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ALOS PALSAR BACKSCATTER MOSAICS FOR QUEENSLAND, AUSTRALIA

John Armston 1, João Carreiras 2, Richard Lucas 3, Masanobu Shimada 4

1 Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, The University of Queensland,

Remote Sensing Centre, Queensland Department of Environment and Resource Management, 80 Meiers Road, Indooroopilly, Queensland, Australia

Tel: +61 7 3896 9696, Email: john.armston@qld.gov.au

2 Tropical Research Institute (IICT), Department of Natural Sciences, Rua João de Barros, 27 1300-319 Lisboa, Portugal

3 Institute of Geography and Earth Sciences, Aberystwyth University,

Aberystwyth, Ceredigion, Wales, SY23 3DB, Wales, UK

4 Japan Aerospace Exploration Agency (JAXA), Earth Observation Research Center (EORC), Sengen 2-1-1, Tsukuba, Ibaraki, Japan, 305-8505

Abstract Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) 50 m spatial resolution dual polarisation (HH and HV) strip data were provided for northern Australia as part of the Japanese Space Exploration Agency (JAXA) Kyoto and Carbon (K&C) Initiative. In contrast to other regions of the world, large differences in the backscattering coefficient were observed between strips acquired on different dates and paths. In a previous study (Lucas et al., 2010a), these changes were attributed to apparent differences in the dielectric properties of the surface, with this established through reference to soil and effective vegetation water content estimates from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and SILO daily rainfall surfaces. This paper presents an overview of the approach used to generate spatially and temporally consistent large area mosaics of ALOS PALSAR strip data in Queensland Australia. Primary results showed minimum L-band HH compositing produced near-seamless mosaics. However, observations acquired over at least one year were required to achieve best results. Changes in surface moisture can cause greater than 3 dB changes in HH polarised backscatter. The resulting multi-date mosaics are now being used for detailed regional assessments of land cover, above-ground biomass, mangroves and vegetation condition.

Introduction The launch of the Japanese Space Exploration Agency (JAXA) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) in 2006 paved the way for regular, systematic and global L-band radar observations of the Earth’s environments. To demonstrate the capacity of

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these data for regional characterisation, mapping and monitoring of forests, wetlands and deserts, the JAXA Kyoto and Carbon (K&C) Initiative1 provided 50 m strip data for scientific investigation. By mid 2010, 50 m mosaics had been generated for several regions by JAXA, including Australia (2009 only), and made available for public release. Mosaics are also being generated by organisations associated with the K&C Initiative (e.g. NASA and the EU Joint Research Centre).

Within most regions globally, the mosaics generated have been relatively seamless (e.g. in eastern Asia and Japan). However, in other regions (e.g. Australia and Africa), non-systematic variability backscatter magnitude within and between strips has been observed. Recent research has linked this to spatial patterns in soil moisture, vegetation water content and rainfall at the time of image acquisition (Lucas et al., 2010a). The sensitivity of L-band SAR to the dielectric properties of soil and vegetation is well understood but the impact on large area compositing and mosaicing of SAR imagery has received little attention. However, the generation of large area radar mosaics that are spatially and temporally consistent is essential for land cover classification and the estimation of biophysical attributes. The geometric and radiometric integrity of the data also needs to be evaluated to facilitate routine uptake and use by government agencies responsible for environmental monitoring.

To address these issues, the research described focused on the use of individual ALOS PALSAR strip data, which were made available to the K&C science team and processed to generate annual ALOS PALSAR K&C mosaics from 2007 to 2009 for Queensland. Reference to Advanced Microwave Scanning Radiometer – EOS (AMSR-E) indicated that the banding in the mosaics was attributable to differences in the moisture content of the vegetation and soil. On this basis, mosaics were generated from K&C strips associated with drier conditions. The resulting mosaics for Queensland and examples of the impact of surface moisture are also presented.

