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Interpretation Guide
for ALOS PALSAR / ALOS-2 PALSAR-2
global 25 m mosaic data
Version 1.1
1 October, 2016
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Table of Contents
1 Briefly about Synthetic Aperture Radar ......................................................................................... 3 1.1 The radar wavelength .................................................................................................................... 3 1.2 Polarisation ....................................................................................................................................... 3 1.3 Radar backscatter ........................................................................................................................... 4 1.3.1 Sigma-‐naught ................................................................................................................................................... 4 1.3.2 Gamma-‐naught ............................................................................................................................................... 4
2 ALOS PALSAR and ALOS-‐2 PALSAR-‐2 mosaics .............................................................................. 5 2.1 Original JAXA mosaic data ............................................................................................................ 5 2.2 Kenya Data Cube mosaic data example ................................................................................... 5 2.3 Data Layer description .................................................................................................................. 6 2.3.1 Radar backscatter .......................................................................................................................................... 6 2.3.2 RGB false-‐colour composite ...................................................................................................................... 7 2.3.3 Observation date ............................................................................................................................................ 8 2.3.4 Mask image ....................................................................................................................................................... 9 2.3.5 Google Earth visualisation ...................................................................................................................... 10
3 PALSAR (L-‐band SAR) quick interpretation guide .................................................................... 11 3.1 Caveats .............................................................................................................................................. 11 3.2 Examples from Kenya .................................................................................................................. 11 Forest ........................................................................................................................................................................... 11 Sparse Forest ............................................................................................................................................................ 12 Agriculture ................................................................................................................................................................. 12 Irrigated rice ............................................................................................................................................................. 13 Bare soil/sparse vegetation ............................................................................................................................... 13 Rocky terrain ............................................................................................................................................................ 14 Water ............................................................................................................................................................................ 14
Acknowledgements ..................................................................................................................................... 15
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1 Briefly about Synthetic Aperture Radar
1.1 The radar wavelength
Synthetic Aperture Radar, SAR, is an active system operating in the microwave domain of the electromagnetic spectrum. Microwaves are not visible to the human eye and provide a very different view of the world from what we are used to. While optical remote sensing sensors function similar to the human eye – they are passive sensors which record reflected sunlight – a radar sensor operates more like a flash camera in a dark room. The radar emits a light pulse and records the part of the pulse that is reflected, or scattered, back to the sensor (hence the term backscatter). Unlike sunlight which is non-polarised and comprises a large range of different wavelengths, the radar is a laser which operates within narrow and well-defined wavelength bands, and at a specific polarisation. The most common present and near-future spaceborne radar systems operate with the following bands:
• P-band: ~23.5 cm (BIOMASS) • L-band: ~23.5 cm (ALOS PALSAR, PALSAR-2, SAOCOM-1A/B) • S-band: ~10 cm (NovaSAR) • C-band: ~5.6 cm (Sentinel-1, Radarsat) • X-band: ~3.1 cm (TerraSAR-X, TanDEM-X, COSMO-SkyMed)
The choice of wavelength band strongly affects what type (size) of objects the radar is sensitive to. As a rule of thumb, the radar “can see” objects of about the same spatial magnitude as the radar wavelength, and larger. Objects significantly smaller than the radar wavelength on the other hand, become transparent to the radar. The smaller the object, the less influence on the backscatter. Longer wavelength radar signals (L-band) consequently penetrate through the forest canopy (the small leaves are invisible) and interact with the larger structures such as the trunks and larger branches of trees – and hence display a positive, although limited, correlation with above-ground biomass. Systems operating at shorter wavelengths (C-band, X-band) on the other hand, are more sensitive to sparse and low vegetation.
1.2 Polarisation
The radar polarisation is another parameter affecting the strength of the backscatter. Current spaceborne radar systems operate with linear polarisation, where the radar signals are transmitted and received at horizontal (H) and/or vertical (V) polarisation.
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The polarisation of SAR imagery are commonly denoted by two letters, the first indicating the transmitted polarisation and the latter the received polarisation.
