**Research article**
04 Mar 2021

**Research article** | 04 Mar 2021

# A study of polarimetric error induced by satellite motion: application to the 3MI and similar sensors

Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali

**Souichiro Hioki et al.**Souichiro Hioki, Jérôme Riedi, and Mohamed S. Djellali

- Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique Atmosphérique, Lille, 59000, France

- Univ. Lille, CNRS, UMR 8518 – LOA – Laboratoire d'Optique Atmosphérique, Lille, 59000, France

**Correspondence**: Souichiro Hioki (souichiro.hioki@univ-lille.fr)

**Correspondence**: Souichiro Hioki (souichiro.hioki@univ-lille.fr)

Received: 08 Oct 2020 – Discussion started: 20 Oct 2020 – Revised: 19 Jan 2021 – Accepted: 25 Jan 2021 – Published: 04 Mar 2021

This study investigates the magnitude of the error
introduced by the co-registration and interpolation in computing Stokes
vector elements from observations by the Multi-viewing, Multi-channel,
Multi-polarisation Imager (3MI). The Stokes parameter derivation from the
3MI measurements requires the syntheses of three wide-field-of-view images
taken by the instrument at 0.25 s interval with polarizers at different
angles. Even though the synthesis of spatially or temporally inhomogeneous
data is inevitable for a number of polarimetric instruments, it is
particularly challenging for 3MI because of the instrument design, which prioritizes
the stability during a long life cycle and enables the
wide-field-of-view and multiwavelength capabilities. This study therefore
focuses on 3MI's motion-induced error brought in by the co-registration
and interpolation that are necessary for the synthesis of three images. The
2-D polarimetric measurements from the Second-generation Global Imager
(SGLI) are weighted and averaged to produce two proxy datasets of the 3MI
measurements, with and without considering the effect of the satellite
motion along the orbit. The comparison of these two datasets shows that the
motion-induced error is not symmetric about zero and not negligible when the
intensity variability of the observed scene is large. The results are
analyzed in five categories of pixels: (1) cloud over water, (2) clear sky over water, (3) coastlines, (4) cloud over
land, and (5) clear sky over land. The
most spread distribution of normalized polarized radiance (*L*_{p})
difference is in the cloud-over-water class, and the most spread
distribution of degree of linear polarization (DOLP) difference is in the
clear-sky-over-water class. The 5th to 95th percentile ranges of *L*_{p} difference for
each class are (1) [$-\mathrm{0.0051},\mathrm{0.012}$], (2) [$-\mathrm{0.0040},\mathrm{0.0088}$], (3)
[$-\mathrm{0.0033},\mathrm{0.012}$], (4) [$-\mathrm{0.0033},\mathrm{0.0062}$], and (5) [$-\mathrm{0.0023},\mathrm{0.0032}$]. The same
percentile range of DOLP difference for each class are (1) [$-\mathrm{0.023},\mathrm{0.060}$],
(2) [$-\mathrm{0.043},\mathrm{0.093}$], (3) [$-\mathrm{0.019},\mathrm{0.082}$], (4) [$-\mathrm{0.0075},\mathrm{0.014}$], and (5)
[$-\mathrm{0.011},\mathrm{0.016}$]. The medians of the *L*_{p} difference are (1) 0.00035, (2)
0.000049, (3) 0.00031, (4), 0.000089, and (5) 0.000037, whereas the medians
of the DOLP difference are (1) 0.0014, (2) 0.0015, (3) 0.0025, (4) 0.00027,
and (5) 0.00014. A model using Monte Carlo simulation confirms that the
magnitude of these errors over clouds are closely related to the spatial
correlation in the horizontal cloud structure. For the cloud-over-water
category, it is shown that the error model developed in this study can
statistically simulate the magnitude and trends of the 3MI's motion-induced
error estimated from SGLI data. The obtained statistics and the simulation
technique can be utilized to provide pixel-level quality information for 3MI
Level 1B products. In addition, the simulation method can be applied to the
past, current, and future spaceborne instruments with a similar design.

The Multi-viewing, Multi-channel, Multi-polarisation Imager (3MI) is a planned spaceborne sensor on the MetOp Second Generation-A (MetOp-SG-A) satellite platform. The sensor consists of two wide-field-of-view cameras with narrowband wavelength filters, inheriting the legacy of the POLarization and Directionality of the Earth's Reflectance (POLDER) sensor. The rotating wheel carries 31 filters that enable polarimetric measurements at nine wavelengths and non-polarimetric measurements at three wavelengths (Fougnie et al., 2018). The spatial resolution at nadir is 4 km, and the instantaneous swath is 2200 km. The MetOp-SG-A series expects the launch of three identical platforms with 7-year intervals, providing continuous and homogeneous monitoring of the Earth's weather and climate for 21 years. The 3MI sensors on these platforms are anticipated to perform multi-viewing and multi-channel polarimetric observation at unparalleled spatial and temporal scales.

