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The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facility of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing “Sentinel-1 GRD Border Noise Removal” algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform.1 Validation and evaluation of the newly proposed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise. [ABSTRACT FROM PUBLISHER]

Surface soil moisture is one of the operational products derived from Advanced Scatterometer (ASCAT) data. The reliability of its estimation depends on the detection of predominantly frozen conditions of the landscape (including soil and vegetation) and the presence of wet snow, which would otherwise impede the estimation. As the robust determination of the freeze/thaw (F/T) state using exclusively scatterometer measurements on a global basis is complicated due to the myriad of different climatic and land cover conditions; we propose to support the retrieval using ERA Interim temperature data. The approach is based on a probabilistic time series model, whereby backscatter and temperature data are combined to estimate the freeze/thaw state. The method is assessed with proxy F/T states derived from modeled and in situ air and soil temperature data on a global basis. These analyses show an improved consistency compared to a previously published ASCAT F/T algorithm, with typical agreements between the external data and the results of the algorithm exceeding 80%. The quantitative interpretation of these comparisons is, however, hampered by discrepancies between the F/T state derived from temperature data and the one pertinent to radar remote sensing, as the former does not account for, e.g., wet snow conditions. The inclusion of the ERA Interim temperature data can improve the accuracy of the algorithm by more than 10 percentage points in regions where freezing conditions are rare. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable and a key parameter in hydrology, meteorology and agriculture. Surface Soil Moisture (SSM) can be estimated from measurements taken by ASCAT onboard Metop-A and have been successfully validated by several studies. Profile soil moisture, while equally important, cannot be directly measured by remote sensing but must be modeled. The Soil Water Index (SWI) product developed for near real time applications within the framework of the GMES project geoland2 aims to provide such a modeled profile estimate using satellite data as input. It is produced from ASCAT SSM estimates using a two-layer water balance model which describes the relationship between surface and profile soil moisture as a function of time. It provides daily global data about moisture conditions for eight characteristic time lengths representing different depths. The objective of this work was to assess the overall quality of the SWI data. Furthermore we tested the assumptions of the used water balance model and checked if ancillary information about topography, water fraction and noise information are useful for identifying observations of questionable quality. SWI data from January 1st 2007 until the end of 2011 was compared to in situ soil moisture data from 664 stations belonging to 23 observation networks which are available through the International Soil Moisture Network (ISMN). These stations delivered 2081 time series at different depths which were compared to the SWI values. The average of the significant Pearson correlation coefficients was 0.54 while being greater than 0.5 for 64.4% of all time series. It was found that the characteristic time length showing the highest correlation increases with in situ observation depth, thus confirming the SWI model assumptions. Relationships of the correlation coefficients with topographic complexity, water fraction, in situ observation depth, and soil moisture noise were found. [ABSTRACT FROM AUTHOR]

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al. [ABSTRACT FROM PUBLISHER]

Soil moisture is a key environmental variable, important to e.g., farmers, meteorologists, and disaster management units. We fuse surface soil moisture (SSM) estimates from spatio-temporally complementary radar sensors through temporal filtering of their joint signal and obtain a kilometre-scale, daily soil water content product named SCATSAR-SWI. With 25 km Metop ASCAT SSM and 1 km Sentinel-1 SSM serving as input, the SCATSAR-SWI is globally applicable and achieves daily full coverage over operated areas. We employ a near-real-time-capable SCATSAR-SWI algorithm on a fused 3 year ASCAT-Sentinel-1-SSM data cube over Italy, obtaining a consistent set of model parameters, unperturbed by coverage discontinuities. An evaluation of a therefrom generated SCATSAR-SWI dataset, involving a 1 km Soil Water Balance Model (SWBM) over Umbria, yields comprehensively high agreement with the reference data (median R = 0.61 vs. in situ; 0.71 vs. model; 0.83 vs. ASCAT SSM). While the Sentinel-1 signal is attenuated to some extent, the ASCAT's signal dynamics are fully transferred to the SCATSAR-SWI and benefit from the Sentinel-1 parametrisation. Using the SM2RAIN approach, the SCATSAR-SWI shows excellent capability to reproduce 5 day-accumulated rainfall over Italy, with R = 0.89 against observed rainfall. The SCATSAR-SWI is currently in preparation towards operational product dissemination in the Copernicus Global Land Service (CGLS). [ABSTRACT FROM AUTHOR]