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Upcoming remote sensing systems onboard satellites will generate unprecedented volumes of spatial data, hence challenging processing facilities in terms of storage and processing capacities. Thus, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data׳s storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions. To this end, we defined a new metric named grid oversampling factor (GOF) that estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. Based on common map projections, we defined sets of spatial grids optimised to minimise data oversampling. Moreover, they ensure that data undersampling cannot occur at any location. From the resulting GOF-values we concluded that equidistant projections are most suitable, with a global mean oversampling of 2% when using a system of seven continental grids (introduced under the name Equi7 Grid). Opposed to previous studies that suggested equal-area projections, we recommend the Plate Carrée, the Equidistant Conic and the Equidistant Azimuthal projection for global, hemispherical and continental grids, respectively. [ABSTRACT FROM AUTHOR]

Abstract: The high data throughput of spaceborne Synthetic Aperture Radar (SAR) instruments introduces stringent requirements on the end-to-end system, from the on-board storage capacity and data downlink to processing power and archiving capacity in the ground segment. An efficient raw data compression scheme on-board the satellite is therefore required to maximise the return on investment for SAR missions. The next generation of European SAR satellites, the Sentinel-1 mission, will employ the Flexible Dynamic Block Adaptive Quantization (FDBAQ) compression scheme. In order to validate the implementation of the FDBAQ, expected radar reflectivity levels for the global land surface were needed. The paper presents the development of the Global Backscatter Model (GBM) which has been used to simulate realistic radar reflectivity acquisitions in support of performance validation of the Sentinel-1 FDBAQ implementation. The GBM consists of a parameter database and SAR image simulation software. The model parameter database characterises C-band radar reflectivity as a function of local incidence angle with a resolution of 1km for 85% of the global land surface. The parameter database was derived from a global multi-temporal coverage of ENVISAT Advanced Synthetic Aperture Radar (ASAR) data. The SAR image simulation software uses the parameter database together with orbit propagation and swath determination algorithms in order to account for the instrument acquisition geometry and the location specific backscattering characteristics. The model has the potential to simulate acquisitions for other SAR instruments. Furthermore, the parameter database could be a valuable source of information for global land cover studies. [Copyright &y& Elsevier]

A surface soil moisture (SSM) product at a 1-km spatial resolution derived from the Envisat Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode data was evaluated over the entire African continent using coarse spatial resolution SSM acquisitions from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and the Noah land surface model from the Global Land Data Assimilation System (GLDAS-NOAH). The evaluation was performed in terms of relative soil moisture values (%), as well as anomalies from the seasonal cycle. Considering the high radiometric noise of the ASAR GM data, the SSM product exhibits a good ability (Pearson correlation coefficient (R) = ~0.6 for relative soil moisture values and root mean square difference (RMSD) = 11% when averaged to 5-km resolution) to monitor temporal soil moisture variability in regions with low to medium density vegetation and yearly rainfall >250 mm. The findings agree with previous evaluation studies performed over Australia and further strengthen the understanding of the quality of the ASAR GM SSM product and its potential for data assimilation. Problems identified in the ASAR GM algorithm over arid regions were explained by azimuthal effects. Diverse backscatter behavior over different soil types was identified. The insights gained about the quality of the data were used to establish a reliable masking of the existing ASAR GM SSM product and the identification of areas where further research is needed for the future Sentinel-1-derived SSM products. [ABSTRACT FROM AUTHOR]

Flood detection and inundation mapping are amongst the most important applications for remote-sensing data. Space-borne radar systems, synthetic aperture radar (SAR) in particular, and its application for waterbody mapping have recently been subject to research in many publications. Although very good results have been achieved with such data, in some cases automatic waterbody classification based on SAR data is not feasible. Factors influencing the applicability are, e.g., local environmental conditions, roughening of water surfaces due to wind, or the satellite observation geometry. In this study, a measure for the usability of SAR imagery for flood mapping was investigated. Additionally, a method for permanent waterbody mapping was introduced. The study is based on Envisat ASAR wide swath mode (150 m spatial resolution) data of the Mekong River Basin. For the usability measure, the concept of ‘high-contrast tiles’ was established, which allows anaprioriestimation of the expected accuracy of a waterbody classifier. The SAR-based permanent waterbody map was used for the validation of the approach. It was found that, for the test site, the new SAR usability measure allows the identification of unsuitable scenes with a certainty of more than 90%. The method is expected to be very useful for near-real-time flood mapping applications where human interaction is neither desired nor feasible when large regions and large data volumes are considered. [ABSTRACT FROM PUBLISHER]

The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = -0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88. [ABSTRACT FROM AUTHOR]

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 5×20m 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 1km 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. [Copyright &y& Elsevier]

Abstract: Due to the high temporal sampling rate of ASAR Global Monitoring (GM) mode, it has a high application potential for analyzing the land surface freeze/thaw process in high latitudes. This study aims to develop effective methods of extracting freeze/thaw transition dates of permafrost areas from ASAR GM data sets. In order to use ASAR GM time-series for analyzing freeze/thaw states, a least square fitting of piecewise step function is introduced. The thawing date can be determined by minimizing the sum of squared residuals between measured backscattering time-series and a pre-defined step function. An experimental result for a Siberian permafrost region illustrates that it can be a promising approach in monitoring permafrost ecosystems. [Copyright &y& Elsevier]

The advanced synthetic aperture radar (ASAR) on-board of the satellite ENVISAT can be operated in global monitoring (GM) mode. ASAR GM mode has delivered the first global multiyear C-band backscatter data set in HH polarization at a spatial resolution of 1 km. This paper investigates if ASAR GM can be used for retrieving soil moisture using a change detection approach over large regions. A method previously developed for the European Remote Sensing (ERS) scatterometer is adapted for use with ASAR GM and tested over Oklahoma, USA. The ASAR-GM-derived relative soil moisture index is compared to 50-km ERS soil moisture data and pointlike in situ measurements from the Oklahoma MESONET. Even though the scale gap from ASAR GM to the in situ measurements is less pronounced than in the case of the ERS scatterometer, the correlation for ASAR against the in situ measurements is, in general, somewhat weaker than for the ERS scatterometer. The analysis suggests that this is mainly due to the much higher noise level of ASAR GM compared to the ERS scatterometer. Therefore, some spatial averaging to 3-10 km is recommended to reduce the noise of the ASAR GM soil moisture images. Nevertheless, the study demonstrates that ASAR GM allows resolving spatial details in the soil moisture patterns not observable in the ERS scatterometer measurements while still retaining the basic capability of the ERS scatterometer to capture temporal trends over large areas. [ABSTRACT FROM AUTHOR]