Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

Sentinel-1 FDBAQ Performance Validation Using TerraSAR-X Data

Two Block Adaptive Quantization (BAQ) algorithms considered for implementation on-board Sentinel-1, the Entropy Constrained BAQ (ECBAQ) and the Flexible Dynamic BAQ (FDBAQ) are investigated with real data acquired by TerraSAR-X. The two algorithms are compared with respect to the resulting signal-to-quantization-noise ratio (SQNR) and the compression rate. The results confirm the improved performance of FDBAQ to be expected for Sentinel-1 compared to the more conventional ECBAQ

ABSTRACT BIOMASS MONITORING WITH SAR

By mapping the aboveground woody biomass in northern boreal forests and the distribution and accumulation of secondary regenerating forests in the tropics, along with the vegetation in the savannah, biomass measurements will provide insight into the size of the carbon sink. The carbon fluxes however are related to changes in the carbon sink and to green biomass activity and therefore monitoring of vegetation changes and activity are needed. By monitoring the changes in above ground woody biomass and estimation of total biomass and its temporal variability, such a mission will contribute significantly to the understanding of the carbon cycle. Furthermore biomass information is also very important to the economies of various countries both in the tropics and in boreal climates. Airborne measurements and in-situ ground campaigns cannot provide a homogeneous and frequently updated data set on a global scale, which is collected independent of national interests. Radar backscatter measurements have proven to be positively correlated with aboveground biomass and this correlation increases with the wavelength. Biomass retrieval algorithms have been developed for airborne P-band data collected over both boreal and tropical forests. Radar measurements are insensitive to cloud cover and can be operated during day and night. Hence a spaceborne radar system, operating at low frequency, will permit the measurement, mapping, and understanding of these parameters with a spatial and temporal resolution suitable for modelling ecosystem processes at regional, continental, and global scales. BIOSAR will be a stand-alone mission such that its objective can be met without any additional data, but synergy is expected wit

Comparison of Sentinel-1 and TerraSAR-X TOPS Processor Implementations based on Simulated Data

TOPS (Terrain Observation with Progressive Scan) mode is a new and promising mode of operation for future SAR satellite missions and is the baseline for ESA’s GMES Sentinel-1 mission. In 2007, DLR HR demonstrated the technical feasibility of TOPS successfully in space with TerraSAR-X. The processing was carried out at DLR-HR using the Experimental TerraSAR-X TOPS processor. In Sentinel-1, the TOPS operational and verification processor are based on the same prototype implementation and thus, a cross check with the TerraSAR-X TOPS processor was performed in a collaboration between DLR-HR and Aresys under project control of ESA/ESTEC. The paper reports about the comparison of the two TOPS processors based on the analysis of processing results from simulated data, i.e. simulated TerraSAR-X and Sentinel-1 TOPS raw data. The comparison program and the analysed performance parameters are presented in the form of simulated Sentinel-1 and TerraSAR-X raw data scenarios

Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1

The forthcoming two-satellite GMES Sentinel-1 constellation is expected to render systematic surface soil moisture retrieval at 1 km resolution using C-band SAR data possible for the first time from space. Owing to the constellation’s foreseen coverage over the Sentinel-1 Land Masses acquisition region—global approximately every six days, nearly daily over Europe and Canada depending on latitude—in the high spatial and radiometric resolution Interferometric Wide Swath (IW) mode, the Sentinel-1 mission shows high potential for global monitoring of surface soil moisture by means of fully automatic retrieval techniques. This paper presents the potential for providing such a service systematically over Land Masses and in near real time using a change detection approach, concluding that such a service is—subject to the mission operating as foreseen—expected to be technically feasible. The work presented in this paper was carried out as a feasibility study within the framework of the ESA-funded GMES Sentinel-1 Soil Moisture Algorithm Development (S1-SMAD) project

TOPS Sentinel-1 and TerraSAR-X Processor Comparison based on Simulated Data

The paper reports about the comparison of the Sentinel-1 Prototype TOPS Processor with the Experimental TerraSAR- X TOPS processor. The comparison is based on simulated raw data generated from TerraSAR-X and Sentinel-1 parameter scenarios. Fundamental impulse response parameters were investigated in point target scenarios. Scenarios with point targets on top a noise floor allowed for comparison of burst images by means of a cross-interferogram. The comparison shows good accordance between the processing results from both processors

Uncertainty Estimates for the FAPAR Operational Products Derived from MERIS - Impact of Top-of-Atmosphere Radiance Uncertainties and Validation with Field Data

This paper discusses the accuracy of the operational Medium Resolution Imaging Spectrometer (MERIS) Level 2 land product which corresponds to the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value is estimated from daily MERIS spectral measurements acquired at the top-of-atmosphere, using a physically based approach. The products are operationally available at the reduced spatial resolution, i.e. 1.2 km, and can be computed at the full spatial resolution, i.e. at 300 m, from the top-of-atmosphere MERIS data by using the same algorithm. The quality assessment of the MERIS FAPAR products capitalizes on the availability of five years of data acquired globally. The actual validation exercise is performed in two steps including, first, an analysis of the accuracy of the FAPAR algorithm itself with respect to the spectral measurements uncertainties and, second, with a direct comparison of the FAPAR time series against ground-based estimations as well as similar FAPAR products derived from other optical sensor data. The results indicate that the impact of top-of-atmosphere radiance uncertainties on the operational MERIS FAPAR products accuracy is expected to be at about 5-10 % and the agreement with the ground-based estimates over different canopy types is achieved within+ 0.1.JRC.H.5-Land Resources Managemen

Validation of the Operational Meris FAPAR Products

This paper discusses the validation of the operational Medium Resolution Imaging Spectrometer (MERIS) land product which corresponds to the biophysical variable of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). The FAPAR value acts as an indicator of the presence and state of the vegetation and it is currently estimated from MERIS data at both reduced and full resolution using a physically-based approach. The quality of the MERIS FAPAR products, derived from the MERIS Global Vegetation Index (MGVI) algorithm, capitalizes on the availability of MERIS data since June 2002. The validation protocol to assess the accuracy of FAPAR product is done 1) by analyzing the estimates of theoretical uncertainties (versus the algorithm formulae and instrument calibration performance), 2) by assessing the performance for detecting expected event using long time-series of FAPAR data over a well-known land surfaces and 3) by comparing the FAPAR MERIS values to similar products generated by other independent sensors like the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the MODerate Resolution Imaging Spectro-radiometer (MODIS) and against ground-estimates of FAPAR which have been performed over various types of land.JRC.H.3-Global environement monitorin