• HH: Transmission of horizontal wave; Reception of horizontal component • HV: Horizontal transmission; Vertical reception • VH: Vertical transmission; Horizontal reception • VV: Vertical transmission; Vertical reception • Quad-pol (QP): H and V transmission; H and V reception (HH+HV+VH+VV)
1.3 Radar backscatter
1.3.1 Sigma-naught
Radar image brightness is normally expressed in σo (sigma-naught) which is the radar backscatter per unit area. The unit of σo is [m2/m2], expressed in decibel (dB). The standard formula to calculate σo is σo = 10 * log10(DN2) + K where DN is the image pixel digital number measured in the SAR image (or more accurately, the average pixel value over a group of pixels). K is a calibration factor which varies depending on the SAR sensor and processor system used. For ALOS/PALSAR and ALOS-2/PALSAR-2 data provided by JAXA, the calibration factor is -83.0 db.
1.3.2 Gamma-naught
Even for homogeneous targets, σo varies slightly depending on the angle between the ground and the sensor – the incidence angle – being higher (brighter) in the near-range part of the image (closest to the satellite) and lower (darker) in the far-range of the image, further away from the satellite. By normalising σo with respect to the incidence angle we can remove the range-dependency to obtain γo (gamma-naught): γo = σo/cosϕ where ϕ is the incidence angle.
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2 ALOS PALSAR and ALOS-2 PALSAR-2 mosaics
2.1 Original JAXA mosaic data
The ALOS/PALSAR and ALOS-2/PALSAR-2 global mosaic datasets are created by the Japan Aerospace Exploration Agency (JAXA) by assembling adjacent satellite observation paths over extensive regions to form a seamless global mosaic. Corrections of geometric distortions specific to SAR (ortho-rectification) as well as topographic effects on image intensity (slope correction) have been applied using the SRTM-90 Digital Elevation Model. Backscatter is corrected for incidence angle and thus given as gamma naught (γo). The mosaics are given in geographical (lat/long) coordinates, using the GRS80 ellipsoid, and provided in rectangular tiles, 1° by 1° in latitude and longitude direction. The pixel spacing is 0.8 arc seconds, corresponding to 25 m at the Equator. ALOS/PALSAR mosaics have been generated for four years: 2007, 2008, 2009 and 2010 while one ALOS-2/PALSAR-2 mosaic, from the year 2015, has been generated to date. The global mosaic data can be downloaded free of charge from JAXA at: http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm
2.2 Kenya Data Cube mosaic data example
The mosaic data provided in the Kenya Data Cube are derived from the original JAXA mosaics, but with some additional products (RGB images) and formatting (GeoTIF) to improve utilisation for users not familiar with radar remote sensing. The ALOS PALSAR and ALOS-2/PALSAR-2 data sets provided with the Kenya Data Cube comprises six separate images/layers:
• Radar backscatter (γo) – HH polarisation • Radar backscatter (γo) – HV polarisation • RGB false-colour composite (Data Cube only) • Observation date image • Mask image • RGB image in KML format for visual display in Google Earth (Data Cube only)
All data layers have the same (0.8 arc sec) pixel spacing, which for most practical purposes can be considered equal to 25 m in countries along the Equator.
File name convention: AAABBBB_YY_[layer indicator], where AAA: latitude of tile upper left corner [e.g. S01] BBBB: longitude of tile upper left corner [e.g. E041] YY: two last digits of mosaic year [e.g. 09 for 2009] [layer indicator]: “sl_HH”, “sl_HV”, “MOS-composite”; “date”, “mask”
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2.3 Data Layer description
2.3.1 Radar backscatter
Figure 1. Radar backscatter – HH polarisation (left); HV polarisation (right)
Content: Calibrated radar backscatter at HH polarisation and HV polarisation.
Data type: 16 bits integer
Format: GEOTIF
Size: 4500 pixels x 4500 lines (40.5 MB)
File names: AAABBBB_YY_[sl_HH]
AAABBBB_YY_[sl_HV]
Radar backscatter is provided as two separate images: one for the Horizontal-Horizontal (HH) polarisation, and one for Horizontal-Vertical (HV) ditto. The backscatter images are identical to those provided by JAXA, with the exception that the Kenya Data Cube images are provided in GEOTIF format. The backscatter images constitute the data files on which all analysis should be undertaken. SAR backscatter data have a large radiometric dynamic range with DN values ranging up to several thousand and are therefore provided as 16 bits unsigned integer data (accommodating DN values 0 ~ 63000).