Data and Methods

K&C strip data The ALOS PALSAR was launched on 24th January, 2006, and has since operated in a 46 day repeat orbit cycle. The PALSAR is a multi-polarisation L-band SAR with fine-beam single polarisation (FBS HH), fine-beam dual polarisation (FBD HH+HV), polarimetric (PLR) and ScanSAR imaging modes. The ALOS PALSAR has a systematic observation strategy implemented by JAXA that aims to provide global terrestrial coverage at medium spatial resolutions (10−50 m), at least twice per year since 2007 for the duration of the ALOS mission (Rosenqvist et al., 2007). This study used data from the FBD imaging mode, typically acquired between May and September each year with an off-nadir angle of 34.3°.

1 www.eorc.jaxa.jp/ALOS/en/kyoto/kyoto_index.htm

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The FBD data were received as part of the JAXA K&C Initiative. The data were provided as individual orbit strips, which were approximately 70 km across-track and covered at least the length of Queensland along-track (Figure 1). These data were multi-looked to give range and azimuth pixel spacing in ground range of approximately 37 m and 51 m, respectively, which is a factor of four less that the full spatial resolution FBD data. Figure 1 shows that up to three dates were provided for each year from 2007 to 2009, however some strips (e.g. 370) were, at the time of writing, missing data for some years.

150°E

150°E

140°E

140°E

10°S

10°S

20°S

20°S

30°S

30°S

390

388

386

384

382380

378

376

374

372370

368

366

364

362360

200 km

Number of strips

ALO

S P

ALS

AR

RS

P

359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390

2 4 6 8 10

200720082009

Figure 1. ALOS PALSAR K&C strip data (2007−2009) provided by JAXA for ALOS Reference System for Planning (RSP) 359 to 390.

The K&C strip data were delivered as 16-bit image data in slant range geometry. All processing was then undertaken using Gamma SAR processing software (Wegmüller et al., 1998; Wegmüller, 1999) and in-house software. The data were converted from digital number (DN) to values of σ0 (dB) by

( ) ( ) CFDNdB += 210

0 log10σ , (1)

where CF is the calibration factor (Table 1; Shimada et al., 2009). Note that subsequent processing were in linear units of 0σ , not dB.

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Table 1. Calibration factors used for operational processing of K&C strip data.

Polarisation Processing Date Calibration Factor HH Before 2009/01/09

2009/01/09 to date -83.2 dB -83.0 dB

HV Before 2009/01/09 2009/01/09 to date

-80.2 dB -83.0 dB

Masking of range power loss Prior to geocoding, an automated approach was required to correct slant range images affected by range power loss at near range, far range or both (Figure 2). This problem with K&C strip data is caused by antenna pattern reduction and has also been reported by other research (Rosenqvist and De Grandi, 2009). The extent of the power loss was not consistent in azimuth or range, within or between strips.

Figure 2. An example of range power loss in K&C strip data. The loss of power at far

range is shown in the HH slant range backscatter amplitude image (left). The horizontal red line is the location of the backscatter profile (right). The vertical red line shows the

cut-off point determined by the adaptive mask.

A simple adaptive method was therefore developed to mask these data from further processing. Each HH and HV strip was processed in 256 line blocks. For each block, the following process was applied:

1. All pixels were averaged in the azimuth direction.

2. Relative cumulative histograms were calculated separately for near and far range to mid swath.

3. The near and far range cut-off points were set to be where each relative cumulative histogram was 5%.

4. To avoid masking useful data at near or far range, tests were implemented to establish if there was a difference between the mean of range pixels up to the cut-off point and range pixels from the cut-off point to mid-swath. This involved:

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i. Calculation of the nearly-normal Z test statistic using the Wilcoxon rank-sum test.

ii. Calculation of the one-tailed probability (p) of obtaining a value of Z or greater.

iii. Masking to block, if the p was ≤0.025, then the block is masked up to the cut-off point (plus an optional offset) or the 400th pixel, whichever was first. Otherwise no pixels in the block were masked.

