| 1. |
- Ban, Yifang, et al.
(author)
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EO4Urban : First-year results on Sentinel-1A SAR and Sentinel-2A MSI data for global urban services
- 2016
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In: European Space Agency, (Special Publication) ESA SP. - 9789292213053
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Conference paper (peer-reviewed)abstract
- The overall objective of this research is to evaluate multitemporal Sentinel-1A SAR and Sentinel-2A MSI data for global urban services using innovative methods and algorithms, namely KTH-Pavia Urban Extractor, a robust algorithm for urban extent extraction and KTHSEG, a novel object-based classification method for detailed urban land cover mapping. Ten cities around the world in different geographical and environmental conditions were selected as study areas. Large volume of Sentinel-1A SAR and Sentinel-2A MSI data were acquired during vegetation season in 2015 and 2016. The preliminary urban extraction results showed that urban areas and small towns could be well extracted using multitemporal Sentinel-1A SAR data with the KTH-Pavia Urban Extractor. For urban land cover mapping, multitemporal Sentinel-1A SAR data alone yielded an overall classification accuracy of 60% for Stockholm. Sentinel-2A MSI data as well as the fusion of Sentinel-1A SAR and Sentinel-2A MSI data, however, produced much higher classification accuracies, both reached 80%.
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| 2. |
- Blomberg, Erik, 1987, et al.
(author)
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Simulated biomass retrieval from the spaceborne tomographic Saocom-CS mission at L-band
- 2016
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In: European Space Agency, (Special Publication) ESA SP. - 0379-6566. - 9789292213053 ; 740
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Conference paper (peer-reviewed)abstract
- This paper presents an evaluation of above-ground biomass (ABG) retrieval in boreal forests using simulated tomographic synthetic-aperture radar (SAR) data corresponding to the future SAOCOM-CS (L-band 1.275 GHz) mission. Using forest and radar data from the BioSAR 2008 campaign at the Krycklan test site in northern Sweden the expected performance of SAOCOM-CS is evaluated and compared with the E-SAR airborne Lband SAR (1.300 GHz). It is found that SAOCOM-CS data produce retrievals on par with those obtained with E-SAR, with retrievals having a relative RMSE of 30% or less. This holds true even if the acquisitions are limited to a single polarization, with HH results shown as an example.
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| 3. |
- Buchwitz, M., et al.
(author)
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The GHG-CCI project of ESA's climate change initiative : Data products and application
- 2016
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In: Proceedings of Living Planet Symposium 2016. - 9789292213053 ; SP-740
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Conference paper (peer-reviewed)abstract
- The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).
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| 4. |
- McCutchan, Marvin, et al.
(author)
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Multitemporal Sentinel-1A data for urban land cover mapping using deep learning : Preliminary results
- 2016
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In: European Space Agency, (Special Publication) ESA SP. - : European Space Agency. - 9789292213053
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Conference paper (peer-reviewed)abstract
- The objective of this research is to evaluate multitemporal Sentinel-1A SAR data for urban land cover mapping using a pixel-based Deep Belief Network (DBN) and an object-based post-processing. Multitemporal Sentinel-1A SAR in both ascending and descending orbits were acquired in Stockholm during the 2015 vegetation season. The images were first terrain corrected, co-registered, speckle filtered and scaled to 8 bit. Then the images were segmented using KTH-SEG, an edgeaware region growing and merging algorithm. For classification, a pixel-based deep belief network (DBN) was used. Then classification result was post-processed using object-based majority voting. For comparison, the same dataset was classified using an object-based support vector machine (SVM). The preliminary results show that the hybrid deep learning classification scheme produced comparable results as object-based SVM while yielded higher accuracies for builtup classes.
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| 5. |
- Ulander, Lars, 1962, et al.
(author)
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Borealscat: A tower experiment for understanding temporal changes in P- and L-band backscattering from a Boreal forest
- 2016
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In: European Space Agency, (Special Publication) ESA SP. - 0379-6566. - 9789292213053 ; 740
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Conference paper (peer-reviewed)abstract
- This paper describes the tower-based radar BorealScat, which is being developed for polarimetric, tomographic and Doppler measurements at the hemi-boreal forest test site in Remningstorp, Sweden. The facility consists of a 50-m high tower equipped with an antenna array at the top of the tower, a 20-port vector network analyser (VNA), 20 low-loss cables for interconnection, and a calibration loop with a switching network. The first version of BorealScat will perform the full set of measurements in the frequency range 0.4-1.4 GHz, i.e. P-band and L-band. The tower is currently under construction at a forest stand dominated by Norway spruce (Picea abies (L.) Karst.). The mature stand has an above-ground dry biomass of 300 tons/ha. Data collections are planned to commence in autumn 2016.
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