As increasingly advanced retrieval techniques are used to extract atmospheric composition parameters from multispectral and polarimetric observations (Knobelspiesse et al., 2012; Fu and Hasekamp, 2018; Dubovik et al., 2019), the knowledge and reduction of uncertainties associated with polarimetric measurements become more and more critical. In particular for all techniques relying on optimal estimation, the correct understanding of observational uncertainties, as discussed in detail by Povey and Grainger (2015), is pivotal to obtain meaningful and successful retrieval. The best-described uncertainty in the Level 1B product is the radiometric uncertainty. Table 1 summarizes the radiometric requirements of the 3MI sensor according to Fougnie et al. (2018). With no onboard calibration, the 3MI sensor needs to rely on in-flight vicarious calibration techniques to monitor and assess the instrument radiometric performance. Among vicarious calibration techniques that are developed for the PARASOL sensor (Fougnie, 2016) and new techniques (e.g., Djellali et al., 2019), some are useful for an independent absolute calibration and others are more suited to perform inter-band or inter-sensor cross-calibration. For example, the Rayleigh scattering method and the new sunglint method can be used to monitor the absolute calibration coefficient, whereas existing sunglint method is designed for inter-band calibration. The temporal evolution of calibration coefficients could be monitored by the calibration over Antarctica, and inter-pixel calibration could be performed over deep convective clouds. The synergies with other sensors of the MetOp-SG-A platform will also be beneficial (Fougnie et al., 2018), e.g., through a cross-calibration over a relatively invariant target over the African and Arabian deserts.

The radiometric performance of the instrument, however, is not the only factor driving the overall measurement uncertainties, especially for polarimetric observations where the useful quantities (Stokes parameters) are not directly measured but derived, e.g., from a set of different radiances. Therefore, the polarimetric performance of the 3MI instrument will depend on both the radiometric accuracy and the process used to derive the Stokes parameters. This paper investigates in particular the uncertainty induced by this derivation process in order to provide an instantaneous, realistic, and quantitative estimate of polarimetric error at pixel level for the 3MI instrument or similar sensors.

The 3MI instrument derives the first three Stokes parameters (*I*, *Q*, and
*U*) by synthesizing three wide-field-of-view images that are taken
sequentially at a 0.25 s interval. Each of the three images is acquired with
the linear polarizer oriented in different directions with the polarizing axis being
−60, 0, and 60^{∘} with respect to the
direction of the satellite's orbital motion (along-track direction).
However, within the 0.25 s interval, the instantaneous field of view
(IFOV) shifts by 1.8 km (0.45 pixel) on the ground because of the motion of
the satellite. The shifts between the acquired images require interpolation and co-registration to compensate for the satellite's
along-track motion before the computation of the Stokes parameters. These
non-simultaneous acquisitions therefore introduce a possible source of error
in the polarimetric observation by the 3MI sensor due the co-registration
and the interpolation required to match the three images.

This kind of error due to the synthesis of spatially or temporally inhomogeneous data is inevitable for polarimetric instruments without a beam splitter, including the 3MI, because it is impossible to measure multiple radiometric quantities along the same line of sight simultaneously and independently. The practical solution is to spatially or temporally change the polarimetric modulation in a way that minimizes the errors while at the same time providing measurements that serve the mission objectives. For the details of the different instrument designs, readers are referred to Dubovik et al. (2019) and references therein. The rotating-filter design of the 3MI and POLDER sensors is suited for the spatially continuous wide-field-of-view measurements, but its asynchronous acquisition may result in a significant polarimetric error, particularly over inhomogeneous scenes.

The magnitude of error due to the interpolation and co-registration is
expected to be neither spatially uniform nor symmetric about zero, and
it therefore cannot be removed by spatiotemporal averaging. This is because
the intensity in the original images and the polarized normalized radiance
(*L*_{p}) are not linearly related. We define the normalized radiance (*L*)
and, in analogy, the polarized normalized radiance as follows:

where *I* is the intensity, *Q*_{i} and *U*_{i} are the second and third
elements of Stokes vector in terms of intensity, and *E*_{0} is the beam flux of
the extraterrestrial solar radiation. The degree of linear polarization
(DOLP) is defined as follows:

Assuming three ideal linear polarizers with a perfect alignment, the normalized radiance and the polarized normalized radiance can be computed from the original intensity measurements $\left({X}_{\mathrm{m}\mathrm{60}},\phantom{\rule{0.125em}{0ex}}{X}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}{X}_{\mathrm{p}\mathrm{60}}\right)$ as follows:

where *X*_{m60} corresponds to the intensity with the polarizer aligned
−60^{∘} off from the along-track direction, *X*_{0} to 0^{∘}
off, and *X*_{p60} to +60^{∘} off. Equation (5) demonstrates that
the error in the original images and the normalized polarized radiance are
not in linear relation. Rather, the motion-induced error tends to suppress
the polarization for strongly polarized target and tends to enhance the
polarization for weakly polarized target. This is because *L*_{p} is bounded
by $\mathrm{0}<{L}_{\mathrm{p}}<L$ (i.e., $\mathrm{0}<\mathrm{DOLP}<\mathrm{1}$), and any random error near
*L*_{p}=0 increases *L*_{p}, whereas that near *L*_{p}=*L* decreases *L*_{p}.
From this discussion, we emphasize again that the motion-induced error
likely depends on the observation target, and the spatial distribution may
not be uniform.

