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A methodology of intercalibration of C-band spaceborne scatterometers is developed and applied to European Remote Sensing satellite-1 (ERS-1), European Remote Sensing satellite-2 (ERS-2), and Meteorological Operational satellite (METOP) scatterometer data. Assuming that the differences between the instruments can be represented by an incidence-angle-dependent bias, this paper presents and discusses four methods, providing an estimate of that bias and of its standard deviation. Model-based methods performed more accurately than a direct comparison of \sigma0. The latter provides a systematic larger positive bias. The methodology is applied to ERS-1 and ERS-2 data acquired during the tandem mission in 1996. The same methodology is applied to ERS-2 and Advanced Scatterometer (ASCAT) data acquired in December 2008. Generally, the bias between the ERS-1 and ERS-2 scatterometers is smaller than 0.2 dB over most incidence angles, and the four methods provide relatively consistent results. The bias between ERS-2 and ASCAT is slightly higher, reaching 0.4 dB at certain incidence angles. These results suggest that these scatterometers need to be intercalibrated to achieve a consistent backscatter data. [ABSTRACT FROM PUBLISHER]

In this paper, we define the method that is used to merge high-resolution multisensor chlorophyll- a (chl-a) data on the Ireland–Biscay–Iberia Regional Ocean Observing System area from 1998 to the present at a resolution of 1.1 km. The method is based on geostatistics and is known as kriging. The merged variable is the daily anomaly of chl-a, with the anomaly being defined as the difference between the daily image and the mean historical field for the considered day. For each day, the continuous anomaly image is generated using the kriging method, and the mean historical field is then added to obtain the cloudless field of chl- a. The initial satellite chl-a data set used in the merging procedure is derived from the daily level-2 water leaving radiances of three ocean color sensors: the Sea-Viewing Wide Field of View Sensor on the Orbview platform, the Moderate Resolution Imaging Spectroradiometer on the Aqua platform, and the Medium Resolution Imaging Spectrometer Instrument on the ENVISAT platform. The chl-a concentration is obtained using a specific algorithm developed by Ifremer, known as OC5 product. Before merging, each satellite-derived chl- a product has been compared to in situ data and has been validated using a matchup data set. After this validation against in situ data, intercomparisons between the satellite data sets have been performed. As the chl-a anomaly variability depends, in this region, on the season and the distance from the shore, local space-time semivariograms have been calculated to estimate the spatiotemporal dependence or covariance of the chlorophyll anomalies. The semivariograms, used in the estimation of the kriged anomaly, are defined by their nuggets (noise), their spatial and temporal range (maximum distance for a nonnull covariance between the anomalies), and their sill (maximum variance). The spatial range of the semivariogram has been approximated locally on a regular grid. The nuggets and the sills have been deduced from the square of the mean of the chl-a concentration (the climatological reference) as we have observed a classical proportionality effect between the square of the chl-a mean, the variance of the distribution, and the parameters of the semivariograms. Compared to each original product, the analysis shows a complete coverage and differences with the in situ data that are statistically equivalent to those observed with the initial satellite data set. The merged product offers a number of applications for environmental monitoring such as the monitoring of the eutrophication risk required by the Water Framework Directive of the European Union. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) Mission is the second of the European Space Agency's Living Planet Program Earth Explorer Opportunity Missions, and it is scheduled for launch in July 2009. Its objective is to provide global and frequent soil-moisture and sea-surface-salinity (SSS) maps, SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band 2-D aperture-synthesis interferometric radiometer. For the SSS, the output products of SMOS, at Level 3, will have global coverage and an accuracy of 0.1-0.4 psu (practical salinity units) over 100 x 100-200 x 200 km² in 10-30 days. During the last few years, several studies have pointed out the necessity of combining auxiliary data with the MIRAS-measured brightness temperature to provide the required accuracy. In this paper, we propose and test two techniques to include auxiliary data in the SMOS SSS retrieval algorithm. Aiming at this, pseudo-SMOS Level-3 products have been generated according to the following steps: 1) A North Atlantic configuration of the NEMO-OPA ocean model has been run to provide consistent geophysical parameters; 2) the SMOS end-to-end processor simulator has been used to compute the brightness temperatures as measured by the MIRAS; 3) the SMOS Level-2 processor simulator has been applied to retrieve SSS values for each point and overpass; and 4) Level-2 data have been temporally and spatially averaged to synthesize Level-3 products. In order to assess the impact of the proximity to the coast at Level 3, and the effect of these techniques on it, two different zones have been simulated: the first one in open ocean and the second one in a coastal region, near the Canary Islands (Spain) where SMOS and Aquarius CAL/VAL activities are foreseen. Performance exhibits a clear improvement at Level 2 using the techniques proposed; at Level 3, a smaller effect has been recorded. Coastal proximity has been found to affect the retrieval of up to 150 and 300 km from the coast, at Levels 2 and 3, respectively. Results for both scenarios are presented and discussed. [ABSTRACT FROM AUTHOR]

Strong winds cause severe damage worldwide to forested land every year. The devastating storms that struck large parts of Europe in late 1999 destroyed the equivalent of several years of normal forest harvesting, amounting to very large economical sums. Therefore, rapid mapping of damaged areas is of major importance for assessment of short-term actions as well as for long-term reforestation purposes. In this paper, the use of airborne CARABAS-II very high frequency (VHF) (20-90 MHz) synthetic aperture radar (SAR) imagery for high spatial resolution mapping of wind-thrown forests has been investigated and evaluated. The investigation was performed at a test site located in southern Sweden and dominated by Norway spruce forests. A regression model estimating radar backscattering amplitude prior to the storm was developed. The estimated amplitudes were compared to measured amplitudes after the storm. The results clearly show that the backscattering amplitude, at a given stem volume, is considerably higher for wind-thrown forests than for unaffected forests. Furthermore, the backscattering from fully harvested storm-damaged areas was, as expected, significantly lower than from unaffected stands. These findings imply that VHF SAR imagery has potential for mapping wind-thrown forests. However, to prevent ambiguities in increased backscattering caused by normal stem volume growth or wind-fellings, multitemporal change detection techniques using VHF SAR images acquired prior to and after wind-fellings would be preferable. [ABSTRACT FROM AUTHOR]