Geometric correction Geocoding of the strip data was undertaken within the Gamma Differential Interferometry and Geocoding (DIFF & GEO) modules by generating a look-up table to transform the SAR data from the slant-range/azimuth geometry to map projection geometry. The geocoding involved initial determination and then refinement of the geometric transformation and subsequent resampling of the strips from slant range SAR coordinates to map geometry. The initial determination of the geometric transformation was based on orbital state vectors and used a version of the Shuttle Radar Topography Mission (SRTM) derived 30 m Digital Elevation Model (DEM) that was released by Geoscience Australia (GA, 2009).

Refinement of the transformation was undertaken by automatically quantifying offsets between the actual SAR image and one simulated from the SRTM-derived DEM (transformed to radar geometry) through image chip cross-correlation. This refinement is generally successful over areas of substantial topographic complexity. However, as much of Queensland has very little topographic relief, particularly in the interior, there were often insufficient features for reliable cross-correlation. Therefore refinement of the geometric transformation was undertaken by image chip cross-correlation matching between the HV strip image and a Landsat Enhanced Thematic Mapper (ETM+) panchromatic mosaic registered to ground control points (Gill et al., 2010). This was undertaken as sufficient spatial contrasts in land cover were evident.

A polynomial was then fitted to the offsets determined though automated cross-correlation. The order of the polynomial depended on the length of the strip. For some coastal strips (e.g. RSP’s 359 and 360), a 1st or 2nd order polynomial was sufficient. For long strips that cover the entire length of Queensland (e.g. RSP 374), a polynomial up to 6th order was used. The strip data were resampled to UTM (WGS84) projection where they intersected zones 54, 55 and 56 using the polynomial refined look-up table.

Radiometric correction Following geocoding, simple corrections for local ground scattering area and local incidence angle were undertaken using the theory outlined by Castel et al. (2001). This is also the approach being taken in other K&C research (Thiel et al., 2009; Santoro et al., 2009) and the basic equations are presented here. The first step is to correct for variations in local ground scattering area (A). With the assumption of flat terrain, A is given by

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( )θsinsa

f

rrA

⋅= , (2)

and over sloped terrain is given by

( )ψcossa

s

rrA

⋅= , (3)

where ra and rs are the azimuth and slant range pixel spacing respectively, θ is the local incidence angle assuming flat terrain and ψ is the angle between the surface normal and image plane normal (See Ulander (1996; Figure 3) for a useful illustration of this geometry). The next step was to correct for variation in backscatter caused by topography. Using the scattering approximations of Castel et al. (2001), the main quantities that change with varying topography are local incidence angle and path length through the canopy. Using the same notation of Santoro et al. (2009), these effects of slope on local ground scattering area and angular variation in backscatter were corrected by

( )( )

n

i

r

s

f

A

A

=

θθσγ

coscos00 , (4)

where 0γ is the corrected σ0 in backscatter intensity, and cos(θi) and cos(θr) are the local and reference incidence angles, respectively. Previous studies have used the local incidence angle for flat terrain at mid-swath for individual images (e.g. Thiel et al., 2009). However, 39° was used as this was the mode local incidence angle for flat terrain at mid-swath for all strips. n is the parameter that encapsulates the effect of SAR configuration and crown optical depth on angular variation in backscatter. For bare surfaces, n is equal to one and decreases with increasing canopy optical depth. Since there was no a priori knowledge of canopy optical depth, n was set to one. All other parameters were provided with the strip metadata or derived from the 30 m SRTM DEM

Compositing and mosaicing

For each year and RSP, the average HH 0γ was calculated. The acquisition

date with the minimum averaged HH 0γ was selected for mosaicing. The HH polarisation was used because it is more sensitive to variation in soil moisture and effective vegetation water content, as shown later in this paper. Near range pixels were selected over far range pixels to preserve original 0γ values in the final mosaics. Problems with image quality at near and far range due to antenna pattern reduction were avoided by masking the power range loss. Spatial metadata are also created to accompany the final mosaics including the acquisition date (number of days since launch), and layover/shadow/water masks.