As the quality of the retrieval product hinges on the quality of the radiance product and ancillary information that delivers the reliability, the quantification of the error triggered by the interpolation and co-registration is significantly useful in the quality control of both Level 1B and Level 2 data products. Some studies have already investigated the impact of the polarimetric accuracy on the performance of retrieval algorithms (e.g., Hasekamp et al., 2019), but the quantification of the error in Level 2 data products is still challenging not only because the retrieval error highly depends on the retrieval algorithm but also because the characterization of errors in Level 1B data products is still largely limited to the radiometric uncertainty. By addressing the characteristics of the motion-induced error, this study therefore adds a new dimension to the reliability of the Level 1B and Level 2 data products.

Section 2 describes the data and methods used in this study, Sect. 3 describes the results, and Sect. 4 discusses the simulation of the error statistics. Conclusions are given in Sect. 5.

## 2.1 Data

In this study, we produce 3MI proxy polarimetric measurements from the actual high-resolution polarimetric measurements obtained by the Second-generation Global Imager (SGLI) aboard the Global Change Observation Mission – Climate (GCOM-C) satellite (Imaoka et al., 2010). The SGLI sensor provides polarized normalized radiances at 1 km nadir resolution within a 1150 km wide swath. We selected the SGLI data as a source of our proxy data, because they provide the global polarimetry at higher resolution than the 3MI; it suffers less from the co-registration and interpolation errors, and its orbit and swath are compatible to that of 3MI.

Because of the push-broom design of the SGLI sensor, SGLI's interpolation error is expected to be negligible compared to that introduced by the 3MI's more complex co-registration and interpolation. Figure 1 is a schematic diagram showing the difference between the observation geometry of the 3MI and that of the SGLI. Both sensors measure the first three elements of the Stokes vector through the measurements of intensity through linear polarizer oriented at three different directions. The pronounced difference is that the 3MI sensor acquires the three wide-field-of-view images asynchronously, while the SGLI sensor acquires three cross-track profiles of intensity with 170 m offset. Because the SGLI sensor design suffers less from the shift between the acquisition of three images, we treat the SGLI measurement as truth and evaluate the magnitude of error introduced by the interpolation and co-registration by producing 3MI-proxy data from the SGLI data.

One week of global SGLI Level 1B data near the 2008 September equinox (20–26 September) are used. In this study, we performed our analysis based on the polarimetric data in the Level 1B POLDK product and visible, near infrared, and thermal infrared data in Level 1B VNRDK, VNRDL, IRSDK, and IRSDL products. The product name conventions are described in the GCOM-C Data Users Handbook (Japan Aerospace Exploration Agency GCOM-C project team, 2018a), but in short, the first three letters indicate the subsystem of the SGLI instrument (“POL” for polarization, “VNR” for visible and near infrared, and “IRS” for infrared scanning subsystems), the fourth letter “D” indicates the observation mode (“D” for daytime data), and the last letter indicates the resolution (“K” for 1 km resolution, “L” for aggregated 1 km resolution). As the GCOM-C satellite is in the sun-synchronous orbit at 800 km altitude with descending-node local time of 10:30 LT, the data from the SGLI are valuable to simulate measurements from the 3MI that are anticipated also in the sun-synchronous morning orbit at 830 km altitude.

## 2.2 Methods

### 2.2.1 Estimation of the motion-induced error

As the nominal resolution of the 3MI is 4 km and that of the SGLI is 1 km,
4×4 SGLI pixels are aggregated to produce a 3MI pixel. We repeat the
aggregation for every 4×4 SGLI pixel blocks in three original
(“Lt_P2_m60”, “Lt_P2_0”, and “Lt_P2_p60”)
radiance datasets in the SGLI Level 1B product. These three datasets correspond
to the radiance measured at 0.869 µm with polarizers at three
different directions at −60, 0, and 60^{∘}
with respect to the along-track axis of the satellite (i.e., ${X}_{\mathrm{m}\mathrm{60}},{X}_{\mathrm{0}},\phantom{\rule{0.125em}{0ex}}{X}_{\mathrm{p}\mathrm{60}}$). From the aggregated data, we compute the normalized
radiance and polarized normalized radiance by Eqs. (4) and (5). The results
are referred to as “reference data” hereafter.