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As mentioned above, the linear backscatter values in the HH and HV images can be converted from image digital numbers (DN) to γo, and expressed in decibel (dB), by the formula:
γo = 10 * log10(DN2) -83.0 [dB]
γo can vary from around -35 dB (DN ~500) in very low backscatter areas, up to about -5 dB (DN ~8,000) for extremely high backscatter. γo is generally negative and very seldom reaches 0 dB.
2.3.2 RGB false-colour composite
Figure 2. 3-channel false-colour composite
Content: 3-channel RGB false-colour composite image
R: HH γo backscatter
G: HV γo backscatter
B: HH/HV ratio
Data type: 8 bits (DN 0~255)
Format: GEOTIF
Size: 4500 pixels x 4500 lines per channel (60.8 MB)
File name: AAABBBB_YY_[MOS]
The RGB image is false-colour composite generated for visualisation purposes only. It is not included amongst the original data lays provided by JAXA.
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The red and green image channels contain the HH and HV backscatter images respectively, while the third (blue) channel is synthesized from the former two. The HH/HV backscatter ratio is commonly used for the blue channel as it results in a composite image in which vegetated areas appear green. Note that since the HH and HV backscatter data in RGB image have been scaled to 8 bits, the formula to calculate γo in 2.3.1 above is not applicable.
2.3.3 Observation date
Figure 3. Observation date image
Content: Image showing the observation date
Data type: 16 bits integer
Format: GEOTIF
Size: 4500 pixels x 4500 lines (40.5 MB)
File name: AAABBBB_YY_[date]
As the mosaic tiles are composed of SAR data from multiple satellite paths, an observation date image is required to provide information about the date of acquisition for each pixel in the mosaic tile. The (16 bits) pixel digital numbers in the observation date image correspond to the number of days after the launches of ALOS and ALOS-2 satellites. ALOS was launched on Jan. 24, 2006 (used for the 2007, 2008, 2009 and 2010 mosaics), and ALOS-2 on May 24, 2014 (used for the 2015 mosaic).
DN: 498 DN: 470 DN: 409
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The Digital Numbers 409, 470 and 498 in the ALOS-2 mosaic image shown in Figure 3 above thus correspond to: 24/05/2014 + 409 = 07/07/2015 24/05/2014 + 498 = 06/09/2015 24/05/2014 + 498 = 04/10/2015
2.3.4 Mask image
Figure 4. Mask image and Look-up table of DN values
Content: Mask image
Data type: 8 bits
Format: GEOTIF
Size: 4500 pixels x 4500 lines (20.3 MB)
File name: AAABBBB_YY_[mask]
The mask image provides information about pixels affected by radar shadowing and lay over, as well as the location of water bodies and no-data areas. It is identical to the mask image provided by JAXA, but in GEOTIF format.
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2.3.5 Google Earth visualisation
Figure 5. KML
Content: RGB false-colour composite.
Format: KMZ
Size: 3 ~ 12 MB
File name: AAABBBB_YY_[MOS].KMZ
An RGB false-colour composite image is provided for visual display in Google Earth. It is not included amongst the original data lays provided by JAXA.
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3 PALSAR (L-band SAR) quick interpretation guide
3.1 Caveats
Below follow examples of how some typical land cover types found in the Kenyan landscape appear in L-band SAR data, or more specifically, in ALOS PALSAR/ALOS-2 PALSAR-2 mosaic data at HH and HV polarisations. It should be noted that the backscatter images in the figures have been scaled from the original 16 bits data. While the HV backscatter typically is 5 – 10 dB lower (darker) than that of HH, the two channels can look similar in the examples as different scaling factors have been used to maximise the visual impression. The backscatter values shown below the images have however been measured in the original (16 bits) images – averaged over >1000 pixels to reduce the influence of speckle – and should hus indicate “true” estimates of the backscatter. The backscatter standard deviation is given in brackets when applicable. Also an RGB colour composite image is shown for reference, although it is important to keep in mind that there are neither any standardised rules on how represent microwave data visually, nor how to compose a 3-channel colour image from only two input channels. Yet, the [HH, HV, HH/HV] combination used here for [R, G, B] is frequently utilised by remote sensing users.
