European Space Agency Soil Moisture Research Articles

Ice Freeze‐Up and Break‐Up in Arctic Rivers Observed With Satellite L‐Band Passive Microwave Data From 2010 to 2020

AbstractThe timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.

Validation of Soil Moisture Data Products From the NASA SMAP Mission

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error &lt;0.04 m³&#x002F;m³). The validation approach also includes other (&#x201C;sparse network&#x201D;) <i>in situ</i> SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products&#x2019; performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the <i>L</i>-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio&#x2013;temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

One-Point Microwave Radiometer Calibration

A method for internally calibrating microwave total power radiometers by using only one level of noise injection is presented. It is based on having a previous accurate characterization of the receiver noise temperature, which used de facto as a second calibration standard. The method proves to be at least equivalent to the classical two level, as demonstrated through their intercomparison using the data provided by the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Satellite. The long-term stability in terms of retrieved brightness temperature using both methods has similar trends with a small advantage for the one-point approach proposed here.

Responses of soil moisture to regional climate change over the Three Rivers Source Region on the Tibetan Plateau

AbstractIn this study, soil moisture data from two reanalysis datasets (ERA‐Interim, ERA5), a satellite soil moisture product from the European Space Agency (ESA) and three assimilation datasets from the Global Land Data Assimilation System (GLDAS), that is, GLADS‐NOAH025, GLADS‐NOAH10, and GLDAS‐CLM were compared with the observed soil moisture data in Maqu and Maduo stations over the Three Rivers Source Region (TRSR) on the Tibetan Plateau in China. Comparative statistical parameters between the soil moisture observations and the products, including the root mean square error, correlation coefficient (R), and standard deviation ratio were calculated to evaluate the products in different seasons. It was found that most products overestimated the soil moisture values, among which GLADS‐NOAH025 had relatively good consistency with the observations in the surface soil layer (0–10 cm), whereas GLDAS‐CLM agreed well with the observations in the 10–40 cm layer. Both ERA5 and ERA‐interim soil moisture overestimated soil moisture at all levels. The responses of soil moisture to regional climate change over the TRSR were also investigated by using the GLDAS‐NOAH025 soil moisture product, the ESA soil moisture product, and the observations during the nonfreezing period. The results show that the trends of soil moisture are consistent with the precipitation changes in the main central part of the TRSR. A significant positive correlation was found between soil moisture and precipitation in most regions of the TRSR, which indicates that soil moisture increases with the increase of precipitation, except in the northwest TRSR, where the variation of soil moisture was possibly influenced by both the precipitation changes and the increase of evapotranspiration.

Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., ~40 km × 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km × 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km × 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25° × 0.25°) and CLDAS land surface model (LSM) SSM output (0.0625° × 0.0625°) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of −0.001 cm3/cm3 (0.041 cm3/cm3) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km × 1 km resolution.

Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations

In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.

In this issue of Weather

We begin the new year of Weather with a fascinating examination of oceanic salinity and its measurement from space in ‘Salinity from space’ by M Srokosz and C Banks. Most important in the process of oceanic circulation and analogous to humidity in the atmosphere, creating water masses with particular characteristics, is its salt content. The proportion and absolute amount of salt is variable, but until recently it has not been possible to sense this variation in near‐real time, the most readily available data being the system of Argo floats. This paper shows the value of the recently launched European Space Agency Soil Moisture and Ocean Salinity satellites, which measure the salinity of all the world's oceans, every three days.On p. 8, Alan Hisscott describes the use of a microwave sensor of the lower troposphere on the Isle of Man to explore atmospheric characteristics and weather features in ‘Exploiting wind profiler information’ using seven examples of data from the instrument.Personal weather stations have become very popular in recent years and sharing the information gathered is a useful adjunct to official weather observations. However, systems to allow this data to be seen on the Internet vary, as outlined by Michael Butler in ‘Personal weather stations and sharing weather data via the Internet’ on p. 22.On p. 30, we have the third in our irregular series of Climate Change Shorts, this one asking ‘How is sea ice in the Arctic and Antarctic changing?’ by Dan Jones, Emily Shuckburgh and Ed Hawkins.Our final paper this month looks at rainfall in north‐eastern India in ‘Spatial trends in rainfall seasonality: a case study in Jharkhand, India’ by Shonam Sharma and Prasoon Kumar Singh on p. 31.