A similar method is applied to produce the 3MI “proxy data” which are compared to the reference data to estimate the magnitude of the error. The proxy data are different from the reference data in that they incorporate the effects of the satellite's motion and interpolation. To simulate the motion of satellite between the acquisition of each image (1.8 km), we compute the contribution of every SGLI pixel to the shifted 4×4 grid by the following equation:

where *i* is the index of line, Π_{a,b}(*x*) is a boxcar
function that is 1 in the interval (*a*,*b*) and 0 otherwise, and
*s* is the amount of shift in SGLI pixel size (i.e., +1.8 or −1.8 in our
case). The results of the calculation is shown in Table 2. The contribution
factors are multiplied by the measured radiance in every SGLI pixel to
perform the weighted average.

After computing the weighted average, we obtain the shifted, aggregated images of “Lt_P2_p60” and “Lt_P2_m60”. As these two images are shifted with respect to the “Lt_P2_0” image, the unshifting process is performed as a next step by interpolating the intensity at the pixel center location of the “Lt_P2_0” image. The linear interpolation is selected for the simplicity and the locality, but the error estimation described in this subsection as well as the error simulation detailed in Appendix A could be performed with other methods of interpolations if deemed necessary for specific applications. The final contribution factors are computed with the following equation:

where sgn(s) is sign of the shift. The results of
the computation are summarized in Table 3. From these unshifted aggregated
images, we compute the normalized radiance (*L*) and the normalized
polarized radiance (*L*_{p}) by Eqs. (4) and (5), and we call them the proxy
data.

The comparison of the proxy and reference data is performed on a
pixel-by-pixel basis. In every pixel, the difference in polarized normalized
radiance Δ*L*_{p} and the difference in degree of linear
polarization ΔDOLP are computed. These differences are
attributed to the error induced by the pixel co-registration and the
interpolation.

### 2.2.2 Classification of data

To further the analysis of the motion-induced error, we classify pixels into
five categories: clouds over water, clear sky over water, clouds over land,
clear sky over land, and coastline. The classification is based on the
land–water flag in the SGLI Level 1B dataset and the cloud flag algorithm
developed for this study. The activity diagram (flowchart) of the cloud
flag algorithm is shown in Fig. 2, while individual test conditions are
listed in Tables 4 (pixels over water) and 5 (pixels over land). To compute
cloud flags in the SGLI Level 1B POLDK product's coordinate, other SGLI L1B
products are projected onto the POLDK grid. Once the cloud flag is derived,
both land–water flag and cloud flag are extended into the along-track
directions by 20 SGLI pixels to cover all SGLI pixels used for the error
estimation and to minimize the pixels on boundaries. A pixel is classified
as “cloud over water” when it is flagged as “confidently cloudy” and
“land cover 0 %”, “clear sky over water” when it is flagged as
“confidently clear” and “land cover 0 %”, “clear sky over land” when
it is flagged as “confidently clear” and “land cover 100 %”, “cloud
over land” when it is flagged as “confidently cloudy” and “land cover
100 %”, and “coastline” when land cover is between 5 % and 95 %.
Only pixels with glint angle greater than 35^{∘} are collected for
cloud-over-water, clear-sky-over-water, and coastline classes to avoid
contamination by sunglint.

In every class of data, the normalized polarized radiance differences
(Δ*L*_{p}) are regrouped according to the along-track
Laplacian that is defined as follows:

where *X*_{−1} is the *X*_{0} of the adjacent pixel in the negative
along-track direction, and *X*_{+1} is the *X*_{0} of the adjacent pixel in
the positive along-track direction. The along-track Laplacian is a measure
of the nonlinearity of the local intensity change in the along-track
direction. We select this metric because the linear interpolation in Eq. (7)
removes the linear error term, and the highest remaining term is
characterized by the Laplacian. In other words, we estimate that the major
source of the error is the (nonlinear) spatial inhomogeneity of the total
radiance that pass through the linear interpolation performed in the
co-registration.

The DOLP differences (ΔDOLP) are regrouped according to the
along-track Laplacian divided by *L*, i.e., ${L}_{\mathrm{AT}}/L$. This is because we
found from the preliminary study that the relation between Δ*L*_{p} and *L*_{AT} does not strongly depend on the value of *L*. As the
DOLP is defined by Eq. (3), utilizing ${L}_{\mathrm{AT}}/L$ as a regrouping parameter
is deemed a reasonable choice.

## 3.1 Estimation of the error

The difference in DOLP between the proxy and reference data is shown in Fig. 3a with corresponding visible composite in Fig. 3b. The figure covers
the western French coast, English Channel, and southern Britain. Figure 3a shows that the DOLP difference can reach more than 0.2
along the coastlines, particularly in a part where the coastline runs in the
cross-track direction. There is little negative DOLP difference near
positive values, implying that the distribution of the DOLP difference is
not symmetric about zero. Along the edges of thin clouds, e.g., over
the Atlantic in the western part of Fig. 3, positive and negative DOLP
differences are mixed. Note that similar clouds over the ocean and land
give different magnitudes of DOLP differences, presumably because of the
background normalized radiance (*L*). As the DOLP is divided by *L*, errors
over dark pixels (e.g., ocean and water near coastlines) tend to be
pronounced. For the same reasons, the magnitude of the error is smaller over
thick clouds covering the eastern end of English Channel than along
coastlines. Figure 3 demonstrates that the magnitude of motion-induced error
is not negligible over the scene where intensity variation is large.