3.2 Examples from Kenya
Forest
HH: -7.7 (1.0) dB
HV: -12.4 (0.9) dB
Dense homogeneous forest (Mount Kenya National Park) results in high and uniform backscatter at both polarisations. HV backscatter is closely correlated with forest structure and above-ground biomass, with HV reaching higher σ0 values than for any other land cover. The darker patch in the upper right corner indicates a deforested area.
L-HH L-HV R: HH G: HV B: HH/HV
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Sparse Forest
HH: -10.5 (N/A) dB
HV: -17.0 (N/A) dB
The high-altitude forest on Mount Kenya shown above, displays medium-high HH and HV backscatter. The drop in backscatter for this lower/sparser forest type, compared to that for the denser lower altitude forest in the previous example, is directly related to parameters such as tree height, structure and stem density, and illustrates the strong relationship between L-band backscatter and forest above-ground biomass.
Agriculture
HH: -8.2 (1.9) dB
HV: -17.5 (2.1) dB
Mixed agriculture area (Kirinyaga county). The low vegetation typical for agricultural crops is largely transparent at the L-band wavelength, signified by low HV backscatter. The higher backscatter observed in the HH channel is not caused by the crop vegetation, but by direct scattering on ploughed fields and rough open soil. Agricultural areas typically have a purple appearance in the RGB composite. The agricultural landscape can also be identified by geometrical patterns and textural features in the image, such as fields, roads and waterways. Small patches of trees appear green due to higher HV backscatter.
HH HV R: HH G: HV B: HH/HV
HH HV R: HH G: HV B: HH/HV
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Irrigated rice
HH: -9.6 (N/A) dB
HV: -21.1 (N/A) dB
Irrigated rice is the only agricultural crop which is clearly visible in L-band SAR data. Reflections between the vertical rice plants and the water surface of the flooded fields can result in a strong HH backscatter, which increases throughout the vegetation season from planting to harvest. While it needs to be confirmed, it is likely that the bright areas in visible in the image above over Wanguru, Kirinyaga county, are rice paddies.
Bare soil/sparse vegetation
HH: -27.7 (5.3) dB
HV: -35.8 (3.0) dB
North Horr, Marsabit county. Arid landscapes with vast expanses of sand or grass result in extremely low HH backscatter, as the smooth surfaces in these type of dry savannah environments produce no return. The HV backscatter is also extremely low, and close to the noise floor of the radar. The slightly brighter patches visible in the HV image are caused by low riparian vegetation along the river channels, and by scattered bushes and scrubs.
HH HV HH/HV
HH HV R: HH G: HV B: HH/HV
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Rocky terrain
HH: -8.9 (1.1) dB
HV: -21.7 (1.6) dB
Kenya-Ethiopia border, Marsabit county. Arid landscape with rock outcrops and exposed bedrock. In contrast to the smooth surfaces in the previous example, a rocky terrain results in strong surface reflections and high HH backscatter. Whether the moderate HV returns are caused by the uneven rock surfaces and/or low vegetation needs to be confirmed.
Water
HH: -18.7 (1.1) dB
HV: -35.8 (1.3) dB
Open water surfaces appear pitch black in HV imagery and result in backscatter levels close to the sensor noise floor. In the HH channel, a smooth water surface similarly results in no scattering back to the sensor. Waves on the water however, which may be present on Lake Turkana in the image above, can result in a slight HH backscatter increase. Water appears (appropriately) blue in the RGB composite.
HH HV R: HH G: HV B: HH/HV
HH HV R: HH G: HV B: HH/HV
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Acknowledgements
The development of this Interpretation Guide was funded
by the CEOS Systems Engineering Office (SEO)
and undertaken by Ake Rosenqvist (soloEO).
Comments and suggestions welcome to