To provide independent indicators of surface moisture conditions for each RSP, Aqua Advanced Microwave Scanning Radiometer-EOS (AMSR-E; launched May 2002) daily effective vegetation water content and soil moisture estimates were sourced through the National Snow and Ice Data Center (NSIDC; Njoku,

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updated daily). The soil moisture estimates are near surface (in the top ~1 cm) and are in units of g cm-3, averaged over the retrieval footprint. The effective vegetation water content, which includes the effects of large scale surface roughness, is the amount of water (kg m-2) in the vertical column of vegetation, averaged over the retrieval footprint.

Ascending Aqua passes were used because comparison of the AMSR-E surfaces with SILO daily rainfall surfaces suggested greater sensitivity than descending passes to rainfall events and the subsequent retention of moisture. See Lucas et al. (2010a) for some comparisons with rainfall surfaces. The use of ascending passes is also consistent with other soil moisture research in Australian savanna environments (Renzullo et al., 2008). To balance reducing noise in the data with preserving short-term variability, a 5-day boxcar average filter was applied to the AMSR-E time-series prior to extraction. This procedure also ensured there was complete ‘daily’ spatial coverage for all of Queensland.

Example time series of SILO daily rainfall and AMSR-E soil and vegetation water content for 2007 are shown in Figure 3 with the timing of PALSAR overpasses. The PALSAR FBD overpasses are typically through the dry season although they still often intersect rainfall events. There is correspondence between the peaks of rainfall and effective vegetation moisture content and to a lesser extent soil moisture.

2007 (AMSR-E ascending passes)

Sur

face

soi

l moi

stur

e (g

cm

−3)

0.0

0.2

0.4

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Mixed Species Forest 145.18°E 16.81°S

0.0

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0.6

Eucalypt Woodland 144.53°E 24.34°S

0.0

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Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mulga Open Woodland 144.64°E 26.40°S

0

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0

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Effective vegetation w

ater content (kg m−2)

Figure 3. The range of environmental conditions in 2007 for three sites with contrasting

vegetation cover compared to the timing of ALOS PALSAR overpasses (dashed vertical lines). The AMSR-E surface soil moisture (blue line) and effective vegetation water content (green line) are shown. The timing and relative magnitude of rainfall

events (SILO daily rainfall; filled grey) is also shown.

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There is very little temporal variation in soil moisture over time, especially over the mixed species forest site. However soil moisture estimates are known to be inaccurate over even moderate vegetation cover (Bindlish et al., 2006). Although estimates of effective vegetation water content include the combined effect of large scale surface roughness and vegetation, temporal changes can be interpreted primarily as changes in water content since these are relatively constant over time at the spatial resolution of the product (Njoku and Chan, 2006).

Results and Discussion

Evaluation of geometric accuracy Figure 4 shows the distributions of range and azimuth root mean square errors (RMSE) for individual K&C strips following geocoding. The median RMSE in both range and azimuth is less than 0.25 pixels and the error is consistent between years, which is a strong result overall. It is important to note that this error assessment is dependent on the polynomial model fit and is therefore not independent. Geometric errors are generally higher in range than azimuth, and the outliers primarily correspond to strips dominated by ocean. This was because insufficient matching of features between the SAR and Landsat image data and a poor spatial distribution of tie-points resulted in spurious polynomial model fits.

Year

RM

SE

(50

m p

ixel

s)

0.2

0.4

0.6

0.8

2007 2008 2009

Azimuth offsets

2007 2008 2009

Range offsets

Figure 4. Distribution of strip registration RMSE in the range and azimuth directions for

each year of data available.

Queensland mosaics 2007−2009

The statewide mosaics produced for Queensland 2007−2009 are shown in Figure 5 along with coincident date mosaics of AMSR-E soil moisture and effective vegetation water content. Areas of water, layover and shadow have been masked.