Accuracy evaluation of the CCI remote sensing soil moisture for revealing drought in Northeast China

The validation research is the basis of application of remote sensing data. This study using soil surface measured data for 10 cm in Northeast China in 1992-2013 to verify the European space agency soil moisture data set (ESA CCI SM). In this study, two methods evaluating CCI data for revealing drought was used: common Pearson coefficient method and drought judgement method for coincidence degree of drought. This verification study is located in the main grain producing areas of Northeast China, which is dominated by rain-fed agriculture. After the result analysis, CCI remote sensing soil moisture is feasible for assessment research on agricultural drought. Through the evaluation of this study, this remote sensing soil water can be used as an effective indicator of agricultural drought in the main grain-producing region of Northeast China.

High-Resolution RFI Localization Using Covariance Matrix Augmentation in Synthetic Aperture Interferometric Radiometry

Radio frequency interference (RFI) is a significant limiting factor in the retrieval of geophysical parameters measured by microwave radiometers. RFI localization is crucial to mitigate or remove the RFI impacts. In this paper, a novel RFI localization approach using covariance matrix augmentation in synthetic aperture interferometric radiometry (SAIR) is proposed. It utilizes the property of the sparse array configuration, which is commonly used in SAIR, where the sparse array can be viewed as a virtual filled array with much larger number of antenna elements. The approach can be applied in SAIR with a sparse array configuration, such as the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. Results on real SMOS data show that, compared with the previous approach, the presented approach has an improved performance of RFI localization with comparable accuracy of localization, such as improved spatial resolution, lower sidelobes, and larger identifiable number of RFIs.

L-band vegetation optical depth and effective scattering albedo estimation from SMAP

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and effective scattering albedo estimates from SMAP Backus-Gilbert enhanced brightness temperatures posted on a 9km grid and with three day revisit time. SMAP VOD values from the MT-DCA follow expected global distributions and are shown to be highly correlated with canopy height. They are also broadly similar in magnitude (though not always in seasonal amplitude) to European Space Agency Soil Moisture and Ocean Salinity (SMOS) VOD. The SMOS VOD values are based on angular brightness temperature information while the SMAP measurements are at a constant incidence angle, requiring an alternate approach to VOD retrieval presented in this study.Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values.

Detecting hydrological consistency between soil moisture and precipitation and changes of soil moisture in summer over the Tibetan Plateau

As the first purely multi-decadal satellite-based soil moisture product that spans over 35 years (from November 1978 to December 2013) on a daily basis designed for climate application, the applicability of the European Space Agency (ESA) soil moisture product, including the hydrological consistency between the product and the observed precipitation and the product continuity on the Tibetan Plateau (TP) were investigated. The results show that there is significant degree between the ESA soil moisture product and the observed precipitation. The positive anomaly of the ESA soil moisture product can reflect the occurrences of precipitation, but the precipitation may not definitely lead to soil moisture anomaly, which largely depends on the precipitation amounts. For climate application, large number of missing gaps was shown on the west of the TP, where it is considered that the retrieval algorithms are largely affected by the permafrost covered in this region, leaving the ESA soil moisture product for further improvement. In application, the ESA soil moisture product was used to study the response of surface soil moisture to climate change on the TP. With the rapid warming and the overall wetting of the TP, soil moisture increases on the central of the TP with the increase of precipitation, and decreases in the southeast TP with the precipitation deduction. However, it decreases in the west TP, where it was probably influenced by both the insignificant precipitation changes and the significant increase of evaporation.

From field experiments to salinity products: a tribute to the contributions of Jordi Font to the SMOS mission

This article summarizes some of the activities in which Jordi Font, research professor and head of the Department of Physical and Technological Oceanography, Institut de Ciencies del Mar (CSIC, Spanish National Research Council) in Barcelona, has been involved as co-Principal Investigator for Ocean Salinity of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Mission from the perspective of the Remote Sensing Lab at the Universitat Politecnica de Catalunya. We have probably left out some of his many contributions to salinity remote sensing, but we hope that this review will give an idea of the importance of his work. We focus on the following issues: 1) the new accurate measurements of the sea water dielectric constant, 2) the WISE and EuroSTARRS field experiments that helped to define the geophysical model function relating brightness temperature to sea state, 3) the FROG 2003 field experiment that helped to understand the emission of sea foam, 4) GNSS-R techniques for improving sea surface salinity retrieval, 5) instrument characterization campaigns, and 6) the operational implementation of the Processing Centre of Levels 3 and 4 at the SMOS Barcelona Expert Centre.

Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002–2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1 m3/m3 compared to climatological values of more than 0.35 m3/m3. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.