It is not only over coastlines that the distribution of error is asymmetric
about zero. In Figs. 4 and 5, we show histograms of degree of linear
polarization (DOLP) and polarized normalized radiance (*L*_{p}) differences
for five different classes of pixels defined in Sect. 2.2.2: cloud over
water, clear sky over water, cloud over land, clear sky over land, and
coastline at wavelength *λ*=0.869 µm, in addition to the
clear sky over land at wavelength *λ*=0.674 µm. Figures 4 and 5 show that the distributions of the proxy–reference differences are not
symmetric about zero and rather skewed to the right (having fat tail in the
right end of the histogram) in all classes of data. The tail of DOLP
difference histogram is particularly fat for cloud-over-water and coastlines
classes, where sharp reflectance gaps near coastal water or cloud edges
induce strong polarization artifacts.

The asymmetry can be confirmed from the 5th to 95th percentile range and
25th to 75th percentile range (interquartile range) of the estimated DOLP
and *L*_{p} error as presented in Table 6. The median of distribution shown
in Table 7 also indicates that the distribution is asymmetric not only about
zero but also about the median. The clear-sky-over-water class has the most
spread distribution of DOLP differences, and the 5th-95th percental range is
$\left[-\mathrm{0.043},\mathrm{0.093}\right]$. On the other hand, the cloud-over-water class
has the most spread distribution of *L*_{p} differences, and the 5th to 95th
percentile range is $[-\mathrm{0.0051},\mathrm{0.012}]$. The asymmetry of the distribution
implies that the error cannot be completely canceled out by computing the
spatial average.

Figure 6 shows the proxy–reference DOLP differences (ΔDOLP)
as a function of the along-track Laplacian (*L*_{AT}). The red curve
indicates the median of ΔDOLP values in each bin of
*L*_{AT}, and the gray shadow the interquartile range of ΔDOLP values. As the *L*_{AT} increases, the median of the error as well
as the spread increase. However, an evident difference exists between the
clear sky over land at *λ*=0.869 and *λ*=0.674. The median of
*λ*=0.674 shows a slight increase as a function of *L*_{AP} but
remains small (within 0.01). At *λ*=0.869, for cloud-over-land,
cloud-over-water, and clear-sky-over-water classes, the spreads of the
distributions saturate when ${L}_{\mathrm{AT}}/L$ increases, while for coastlines the
spread of the distribution does not saturate. In the clear-sky-over-land
class, the spread reaches a peak at ${L}_{\mathrm{AT}}/L=\mathrm{0.15}$. The strong dependence
of medians to the along-track Laplacian imply that the magnitude of the
error is predictable from the image of *X*_{0} (intensity with polarizer at
0^{∘} off the along-track direction), which is used to compute the
*L*_{AT} and observationally available.

The distribution of polarized normalized radiance difference
(Δ*L*_{p}) is also shown to be a function of the along-track
Laplacian (*L*_{AT}), and Fig. 7 show the increase in median with increasing
*L*_{AT}. The spectral difference for clear-sky-over-land class still exists,
but less pronounced than that for the DOLP. The median of *λ*=0.674 µm is closer to 0 than the median of *λ*=0.869 in
the range [0, 0.02] where a majority of pixel exists. It is plausible that
the low land inhomogeneity at *λ*=0.674 µm results in lower
*L*_{p} and DOLP errors. In a similar way as for the DOLP difference, the
spreads of distributions for cloudy classes saturate at about *L*_{AT}=0.1,
whereas it does not for the coastline class. The spread for the clear-sky
pixels over land does not show a maximum as seen in the distribution of the
DOLP differences.

The target polarimetric accuracy of the 3MI sensor is $\mathrm{5}\times {\mathrm{10}}^{-\mathrm{4}}$ in
terms of polarized reflectance over homogeneous clear sky over ocean
(Fougnie et al., 2018). Figure 8 shows the fraction of pixels that satisfy
this condition as a function of the along-track Laplacian (black points and
lines). The blue points and lines indicate the fraction of pixels that
satisfy the POLDER specification, which is $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{3}}$ in terms of
polarized normalized radiance. The first point represents the data with
*L*_{AT}<0.005, and the interval is 0.005 up to 0.01 (21st bin). Given that
68.2 % of data fall within the ±1 standard deviation when the
error distribution follows the normal distribution, we could interpret that
the specification is well satisfied in the bin with the fraction larger than
68.2 % (green horizontal line). Over very homogeneous scenes with low
along-track Laplacian, indeed we find that the requirements are satisfied
for both 3MI and POLDER, but obviously not over as the along-track Laplacian
(i.e., inhomogeneity of the scene) increases. This result is consistent with
the study by Fougnie et al. (2007), which shows that the POLDER data over
homogeneous scene satisfy the requirement.