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Figure 5. Annual mosaics of 0γ for Queensland 2007−2009 generated from strips acquired under relatively dry conditions. Soil moisture and effective vegetation moisture

content at the time of PALSAR image acquisition are also shown.

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There is residual banding in the 2008 and to a lesser extent the 2007 mosaic, which is particularly noticeable around the Carnarvon Gorge region in central Queensland. The 2009 L-band mosaics are relatively seamless. JAXA has released a 50 m mosaic of Australia for this same year.

Lucas et al. (2010a) showed a clear difference in 0γ between K&C strips acquired under minimum and maximum effective vegetation water content in 2007. The residual banding the 2007 and 2008 mosaics correspond to elevated soil moisture and effective water content in the AMSR-E surfaces shown in Figure 5, although to a lesser extent. For all PALSAR mosaic years, south-east Queensland and coastal regions exhibit elevated soil moisture. Much is this area supports moderate to dense vegetation cover, including tropical and subtropical forests, which would bias the soil moisture estimates towards higher values (Bindlish et al., 2006).

The 2009 0γ mosaic exhibits low effective vegetation moisture content throughout the state apart from some coastal areas and the Carnarvon Gorge region. This can be explained by large scale surface roughness because of topographic influences elevating the vegetation water content estimates (Njoku and Chan, 2006). This effect may be removed if the minimum effective vegetation moisture content for each pixel within the time series was subtracted from the estimate, allowing the remaining change to be interpreted solely as vegetation water content.

Impacts of surface moisture An example of the impact of surface moisture condition on a time series of the HH and HV polarised data is shown for a sample of pixels from RSP 374 in Figure 6. The magnitude of difference in 0γ between dates is greater for HH (up to 3 dB RMSE) than for HV (up to 2 dB RMSE) polarisation but the same bias is observed. The dates that have similarly low effective vegetation water content have a 1:1 relationship and correspond to within approximately 1 dB.

This effect is attributed to different scattering mechanisms associated with the HH and HV polarisations. L-band HH is mainly double bounce and ground backscatter and HV mainly volume (crown) scattering (Lucas et al., 2004). A large increase in soil moisture may be more evident in the HH than in the HV 0γ due to increased scattering from the wetter surface. This is primarily through direct ground returns and double bounce interactions between the trunks and ground. With increasing above-ground biomass, attenuation of microwaves is greater and volume scattering dominates, therefore the contribution to HH 0γ from these scattering mechanisms is reduced (Watanabe et al., 2006).

The L-band HH 0γ bias shown in Figure 6 for 2007/09/05 is also illustrated spatially in Figure 7. The areas of woody vegetation and pastures exhibit higher HH 0γ than within the corresponding 2007/10/21 acquisition date. An interesting observation is the appearance of rain “bands” across the 2007/09/05 image, which highlights that surface water following rainfall strongly affects the PALSAR backscatter.

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RSP 374 - 20070709 (1.11 kg m −2)

L-HH γ0 (dB)

L-H

H γ

0 (dB

)

-30

-25

-20

-15

-10

-5 1.67 kg m-2

RMSE = 3.01

20070905

1.19 kg m-2

RMSE = 1.13

20071021

-30

-25

-20

-15

-10

-5

-30 -25 -20 -15 -10 -5

2.25 kg m-2

RMSE = 2.57

20080907

-30 -25 -20 -15 -10 -5

1.28 kg m-2

RMSE = 1.10

20081023

RSP 374 - 20070709 (1.11 kg m −2)

L-HV γ0 (dB)

L-H

V γ

0 (dB

)

-30

-25

-20

-15

-10

-5 1.67 kg m-2

RMSE = 2.08

200709051.19 kg m-2

RMSE = 1.00

20071021

-30

-25

-20

-15

-10

-5

-30 -25 -20 -15 -10 -5

2.25 kg m-2

RMSE = 1.70

20080907

-30 -25 -20 -15 -10 -5

1.28 kg m-2

RMSE = 1.02

20081023

Figure 6. Consistency of L-HH (left) and L-HV (right) backscatter from 2007 to 2008 acquisition dates for RSP 374 when compared to the strip with minimum effective

vegetation water content.