Impact of Sea Surface Temperature and Measurement Sampling on the SMOS Level 3 Salinity Products

The European Space Agency Soil Moisture and Ocean Salinity mission aims at estimating, over the oceans, sea surface salinity (SSS) with spatial and temporal coverage adequate for large-scale oceanography. Spatiotemporal averaging of the retrieved SSS [level-3 (L3) product] has to be properly performed in order to meet the challenging mission requirements. At high latitudes, the generally low sea surface temperature (SST) characterizing the ocean degrades the brightness temperature sensitivity to SSS, but conversely, an improvement in the L3 retrieved SSS performances should be expected due to an increased pixel sampling. This tradeoff between geophysical effects in cold seawater and the concomitant temporal oversampling has been addressed by analyzing the latitudinal trend of the retrieved salinity performances, in various retrieval configurations and settings, once a conservative and optimal data filtering strategy is applied. Quantitative rate of changes of the SSS retrieval performance with the SST variability is provided, together with the net oversampling contribution to the L3 SSS accuracy. The experiments carried out demonstrate that the high-latitude oversampling does not compensate for the SST-driven latitudinal degradation of the L3 SSS product quality.

Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

Validating SMOS Ocean Surface Salinity in the Atlantic With Argo and Operational Ocean Model Data

This paper provides an assessment of synoptic measurements of sea surface salinity (SSS) from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite. Due to the complex nature of the response of L-band signals to SSS, SMOS provides three values of SSS at each grid point from three different forward models. To meet oceanographic requirements for SSS retrieval accuracy, SMOS Level 2 SSS products are averaged over time and space. This paper reports on validation studies in the Atlantic based on monthly Level 3 products on a 1°×1° grid for September 2010. Outside coastal regions, large-scale SSS patterns from SMOS are in general agreement with climatology, Argo, and ocean model output. During September 2010, SSS from descending passes provides reasonable quantitative estimates, while SSS from ascending passes overestimates SSS by over 1 practical salinity unit (psu). The daily mean difference in SSS between ascending and descending passes varies during August-December 2010, reaching a maximum in September. Differences in SMOS SSS from the three models are an order of magnitude smaller than differences between ascending and descending passes. Gridded SMOS SSS data are compared against output from the U.K. Met Office Forecasting Ocean Assimilation Model (FOAM)-Nucleus for European Modelling of the Ocean (NEMO). Basic checks confirm that SSS from FOAM-NEMO is unbiased against Argo and that FOAM-NEMO SSS is a useful independent data source to validate and rapidly identify departures in SMOS SSS. Over the whole Atlantic, SMOS SSS variability against FOAM-NEMO is around 0.9 psu, decreasing to 0.5 psu over the subtropical North Atlantic.

A First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy and Luxembourg

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils.

SMOS Semi-Empirical Ocean Forward Model Adjustment

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> .

Surveys and Analysis of RFI in Preparation for SMOS: Results from Airborne Campaigns and First Impressions from Satellite Data

Several soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out in preparation for the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. The radiometer used in this context is fully polarimetric and is capable of detecting radio frequency interference (RFI) using the kurtosis method. Analyses have shown that the kurtosis method generally detects RFI in an efficient manner, particularly concerning pulsed, low duty cycle signals, but it has some shortcomings when it comes to more continuous wave signal types. Hence, other detection methods have been investigated as well. In particular, inspection of the third and fourth Stokes parameters shows promising results—possibly as a complement to the kurtosis method. The kurtosis method, however, cannot be used with SMOS data. Since SMOS is fully polarimetric, the third and fourth Stokes parameter method is an option, and a first assessment using a fully polarimetric SMOS data set looks promising. Finally, a variable incidence angle signature algorithm is introduced, and the possibility of using this as an RFI indicator is discussed.

L-Band RFI as Experienced During Airborne Campaigns in Preparation for SMOS

In support of the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission, a number of soil moisture and sea salinity campaigns, including airborne L-band radiometer measurements, have been carried out. The radiometer used in this context is fully polarimetric and has built-in radio-frequency-interference (RFI)-detection capabilities. Thus, the instrument, in addition to supplying L-band data to the geophysicists, also gave valuable information about the RFI environment. Campaigns were carried out in Australia and in a variety of European locations, resulting in the largest and most comprehensive data set available for assessing RFI at L-band. This paper introduces the radiometer system and how it detects RFI using the kurtosis method, reports on the percentage of data that are typically flagged as being corrupted by RFI, and gives a hint about geographical distribution. Also, examples of polarimetric signatures are given, and the possibility of detecting RFI using such data is discussed.