Sentinel-1 radar mission: Status and performance
ESTEC, Eur. Space Agency, Noordwijk, NetherlandsConference: Radar Conference - Surveillance for a Safer World, 2009. RADAR. International
Source: IEEE Xplore
The ESA Sentinels constitute the first series of operational satellites responding to the Earth Observation needs of the EU-ESA Global Monitoring for Environment and Security (GMES) programme. The GMES space component relies on existing and planned space assets as well as on new complementary developments by ESA. This paper describes the Sentinel-1 mission, an imaging synthetic aperture radar (SAR) satellite constellation at C-band. It provides an overview of the mission requirements, its applications and the technical concept for the system.
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Conference Paper: Prospects of Sentinel-1 for land applications[Show abstract] [Hide abstract]
ABSTRACT: The Sentinel-1 mission is a polar-orbiting satellite constellation for the continuation of C-band Synthetic Aperture Radar (SAR) applications. Contrary to its predecessor instruments onboard of ENVISAT and RADARSAT, the Sentinel-1 satellites will be operated following a predefined and fixed baseline acquisition scenario. This will significantly facilitate the development of fully automatic processing chains for the generation of higher-level geophysical products and their uptake in applications. This paper gives an overview of the potential use of Sentinel-1 for land applications, discussing different land cover products (permanent water bodies, forest/non-forest, rice) and parameters of high relevance for hydrological monitoring (soil moisture, snow and freeze/thaw status, surface inundation).
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ABSTRACT: The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling.
Conference Paper: SBAS-DInSAR time series generation on cloud computing platforms[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes a parallel model for the Differential Interferometry Synthetic Aperture Radar approach referred to as Small BAseline Subset (SBAS) algorithm. This new computational model has been designed to be specifically exploited within the emerging cloud computing environments. An experimental analysis, involving two different case studies, has been carried out to demonstrate the effectiveness of the methodology. The major novelty of the proposed Parallel SBAS (P-SBAS) model consists in the capability of processing large SAR data sets in reasonable time-frames. This key feature may be of great impact not only for hazard monitoring and risk mitigation activities but also for data sharing and knowledge spreading within the scientific community.