An assessment of the agreement between the ERS scatterometers (ERS-1 and ERS-2) and the Metop scatterometers (ASCAT-A and ASCAT-B) is essential for the consistency of the C-band scatterometry dataset. ERS-1, ERS-2, ASCAT-A and ASCAT-B are C-band fan-beam radar scatterometers covering a range of common incidence angles. During these C-band scatterometry missions, different calibration campaigns have been carried out mainly relying on active ground transponders and natural distributed targets such as the rainforest. Additionally, these missions differ in time with some overlapping periods. Therefore, an assessment of the agreement between ERS and ASCAT measurements is an important and challenging task. This assessment is usually performed over the rainforest but only considering the common incidence angles. In order to perform the comparison over the whole incidence angle range of both radars, a Geophysical Model Function (GMF) is needed. An empirical correction of the CMOD5.n GMF has been suggested recently by KNMI resulting in a new GMF called CMOD6. This correction was derived from the comparison of the ASCAT backscatter measurements and the CMOD5.n model. Taking ASCAT’s measurements as reference, the differences between the CMOD5.n and ASCAT measurements were attributed to GMF errors. Additionally, an overview of the existing C-band models is given. The comparison of these models shows relatively large differences. The aim of this paper is the assessment of the CMOD6 GMF using ERS-1 and ERS-2 ocean backscatter measurements and the validation of the applicability of the corrected GMF to the whole C-band scatterometry dataset. Finally, a method is suggested to calibrate the residual bias of all the C-band scatterometers w.r.t CMOD6. It is shown that after calibration a consistent scatterometer data model is obtained.
ERS-1/2 and Metop-A/B satellites carry a very similar radars operating at similar frequencies (5.3/5.255 GHz) and same polarization (VV). However, the radars on-board the satellites of these two missions differ in the pulse waveform, bandwidth and slightly in geometry. Moreover, the on-board and the on-ground processing is different. This paper investigates the spatial and radiometric resolution of these radars and the resolution enhancement between ERS (1991-2011) and Metop (2006- ) missions. The spatial resolution assessment implies the computation and the comparison of the Spatial Response Function (SRF) of both systems. The SRF involves mainly the antenna gain pattern, the pulse waveform and the different on-board filtering stages. The radiometric resolution depends mainly on the signal to noise ratio (SNR) and the number of averaged independent samples (N). Furthermore, the correlation of the measurement samples in a resolution cell is computed to assess the independence assumption. The metric used to quantify the radiometric accuracy in scatterometry is called Kp which is the relative standard deviation. A comparison of Kp parameter extracted from the nominal products of the two missions confirms the expected performance based on the SNR, N and correlation analysis.
A scatterometer is a radar designed to measure the backscattering coefficient of distributed targets. In order to compute the backscatter from the received power, the scatterometer measures also the thermal noise power. This noise signal is composed of two components, the receiver thermal noise and the viewed ground radiance. The first component is instrument dependent and hence independent of the target and viewing geometry. The second component is target and viewing geometry dependent, it is proportional to the ground target brightness temperature. In this paper the noise signal measured by C-band scatterometers on-board ERS-2 and Metop-A satellites is analyzed. It was found that the noise signal carries valuable geophysical information, which is worth to be exploited. It is shown that the noise signal varies spatially, temporally and with viewing geometry. Thus, different targets (ocean, sea ice, land) could be easily identified. A comparison was carried out between the scatterometer noise and AMSR-E radiometer brightness temperature and high correlation was found. The noise signal processing (mainly noise subtraction) is discussed, including the assessment of the Noise Equivalent Sigma Zero and the Signal-to-noise ratio. This analysis leads to a better understanding of the noise signal and its impact on the backscatter processing.
We recently developed a method for inter-calibrating spaceborne scatterometers. This method was successfully applied
to ERS-1/ERS-2 and Metop-A/ERS-2 C-band scatterometers. The method is based on combining different natural targets
(ocean, sea ice and rainforest) and associated geophysical models. In this paper, the inter-calibration method is applied
to Metop-A and Metop-B scatterometers data with a focus on the ocean measurements. Additionally, the correction
coefficients obtained from the ocean are compared to and validated on other independent targets i.e., rainforest and sea ice.
Calibration of the scatterometer over ocean is widely used for monitoring and correction of the backscattering coefficients.
The method is based on the assessment of the difference between the measured and the simulated backscatter using NWP
winds and Geophysical Model Functions (GMF’s) such as CMOD5. The method provides the instrument bias against
the GMF. It was found that this bias varies spatially and temporally. This temporal and spatial variation of the bias could
lead to discrepancies of up to 0.1 dB, which is significant compared to the calibration accuracy (0.2 dB). This adds to
the actual bias (instrument drift) an artificial error which is due to the misfit of the input wind distribution. It is shown
that this discrepancy is due to the sensitivity of the GMF to the wind speed distribution and this consequently yields the
calibration over ocean to be sensitive to the wind speed distribution. The wind speed distribution variation in time and
space is analyzed. The sensitivity of the calibration over the ocean to the wind speed distribution variation is assessed.
Finally, a method is proposed to mitigate this variation and thus reduces the misfit error.
A methodology of cross-comparison of C-band spaceborne scatterometers is developed and applied to ERS-1 and ERS-2 scatterometers data. Assuming 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. These methods use natural distributed targets such as rainforest, ocean and sea ice, and are based on geophysical model functions, namely constant gamma model, CMOD5 and sea ice line model. The backscatter from the natural distributed targets is compared against a simulated backscatter providedby the models. Finally, the deviation of the two datasets from the models are comparedto yield a bias between the two scatterometers. The methodologyis applied to ERS-1 and ERS-2 data acquired during the tandem mission in 1996. 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. However, in order to achieve a consistent backscatter data, the scatterometers need to be inter-calibrated. The methodology can be useful to cross-calibrate scatterometers on-board other satellites (e.g. METOP, OceanSat-2, HY2A, etc.) in the view of the Global Climate Observing System guidelines.
The main application of a scatterometer is the determination of the wind speed and direction at the sea surface. This
is achieved by measuring the radar backscattering coefficient in three different directions and inverting these measurements
using a geophysical model function (GMF). The scientific value of the data is directly related to the quality of the
There are currently two european C-band scatterometers operating, one on-board the ERS-2 spacecraft launched in
1995 and the other on-board METOP-A, launched in 2006. The similarity of the two scatterometers is an opportunity
to ensure the continuity of more than 15 years of global scatterometer measurements. To achieve the consistency of the
backscattering coefficients data sets, required for long-term climate studies, an accurate cross-calibration is vital. The
cross-calibration is made possible since the two spacecrafts operate simultaneously from 2006 up to now.
As the backscattering coefficients measured by the scatterometers depend on acquisition time, location on the ground
and on the geometry of the measurements (incidence and look angle), a direct comparison of measurements made by both
instruments is practically impossible.
In particular cases, models can be used to cope with measurement differences. On the rain forest, assumed to be
time-invariant, homogeneous and isotropic, the backscattering coefficient depends only on the incidence angle, and the
constant gamma model can be used to cope with the incidence angle effects. On some ice covered areas (e.g. Greenland
and Antarctica), assuming that the ice surface is isotropic, the ice line model can be used. It is a function of incidence
angle and ice age and depends on the location. On the ocean, which is inherently not stable in time, the CMOD5 GMF
is used. CMOD5 relates the observed backscatter to the geophysical parameters which are the wind speed and wind
direction. Using the last model, measurement biases can be assessed making simultaneous observations unnecessary.