## 4.1 Prediction of error by the Monte Carlo model

The error estimation for the 3MI sensor is possible because the SGLI sensor
has a similar swath and orbit as the 3MI with higher spatial resolution and
less anticipated motion-induced errors. However, not all sensors have such
corresponding sensor to be used for the error estimation. This is our
motivation to develop a Monte Carlo model to predict the motion-induced
error over cloud-over-water class, which has the highest *L*_{p} error and
the normalized radiance structure can be randomly generated using the
power-law spectrum and inverse Fourier transform. Technical detail of the
method is described in Appendix A.

Figure 9 shows the predicted magnitude of error based on the Monte Carlo
simulation for cloudy pixels over water. The simulation predicts the median
(red curves) that matches well to the estimation based on the SGLI data
(dotted curves). As the empirical 95th percentile of the ${L}_{\mathrm{AT}}/L$ is 0.39
and that of *L*_{AT} is 0.070, the abscissas of Fig. 9 cover the large
portion of the plausible range of the data. A slight overestimation of the
DOLP difference occurs in the entire range, but the maximum difference is
about $\mathrm{1}\times {\mathrm{10}}^{-\mathrm{3}}$ (i.e., 2 % of the median DOLP, 0.041) in the bin
$\mathrm{0.32}<{L}_{\mathrm{AT}}/L<\mathrm{0.36}$. The difference between the predicted and estimated
normalized polarized radiance is less than 10^{−3} in *L*_{AT}<0.1. The
difference reaches $\mathrm{6.1}\times {\mathrm{10}}^{-\mathrm{4}}$ (i.e., 4.3 % of the median
*L*_{p}, 0.0032) in the bin $\mathrm{0.09}<{L}_{\mathrm{AT}}<\mathrm{0.1}$. Overall, the simulation can
predict the median of the error estimation at better than 5 % accuracy.

The successful prediction of the motion-induced error by the statistical
model implies that the error distribution in the cloud-over-water class
inferred from the SGLI data is a result of the inherent horizontal structure
of clouds. The method is likely to be applicable to other spatial scales to
predict the magnitude of motion-induced error of the past, current, and
future polarimetric instruments sharing similar designs. In the application
to other satellite data, it is necessary to collect four statistics that
are used in the simulation, namely, (1) normalized radiance distribution,
(2) normalized-radiance-binned subpixel inhomogeneity (weighted variance),
(3) normalized-radiance-binned DOLP, and (4) normalized-radiance-binned
AOLP (angle of linear polarization). However, most of these statistics are readily available from
observations (past and current sensors) or simulations (future sensors). The
exception is the subpixel inhomogeneity, which may be a challenging parameter
to obtain if a past or current sensor has no colocated high-resolution
imaging measurements, but one could replace it with an alternative measure
of the local inhomogeneity, e.g., the variance of *X*_{0}
in the along-track direction. The modeling technique in this work is
therefore particularly helpful for the preparation for future missions.

## 4.2 Sensitivity of the Monte Carlo simulation to the assumed power-law parameter

In this study, our simulation of the error statistics assumes power-law coefficient ($-\mathrm{5}/\mathrm{3}$), but it is known that the power-law coefficient varies depending on the type of clouds and spatial scale. A well-known example is for the scale break that occurs in the Landsat radiance data over stratocumulus clouds. When the spatial scale is less than a few hundred meters, the power-law coefficient becomes significantly smaller (i.e., the spectrum becomes steeper). Smaller power-law coefficient means that low-frequency structures pronounce more than high-frequency structures. As a result, when a lower power-law coefficient is specified, the simulated 2-D normalized radiance field looks more horizontally homogeneous, introducing a smaller error in simulated 3MI polarimetric measurements.

To test the sensitivity of our prediction to the specified power-law
coefficient, we simulated the error statistics for two other extreme cases.
Figure 10 shows the magnitude of error in the DOLP and *L*_{p} when no
correlation between neighboring subpixels is assumed, and Fig. 11 shows the magnitude when a
smaller power-law coefficient (−3.0) is assumed.

When there is no spatial correlation in the cloud intensity, the magnitude
of simulated error is larger than the estimates based on the SGLI data. The
median of the SGLI-based estimate is out of the interquartile range of the
simulation (gray shading in Fig. 10) for a significant range of ${L}_{\mathrm{AT}}/L$
and *L*_{AT}. On the other hand, Fig. 11 shows that the magnitude of
simulated error is slightly lower when a smaller power-law coefficient
(−3.0) is assumed. In addition, we note that the SGLI error estimation for
the clouds-over-land category also shows the slope between these two
extremes.

From these results, we propose that the spatial correlation of intensity due to the natural cloud structure should be considered when predicting the statistics of motion-induced error over clouds. For the 3MI sensor, the higher resolution observations provided by the METimage sensor (Wallner et al., 2016) aboard the same MetOp-SG-A platform might be used to further constrain the along-track scene spatial correlation and therefore be useful to improve the pixel-level polarimetric uncertainty estimates.

