Search

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3/m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set. [ABSTRACT FROM AUTHOR]

In recent years, different space agencies have launched satellite missions that carry passive microwave instruments on-board that can measure surface soil moisture. Three currently operational missions are the Soil Moisture and Ocean Salinity (SMOS) mission developed by the European Space Agency (ESA), the Advanced Microwave Scanning Radiometer 2 (AMSR2) developed by the Japan Aerospace Exploration Agency (JAXA), and the Microwave Radiation Imager (MWRI) from China's National Satellite Meteorological Centre (NSMC). In this study, the quality of surface soil moisture anomalies derived from these passive microwave instruments was sequentially assessed over the mainland of the People's Republic of China. First, the impact of a recent update in the Land Parameter Retrieval Model (LPRM) was assessed for MWRI observations. Then, the soil moisture measurements retrieved from the X-band observations of MWRI were compared with those of AMSR2, followed by an internal comparison of the multiple frequencies of AMSR2. Finally, SMOS retrievals from two different algorithms were also included in the comparison. For each sequential step, processing and verification chains were specifically designed to isolate the impact of algorithm (version), observation frequency or instrument characteristics. Two verification techniques are used: the statistical Triple Collocation technique is used as the primary verification tool, while the precipitation-based Rvalue technique is used to confirm key results. Our results indicate a consistently better performance throughout the entire study area after the implementation of an update of the LPRM.We also find that passive microwave observations in the AMSR2 C-band frequency (6.9 GHz) have an advantage over the AMSR2 X-band frequency (10.7 GHz) over moderate to densely vegetated regions. This finding is in line with theoretical expectations as emitted soil radiation will become masked under a dense canopy with stricter thresholds for higher passive microwave frequencies. Both AMSR2 and MWRI make X-band observations; a direct comparison between them reveals a consistently higher quality obtained by AMSR2, specifically over semi-arid climate regimes. Unfortunately, Radio Frequency Interference hampers the usefulness of soil moisture products for the SMOS L-band mission, leading to a significantly reduced revisit time over the densely populated eastern part of the country. Nevertheless, our analysis demonstrates that soil moisture products from a number of multi-frequency microwave sensors are credible alternatives for this dedicated L-band mission over the mainland of the People's Republic of China. [ABSTRACT FROM AUTHOR]

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer--Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures (Tb) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E Tb's to soil temperature (Tsoil ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E Tb's to retrieve Tsoil is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and Tsoil information was chosen to obtain SM in the 2003-2011 period. The new dataset shows a low bias (<0.02 m3/m3) and low standard deviation of the difference (<0.04 m3/m3) with respect to SMOS L3 SM over most of the globe's surface. The new dataset was evaluated together with other AMSR-E SM datasets and the Climate Change Initiative (CCI) SM dataset against the MERRA-Land and ERA-Land models for the 2003-2011 period. All datasets show a significant bias with respect to models for boreal regions and high correlations over regions other than the tropical and boreal forest. All of the global SM datasets including AMSR-E NN were also evaluated against a large number of in situ measurements over four continents. Over Australia, all datasets show a strong level of agreement with in situ measurements. Models perform better over Europe and mountainous regions in North America. Remote sensing datasets (in particular NN and the Land Parameter Retrieval Model (LPRM)) perform as well as models for other North American sites and perform better than models over the Sahel region. [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]

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l -band microwave radiometry. The comparison was based on a brightness temperature (T B ) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular T B data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R 2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m 3 /m 3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R 2 ≈ 0.93, RMSE ≈ 0.035 m 3 /m 3 ). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R 2 ≈ 0.75, RMSE ≈ 0.055 m 3 /m 3 ). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals. [ABSTRACT FROM AUTHOR]