner-reflector sites and the coherence will be estimated in rural areas of the Saar test site.&lt;/p&gt;&lt;p&gt;The test site is located in the Saar-Lorraine coal basin at the French-German border, a nowadays post-mining district with highly urbanized settlements as well as large stretches of forested and rural areas. The area is characterized by century long active deep mining &amp;#8211; mainly for hard coal &amp;#8211; including extensive groundwater management measures. Here, the active coal mining started in the 18&lt;sup&gt;th&lt;/sup&gt; century and ended in 2006 (Lorraine) and 2012 (Saar) [2]. Meanwhile some of the underground mines got progressively flooded. As a consequence surface uplift occurred and is expected to be ongoing in the near future [3]. For a 12 by 14 km area in the Saar district dense and highly accurate leveling campaigns have been performed bi-annually since 2013. Thus, besides good knowledge of subsurface geology and mining activities also precise in-situ measurements of the ground motion are available. The recent and ongoing surface deformations will be monitored using multiple methods including a network of CR at multi-sensor ground stations [4] and publicly accessible Persistent Scatterer Interferometry datasets from the Sentinel-1 based Ground Motion Service Germany [5].&lt;/p&gt;&lt;p&gt;In late 2020 first ALOS-2 acquisitions of the Saar area from the ESA-JAXA cooperation were made available to the authors. The ALOS-2 data are evaluated and placed in relation to Sentinel-1 acquisitions. Finally, an outlook on the possible complementary use of geodetic and C- and L-band data in the Saar district as well as for other mining areas in Germany is given.&lt;/p&gt;&lt;p&gt;[1] Wegmueller et al. 2005: Monitoring of mining induced surface deformation using L-band SAR interferometry. IGARSS 2005; DOI&lt;strong&gt;: &lt;/strong&gt;10.1109/IGARSS.2005.1526447&lt;/p&gt;&lt;p&gt;[2] Corbel et al. 2017: Coal mine flooding in the Lorraine-Saar basin: experience from the French mines. IMWA 2017. https://www.imwa.info/docs/imwa_2017/IMWA2017_Corbel_161.pdf&lt;/p&gt;&lt;p&gt;[3] Heitfeld-Schetelig 2016: Gutachten zu den Bodenbewegungen im Rahmen des stufenweisen Grubenwasseranstiegs in den Wasserprovinzen Reden und Duhamel. http://www.bid.rag.de/bid/PDFs/SA//GWA_Reden_Duhamel/3_IHS_Bodenbewegungen/IHS_Saar_Gelaendehebungen_WH_Reden_Duhamel_2016_04_20.pdf&lt;/p&gt;&lt;p&gt;[4] Spreckels et al. 2020: GNSS, Nivellement und Radar &amp;#8211; einheitliche Multisensor-Standorte als Referenzpunkte zur &amp;#220;berwachung von Bodenbewegungen. Geomonitoring 2020. DOI: 10.15488/9351&lt;/p&gt;&lt;p&gt;[5] BGR, 2021: https://bodenbewegungsdienst.bgr.de&lt;/p&gt;

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