## 4.3 Application of the results

In this study, we estimated 3MI's motion-induced error from the SGLI data. A
straightforward implication of the correlation between the motion-induced
errors and the along-track Laplacian is that it is possible to predict the
quality of polarization data in every pixel. For example, from the
statistics we obtained in this study, the median bias and the standard
deviation for every pixel can be provided as a function of *L*_{AT},
which is a derivable quantity through *X*_{0}. The challenge of this
approach is that the view angle of the SGLI is fixed at positive or negative
45^{∘} in the along-track direction and therefore representative of
only a part of 3MI's view direction. However, as the error estimate shows,
the spatial inhomogeneity primarily determines the motion-induced error, and
statistics obtained in this study are still useful. In addition, the
intensity variation over the land is primarily due to the land surface
inhomogeneity, and month-long or year-long statistics would be
beneficial to define the standard motion-induced error for aerosol retrieval
purposes.

We also confirmed in this study that the motion-induced error over the cloud-over-water class can be predicted with a Monte Carlo model. The advantage of
the simulation is that it does not require high-resolution polarimetry to
perform the error analysis. In the application to the 3MI data, among the
four required input statistics, three statistics other than cloud subpixel
inhomogeneity are readily available from the 3MI sensor itself. The cloud
inhomogeneity parameter can be provided either from the colocated METimage
sensor or by replacing with alternative measure of the inhomogeneity,
exploiting the stable reflectivity power spectrum in the few kilometers
scale. As the motion-induced *L*_{p} error for the cloud-over-water class
was the largest, the simulation technique is helpful to determine the upper
limit of the motion-induced *L*_{p} error.

From the high-resolution global polarimetric observation by the SGLI, we estimate 3MI's polarimetric error induced by the co-registration and interpolation that compensates for the satellite's along-track motion during the acquisition. The estimates show that the magnitude of the interpolation error is not negligible nor symmetric about the zero, particularly over the locations where intensity variation is large.

The asymmetric distribution of the motion-induced error is confirmed in all
four categories of analyzed pixels: clear sky over land, clouds over land,
clouds over water, and coastlines. The clear-sky-over-water class had the most
spread distribution of the degree of linear polarization (DOLP) difference,
whereas the cloud-over-water class had the most spread distribution of the
polarized normalized difference (*L*_{p}) differences. The 9th to 95th
percentile range was $\left[-\mathrm{0.043},\mathrm{0.093}\right]$ for the DOLP differences
and $\left[-\mathrm{0.0051},\mathrm{0.012}\right]$ for the *L*_{p} differences. These
differences strongly depend on the along-track Laplacian that characterizes
the nonlinear change in the normalized radiance (*L*_{p} difference) or
that divided by the normalized radiance (DOLP difference). As the
along-track Laplacian is available from the observation, a pixel-level
quality information can be obtained based on the statistics presented in
this study. In addition, it is possible to perform the statistical
estimation of error when the subpixel inhomogeneity is available as an
input. Either procedures proposed in this study would help ensure the
reliability of the radiance products and downstream applications including
retrieval products as well as data assimilation.

The error statistics based on the SGLI data and Monte Carlo simulation agree within 5 % in terms of median bias, implying the predictability of the error for cloudy pixels at an arbitrary spatial scale. The sensitivity study on the assumed power-law coefficient proved the need for the spatial correlation to be included in the error prediction. The method of prediction is applicable to past, current, and future missions with a polarimetric instrument based on a similar design, paving a way to better predict the performance of the instrument on orbit at the stage of planning. Finally, although simulations presented here are limited to cloudy scenes, a similar diagnosis might be made for land surfaces under clear-sky conditions for use in aerosol remote sensing applications.

The statistics of the error are simulated with a Monte Carlo model for pixels in the cloud-over-water class. In summary, we first generate synthetic 2-D normalized radiance fields by the inverse 2-D Fourier transform, assuming the power-low spectrum of the normalized radiance distribution. We then proceed with the weighting and aggregation equivalent to the method described in Sect. 2.1. This process is repeated for 10 million samples to realize meaningful statistics. The details of the method are described in the rest of this Appendix.

The first step is to apply an inverse 2-D Fourier transform to the assumed power-law spectrum with Gaussian noise as in the method described by Iwabuchi and Hayasaka (2002). A number of studies show that the intensity spectrum of the cloud field follows the power law (Cahalan and Snider, 1989; Davis et al., 1997; Marshak et al., 1995, 1998; Oreopoulos et al., 2000), and the slope is known to be a function of horizontal spatial scale. For scales greater than a few hundred meters and less than a few hundred kilometers, the power-law coefficient is between −1.5 and −2.0, and the value increases for the smaller. We select $-\mathrm{5}/\mathrm{3}$ (1.666…) as the power-law coefficient in our simulation, as it is the value commonly referred to. The impact of this choice is evaluated by the simulations with varied parameter in Sect. 4.2.