Figure 7. ALOS PALSAR L-band HH image for a “wet” (left) and a “dry” (right)

acquisition date for RSP 374. The AMSR-E/Aqua L2B effective vegetation water content product for the corresponding dates are also shown.

2007/09/05 2007/10/21

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Conclusions and Future Research This paper has presented a simple approach to generating annual ALOS PALSAR backscatter mosaics for Queensland, Australia. These are the first large area PALSAR mosaics generated for multiple dates in Australia. These mosaics are now being used for mapping brigalow regrowth following clearing (Accad et al., 2010), above-ground biomass (Lucas et al., 2010b), mangrove extent and change (Lucas, 2010) and improved mapping of wooded extent (Kitchen et al., 2010). An advantage of the K&C data strip being multi-looked to approximately 50m spatial resolution is that speckle is minimised and classification algorithms may be applied without additional pre-processing.

The PALSAR K&C strips were geometrically registered to Landsat-7 ETM+ panchromatic imagery with median range and azimuth average RMSE of less than 0.25 pixels. This has ensured optimum integration with Landsat-derived foliage projective cover (Armston et al., 2009; Kitchen et al., 2010). However these figures are for the polynomial model fits and do not reflect known misregistration errors for strips dominated by ocean. Current work is attempting to address these problems and to generate independent geometric error statistics using similar methods developed for Landsat data (Gill et al., 2010). Future research on the ALOS PALSAR radiometric corrections will focus on accuracy assessment of the local incidence angle correction using pixel pairs in the path overlaps. Accounting for the scattering properties of the surface has the potential to improve the local incidence angle correction and, in turn, estimates of 0γ . It may be possible that Landsat-derived estimates of foliage projective cover (Armston et al., 2009) could be used, as a proxy for canopy optical depth to adjust n in Equation 4 (e.g. Clewley et al., 2010).

The evaluation and use of AMSR-E data products in this study was qualitative, and further exploration of soil and vegetation moisture dynamics and their impact on ALOS PALSAR time series using AMSR-E, Soil Moisture and Ocean Salinity (SMOS) and ground measurement time series data is required. This is particularly important for change detection and above-ground biomass estimation (Lucas et al., 2010b). AMSR-E and SILO data are freely available and are a useful tool for selection of appropriate ALOS PALSAR image dates. Although AMSR-E products, as with PALSAR, are derived from microwave remote sensing, their high temporal resolution coverage permits wall-to-wall assessment of relative changes in soil moisture and vegetation water content over time. Rainfall records can be sparse due to the distribution of the meteorological stations (can be > 100 km apart). Therefore the only available data may not be representative of the rainfall at the desired location. A combination of soil moisture, effective vegetation water content and rainfall may be more appropriate for selecting ALOS PALSAR data as they may better reflect the moisture content of surfaces, particularly those that are densely vegetated.

This work may be of benefit to national efforts for carbon tracking and integration of imaging radar data with Landsat time series data. The application of the image processing chain described in this paper is currently being extended to K&C FBD strip data for the Northern Territory and New South

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Wales. These methods are being applied to all the full spatial resolution JERS-1 SAR, and ALOS PALSAR FBD, FBS and PLR data in the Remote Sensing Centre image archive. When available, incorporation of a higher spatial resolution DEM (e.g. Tandem-X) into the geometric and radiometric corrections will yield improvements to the geometric and radiometric quality of ALOS PALSAR mosaics.

Acknowledgements This work has been undertaken within the framework of the JAXA Kyoto & Carbon Initiative. ALOS PALSAR data have been provided by JAXA EORC. All PALSAR images and derived products illustrated in this paper are copyright to the ALOS K&C © JAXA/METI. Frank De Grandi and Maurizio Santoro are thanked for advising the authors on problems encountered using the K&C strip data. Particular thanks is extended to the JAXA, for the provision of these data

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