Every synthetic 2-D normalized radiance field that is generated in this way is different. They are, however, statistically centered at zero, and their variance depends on the prescribed power-law coefficient. In the second step, to make the simulation consistent with the SGLI-based estimation, we scale and add an offset to each realization as in the following equation:

where *L*_{(i,j)} is the final 2-D normalized radiance field, *x*_{(i,j)}
the output of the inverse Fourier transform, *a* the scaling coefficient, and
*b* the offset. The scale *a* and offset *b* are determined so that the
pixel intensity $\stackrel{\mathrm{\u203e}}{L}={\sum}_{i}{w}_{i}{L}_{i}$ and weighted
variance $V={\sum}_{i}{w}_{i}{\left({L}_{i}-\stackrel{\mathrm{\u203e}}{L}\right)}^{\mathrm{2}}$ follow
the observed empirical distribution function (EDF) of normalized radiance
${\widehat{F}}_{L}\left(l\right)$ and weighted variance ${\widehat{F}}_{\mathrm{V}}(v;l)$. These EDFs are
computed from the SGLI data upon performing the error estimation described
in Sect. 2.1. The EDF of variance ${\widehat{F}}_{\mathrm{V}}(v;l)$ is computed for each
small normalized radiance interval, spanning from 0 to 0.9 with 0.01 width,
from 0.9 to 1 with 0.05 width, and from 1 to 1.5 with 0.5 width. The weight
*w*_{i} is the arithmetic mean of final weights in Table 3 for every line
(i.e., for line 5, it is 1/16 + 9/320 + 11/320 = 1/240).

The third step is to compute *X*_{1(i,j)}, *X*_{2(i,j)}, and *X*_{3(i,j)} from the 2-D normalized radiance field *L*_{(i,j)};
the angle of linear polarization (AOLP) *χ*; and the degree of linear
polarization (DOLP) *δ*. The AOLP is sampled so that it follows the
EDF of AOLP ${\widehat{F}}_{\mathit{\alpha}}(\mathit{\alpha};l)$, and the DOLP is sampled so that
it follows EDF of DOLP ${\widehat{F}}_{\mathit{\delta}}(\mathit{\delta};l)$. The AOLP is assumed
constant over subpixels for each realization, but the DOLP is sampled for
each subpixel because of the strong intensity dependence with weak spatial
correlation. Figure A1 shows the strong decreasing trend of the DOLP with
intensity. We note here that the statistics of the intensity-binned DOLP and
the statistics of AOLP have been found necessary for the reasonable
simulation of the polarimetric error. This is presumably because the DOLP
varies significantly as a function of reflectivity at cloud boundaries and
over thin clouds, and the AOLP determines the relative contribution of error
in *X*_{1} and *X*_{3} images to the polarized normalized radiance
(*L*_{p}).

Once *X*_{1(i,j)}, *X*_{2(i,j)}, and *X*_{3(i,j)} are obtained,
the last step is to proceed through with the weighting and aggregation
equivalent to the method described in Sect. 2.1. This entire process of
2-D Fourier transform, scaling, and ${X}_{\mathrm{1}}-{X}_{\mathrm{2}}-{X}_{\mathrm{3}}$ derivation is
repeated 10 million times.

GCOM-C/SGLI data used in this study are available from the G-Portal website and ftp service operated by Japan Aerospace Exploration Agency (ftp://ftp.gportal.jaxa.jp/standard/GCOM-C/GCOM-C.SGLI/L1B/1/) (Japan Aerospace Exploration Agency GCOM-C project team, 2018b). The user registration is required through the website (https://gportal.jaxa.jp/gpr/, last access: 23 February 2021) to access the data.

SH and JR conceived of the methodology and outlined the project. SH performed the study and composed the article. JR encouraged and supervised the project. MSD contributed the research activities through discussions on the results.

The authors declare that they have no conflict of interest.

We appreciate Japan Aerospace Exploration Agency (JAXA) for providing the GCOM-C/SGLI data needed for the research. This research is conducted in a framework of jointly funded postdoctoral fellowship by Centre National d'Études Spatiales (CNES) and the Make Our Planet Great Again (MOPGA) program by Campus France. The authors are grateful for the financial support and various encouragements by CNES, MOPGA, and the Laboratoire d'Optique Atmosphérique. Our gratitude extends toward Bertrand Fougnie, who provided us valuable comments regarding the 3MI and POLDER mission requirements. It is our pleasure to hereby express our gratitude to three reviewers, Frans Snik, Otto Hasekamp, and Kirk Knobelspiesse, for their constructive and positive comments.

This work was supported by CNES through the TOSCA program under grant no. 5413/MTO/4500065502.

This paper was edited by Andrew Sayer and reviewed by Otto Hasekamp, Kirk Knobelspiesse, and Frans Snik.

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